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6b11f9528e1f2f7ec105c559fc497c81451a9f15 | # Dataset Card for "fingpt_chatglm2_sentiment_instruction_lora_ft_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rodrfons/fingpt_chatglm2_sentiment_instruction_lora_ft_dataset | [
"region:us"
]
| 2023-11-18T16:36:19+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18540941.869938433, "num_examples": 76772}], "download_size": 6417302, "dataset_size": 18540941.869938433}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-18T16:36:22+00:00 | []
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#region-us
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|
548dceef0d713daf826e25fb805b7a6edee86e2a |
# Dataset Card for Guacamol
Dataset from the [Guacamol](https://github.com/BenevolentAI/guacamol) benchmark ([paper](https://arxiv.org/abs/1811.09621)).
Dataset contains two columns, SMILE and SELFIE. Splits are identical to original splits, however, any SMILE that could not be converted to a SELFIE was dropped. Likewise, any SELFIE in the val/test splits that contained a token not found in the train split was dropped.
Can be used with [this tokenizer](https://huggingface.co/haydn-jones/GuacamolSELFIETokenizer). | haydn-jones/Guacamol | [
"arxiv:1811.09621",
"region:us"
]
| 2023-11-18T17:19:38+00:00 | {"dataset_info": {"features": [{"name": "SMILE", "dtype": "string"}, {"name": "SELFIE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 351931924.06659317, "num_examples": 1273077}, {"name": "val", "num_bytes": 21949894.491152223, "num_examples": 79564}, {"name": "test", "num_bytes": 65951655.37470361, "num_examples": 238694}], "download_size": 148629975, "dataset_size": 439833473.932449}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-18T20:39:16+00:00 | [
"1811.09621"
]
| []
| TAGS
#arxiv-1811.09621 #region-us
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# Dataset Card for Guacamol
Dataset from the Guacamol benchmark (paper).
Dataset contains two columns, SMILE and SELFIE. Splits are identical to original splits, however, any SMILE that could not be converted to a SELFIE was dropped. Likewise, any SELFIE in the val/test splits that contained a token not found in the train split was dropped.
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|
b30ae6296b4efdb12469272fcf68c06f8f00c19c | # Dataset Card for "LAMINI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HossainRabby/LAMINI | [
"region:us"
]
| 2023-11-18T17:24:34+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 2150284.5, "num_examples": 1260}, {"name": "test", "num_bytes": 238920.5, "num_examples": 140}], "download_size": 698665, "dataset_size": 2389205.0}} | 2023-11-18T17:25:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "LAMINI"
More Information needed | [
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"# Dataset Card for \"LAMINI\"\n\nMore Information needed"
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12
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|
c803d8e04327e5f86be5cede09200dc75b4166f2 |
# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/Python-Code-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ajibawa-2023/Python-Code-13B](https://huggingface.co/ajibawa-2023/Python-Code-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T17:47:08.897776](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B_public/blob/main/results_2023-11-18T17-47-08.897776.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
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"mc1_stderr": 0.016203316673559696,
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"mc2_stderr": 0.015905372852037223,
"em": 0.02149748322147651,
"em_stderr": 0.001485300865621995,
"f1": 0.08503041107382545,
"f1_stderr": 0.0019611908757143598
},
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"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.038899512528272166
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.031885780176863984,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.031885780176863984
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3108935128518972,
"mc1_stderr": 0.016203316673559696,
"mc2": 0.42826268252930766,
"mc2_stderr": 0.015905372852037223
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
},
"harness|drop|3": {
"em": 0.02149748322147651,
"em_stderr": 0.001485300865621995,
"f1": 0.08503041107382545,
"f1_stderr": 0.0019611908757143598
},
"harness|gsm8k|5": {
"acc": 0.09552691432903715,
"acc_stderr": 0.008096605771155745
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B | [
"region:us"
]
| 2023-11-18T17:50:14+00:00 | {"pretty_name": "Evaluation run of ajibawa-2023/Python-Code-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ajibawa-2023/Python-Code-13B](https://huggingface.co/ajibawa-2023/Python-Code-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T17:47:08.897776](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Python-Code-13B_public/blob/main/results_2023-11-18T17-47-08.897776.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5448646670706938,\n \"acc_stderr\": 0.03364449787116653,\n \"acc_norm\": 0.553017962738193,\n \"acc_norm_stderr\": 0.03441995141099888,\n \"mc1\": 0.3108935128518972,\n \"mc1_stderr\": 0.016203316673559696,\n \"mc2\": 0.42826268252930766,\n \"mc2_stderr\": 0.015905372852037223,\n \"em\": 0.02149748322147651,\n \"em_stderr\": 0.001485300865621995,\n \"f1\": 0.08503041107382545,\n \"f1_stderr\": 0.0019611908757143598\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5469283276450512,\n \"acc_stderr\": 0.014546892052005628,\n \"acc_norm\": 0.5878839590443686,\n \"acc_norm_stderr\": 0.014383915302225405\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6303525194184425,\n \"acc_stderr\": 0.00481722729224028,\n \"acc_norm\": 0.8165704043019318,\n \"acc_norm_stderr\": 0.0038622736265045477\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252605,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252605\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480863,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480863\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5131578947368421,\n \"acc_stderr\": 0.04067533136309173,\n \"acc_norm\": 0.5131578947368421,\n \"acc_norm_stderr\": 0.04067533136309173\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791197,\n \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791197\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5833333333333334,\n \"acc_stderr\": 0.04122728707651282,\n \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.04122728707651282\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5260115606936416,\n \"acc_stderr\": 0.03807301726504513,\n \"acc_norm\": 0.5260115606936416,\n \"acc_norm_stderr\": 0.03807301726504513\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.425531914893617,\n \"acc_stderr\": 0.03232146916224468,\n \"acc_norm\": 0.425531914893617,\n \"acc_norm_stderr\": 0.03232146916224468\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.30701754385964913,\n \"acc_stderr\": 0.04339138322579861,\n \"acc_norm\": 0.30701754385964913,\n \"acc_norm_stderr\": 0.04339138322579861\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4689655172413793,\n \"acc_stderr\": 0.04158632762097828,\n \"acc_norm\": 0.4689655172413793,\n \"acc_norm_stderr\": 0.04158632762097828\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3253968253968254,\n \"acc_stderr\": 0.024130158299762613,\n \"acc_norm\": 0.3253968253968254,\n \"acc_norm_stderr\": 0.024130158299762613\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n \"acc_stderr\": 0.04163453031302859,\n \"acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.04163453031302859\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6290322580645161,\n \"acc_stderr\": 0.027480541887953593,\n \"acc_norm\": 0.6290322580645161,\n \"acc_norm_stderr\": 0.027480541887953593\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.42857142857142855,\n \"acc_stderr\": 0.03481904844438804,\n \"acc_norm\": 0.42857142857142855,\n \"acc_norm_stderr\": 0.03481904844438804\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6484848484848484,\n \"acc_stderr\": 0.037282069986826503,\n \"acc_norm\": 0.6484848484848484,\n \"acc_norm_stderr\": 0.037282069986826503\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.0331847733384533,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.0331847733384533\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8031088082901554,\n \"acc_stderr\": 0.028697873971860677,\n \"acc_norm\": 0.8031088082901554,\n \"acc_norm_stderr\": 0.028697873971860677\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5102564102564102,\n \"acc_stderr\": 0.025345672221942374,\n \"acc_norm\": 0.5102564102564102,\n \"acc_norm_stderr\": 0.025345672221942374\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871923,\n \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871923\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.542016806722689,\n \"acc_stderr\": 0.032363611119519416,\n \"acc_norm\": 0.542016806722689,\n \"acc_norm_stderr\": 0.032363611119519416\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7321100917431193,\n \"acc_stderr\": 0.018987462257978652,\n \"acc_norm\": 0.7321100917431193,\n \"acc_norm_stderr\": 0.018987462257978652\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.03350991604696042,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.03350991604696042\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598014,\n \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598014\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6547085201793722,\n \"acc_stderr\": 0.03191100192835795,\n \"acc_norm\": 0.6547085201793722,\n \"acc_norm_stderr\": 0.03191100192835795\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6259541984732825,\n \"acc_stderr\": 0.042438692422305246,\n \"acc_norm\": 0.6259541984732825,\n \"acc_norm_stderr\": 0.042438692422305246\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.71900826446281,\n \"acc_stderr\": 0.041032038305145124,\n \"acc_norm\": 0.71900826446281,\n \"acc_norm_stderr\": 0.041032038305145124\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.04453197507374983,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.04453197507374983\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.656441717791411,\n \"acc_stderr\": 0.037311335196738925,\n \"acc_norm\": 0.656441717791411,\n \"acc_norm_stderr\": 0.037311335196738925\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690878,\n \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690878\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7948717948717948,\n \"acc_stderr\": 0.026453508054040332,\n \"acc_norm\": 0.7948717948717948,\n \"acc_norm_stderr\": 0.026453508054040332\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7458492975734355,\n \"acc_stderr\": 0.015569254692045757,\n \"acc_norm\": 0.7458492975734355,\n \"acc_norm_stderr\": 0.015569254692045757\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6242774566473989,\n \"acc_stderr\": 0.02607431485165708,\n \"acc_norm\": 0.6242774566473989,\n \"acc_norm_stderr\": 0.02607431485165708\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.376536312849162,\n \"acc_stderr\": 0.016204672385106606,\n \"acc_norm\": 0.376536312849162,\n \"acc_norm_stderr\": 0.016204672385106606\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6339869281045751,\n \"acc_stderr\": 0.027582811415159607,\n \"acc_norm\": 0.6339869281045751,\n \"acc_norm_stderr\": 0.027582811415159607\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6237942122186495,\n \"acc_stderr\": 0.02751392568354943,\n \"acc_norm\": 0.6237942122186495,\n \"acc_norm_stderr\": 0.02751392568354943\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6049382716049383,\n \"acc_stderr\": 0.027201117666925654,\n \"acc_norm\": 0.6049382716049383,\n \"acc_norm_stderr\": 0.027201117666925654\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3900709219858156,\n \"acc_stderr\": 0.02909767559946393,\n \"acc_norm\": 0.3900709219858156,\n \"acc_norm_stderr\": 0.02909767559946393\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41851368970013036,\n \"acc_stderr\": 0.012599505608336456,\n \"acc_norm\": 0.41851368970013036,\n \"acc_norm_stderr\": 0.012599505608336456\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5257352941176471,\n \"acc_stderr\": 0.030332578094555033,\n \"acc_norm\": 0.5257352941176471,\n \"acc_norm_stderr\": 0.030332578094555033\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5718954248366013,\n \"acc_stderr\": 0.020017629214213097,\n \"acc_norm\": 0.5718954248366013,\n \"acc_norm_stderr\": 0.020017629214213097\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6326530612244898,\n \"acc_stderr\": 0.030862144921087555,\n \"acc_norm\": 0.6326530612244898,\n \"acc_norm_stderr\": 0.030862144921087555\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7611940298507462,\n \"acc_stderr\": 0.030147775935409217,\n \"acc_norm\": 0.7611940298507462,\n \"acc_norm_stderr\": 0.030147775935409217\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n \"acc_stderr\": 0.038899512528272166,\n \"acc_norm\": 0.4819277108433735,\n \"acc_norm_stderr\": 0.038899512528272166\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.031885780176863984,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.031885780176863984\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3108935128518972,\n \"mc1_stderr\": 0.016203316673559696,\n \"mc2\": 0.42826268252930766,\n \"mc2_stderr\": 0.015905372852037223\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n },\n \"harness|drop|3\": {\n \"em\": 0.02149748322147651,\n \"em_stderr\": 0.001485300865621995,\n \"f1\": 0.08503041107382545,\n \"f1_stderr\": 0.0019611908757143598\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.09552691432903715,\n \"acc_stderr\": 0.008096605771155745\n }\n}\n```", "repo_url": "https://huggingface.co/ajibawa-2023/Python-Code-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_18T17_47_08.897776", "path": ["**/details_harness|arc:challenge|25_2023-11-18T17-47-08.897776.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-18T17-47-08.897776.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_18T17_47_08.897776", "path": ["**/details_harness|drop|3_2023-11-18T17-47-08.897776.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-18T17-47-08.897776.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_18T17_47_08.897776", "path": ["**/details_harness|gsm8k|5_2023-11-18T17-47-08.897776.parquet"]}, 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ajibawa-2023/Python-Code-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T17:47:08.897776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Python-Code-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T17:47:08.897776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-13B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Python-Code-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T17:47:08.897776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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]
| [
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ajibawa-2023/Python-Code-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Python-Code-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T17:47:08.897776(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
5544a62a82b2ecd36021babc99147767d1672f06 |
# Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Jordan-13B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/Uncensored-Jordan-13B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ajibawa-2023/Uncensored-Jordan-13B](https://huggingface.co/ajibawa-2023/Uncensored-Jordan-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-13B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T18:01:22.350849](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-13B_public/blob/main/results_2023-11-18T18-01-22.350849.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5551346436733366,
"acc_stderr": 0.033773935379363566,
"acc_norm": 0.5623156588862028,
"acc_norm_stderr": 0.03452935511879212,
"mc1": 0.35006119951040393,
"mc1_stderr": 0.01669794942015103,
"mc2": 0.5051246541228124,
"mc2_stderr": 0.015683474268697605,
"em": 0.10371224832214765,
"em_stderr": 0.003122327158910168,
"f1": 0.1647325922818787,
"f1_stderr": 0.003269141000174996
},
"harness|arc:challenge|25": {
"acc": 0.5401023890784983,
"acc_stderr": 0.014564318856924848,
"acc_norm": 0.5742320819112628,
"acc_norm_stderr": 0.01444946427886881
},
"harness|hellaswag|10": {
"acc": 0.6352320254929297,
"acc_stderr": 0.004803812631994952,
"acc_norm": 0.8270264887472615,
"acc_norm_stderr": 0.0037745138826159514
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4740740740740741,
"acc_stderr": 0.04313531696750574,
"acc_norm": 0.4740740740740741,
"acc_norm_stderr": 0.04313531696750574
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5657894736842105,
"acc_stderr": 0.04033565667848319,
"acc_norm": 0.5657894736842105,
"acc_norm_stderr": 0.04033565667848319
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.03005258057955785,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.03005258057955785
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.041227287076512825,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.041227287076512825
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5317919075144508,
"acc_stderr": 0.03804749744364764,
"acc_norm": 0.5317919075144508,
"acc_norm_stderr": 0.03804749744364764
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04690650298201942,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04690650298201942
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4851063829787234,
"acc_stderr": 0.032671518489247764,
"acc_norm": 0.4851063829787234,
"acc_norm_stderr": 0.032671518489247764
},
"harness|hendrycksTest-econometrics|5": {
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"acc_stderr": 0.03892431106518754,
"acc_norm": 0.21929824561403508,
"acc_norm_stderr": 0.03892431106518754
},
"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.31746031746031744,
"acc_norm_stderr": 0.023973861998992065
},
"harness|hendrycksTest-formal_logic|5": {
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},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.37,
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},
"harness|hendrycksTest-high_school_biology|5": {
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.03663974994391244
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"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm_stderr": 0.032894773300986155
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_stderr": 0.028697873971860688,
"acc_norm": 0.8031088082901554,
"acc_norm_stderr": 0.028697873971860688
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4897435897435897,
"acc_stderr": 0.025345672221942374,
"acc_norm": 0.4897435897435897,
"acc_norm_stderr": 0.025345672221942374
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_stderr": 0.02803792996911499,
"acc_norm": 0.3037037037037037,
"acc_norm_stderr": 0.02803792996911499
},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.5588235294117647,
"acc_norm_stderr": 0.032252942323996406
},
"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.304635761589404,
"acc_norm_stderr": 0.037579499229433426
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_stderr": 0.01881918203485007,
"acc_norm": 0.7394495412844037,
"acc_norm_stderr": 0.01881918203485007
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.03293377139415191
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_norm": 0.7794117647058824,
"acc_norm_stderr": 0.02910225438967408
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_stderr": 0.028900721906293426,
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"acc_norm_stderr": 0.028900721906293426
},
"harness|hendrycksTest-human_aging|5": {
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"acc_norm": 0.6681614349775785,
"acc_norm_stderr": 0.031602951437766785
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6259541984732825,
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"acc_norm_stderr": 0.042438692422305246
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.04065578140908705,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.04065578140908705
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6388888888888888,
"acc_stderr": 0.04643454608906275,
"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.04643454608906275
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.6687116564417178,
"acc_stderr": 0.03697983910025588,
"acc_norm": 0.6687116564417178,
"acc_norm_stderr": 0.03697983910025588
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.375,
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"acc_norm": 0.375,
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},
"harness|hendrycksTest-management|5": {
"acc": 0.6699029126213593,
"acc_stderr": 0.0465614711001235,
"acc_norm": 0.6699029126213593,
"acc_norm_stderr": 0.0465614711001235
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8376068376068376,
"acc_stderr": 0.02416161812798774,
"acc_norm": 0.8376068376068376,
"acc_norm_stderr": 0.02416161812798774
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.6,
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"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.7573435504469987,
"acc_norm_stderr": 0.01532988894089986
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.630057803468208,
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"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.02599247202930639
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.41899441340782123,
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"acc_norm": 0.41899441340782123,
"acc_norm_stderr": 0.016501579306861677
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.630718954248366,
"acc_stderr": 0.02763417668960266,
"acc_norm": 0.630718954248366,
"acc_norm_stderr": 0.02763417668960266
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6366559485530546,
"acc_stderr": 0.027316847674192707,
"acc_norm": 0.6366559485530546,
"acc_norm_stderr": 0.027316847674192707
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6419753086419753,
"acc_stderr": 0.02667561192603709,
"acc_norm": 0.6419753086419753,
"acc_norm_stderr": 0.02667561192603709
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.39361702127659576,
"acc_stderr": 0.029144544781596136,
"acc_norm": 0.39361702127659576,
"acc_norm_stderr": 0.029144544781596136
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4165580182529335,
"acc_stderr": 0.012591153245057388,
"acc_norm": 0.4165580182529335,
"acc_norm_stderr": 0.012591153245057388
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5147058823529411,
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"acc_norm_stderr": 0.03035969707904611
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5784313725490197,
"acc_stderr": 0.019977422600227474,
"acc_norm": 0.5784313725490197,
"acc_norm_stderr": 0.019977422600227474
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
"acc_stderr": 0.045820048415054174,
"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6122448979591837,
"acc_stderr": 0.031192230726795656,
"acc_norm": 0.6122448979591837,
"acc_norm_stderr": 0.031192230726795656
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7263681592039801,
"acc_stderr": 0.03152439186555402,
"acc_norm": 0.7263681592039801,
"acc_norm_stderr": 0.03152439186555402
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.8,
"acc_stderr": 0.04020151261036846,
"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4939759036144578,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.4939759036144578,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7719298245614035,
"acc_stderr": 0.032180937956023566,
"acc_norm": 0.7719298245614035,
"acc_norm_stderr": 0.032180937956023566
},
"harness|truthfulqa:mc|0": {
"mc1": 0.35006119951040393,
"mc1_stderr": 0.01669794942015103,
"mc2": 0.5051246541228124,
"mc2_stderr": 0.015683474268697605
},
"harness|winogrande|5": {
"acc": 0.7616416732438832,
"acc_stderr": 0.011974948667702311
},
"harness|drop|3": {
"em": 0.10371224832214765,
"em_stderr": 0.003122327158910168,
"f1": 0.1647325922818787,
"f1_stderr": 0.003269141000174996
},
"harness|gsm8k|5": {
"acc": 0.1508718726307809,
"acc_stderr": 0.009859004137305687
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-13B | [
"region:us"
]
| 2023-11-18T18:04:27+00:00 | {"pretty_name": "Evaluation run of ajibawa-2023/Uncensored-Jordan-13B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ajibawa-2023/Uncensored-Jordan-13B](https://huggingface.co/ajibawa-2023/Uncensored-Jordan-13B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-13B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T18:01:22.350849](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-13B_public/blob/main/results_2023-11-18T18-01-22.350849.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5551346436733366,\n \"acc_stderr\": 0.033773935379363566,\n \"acc_norm\": 0.5623156588862028,\n \"acc_norm_stderr\": 0.03452935511879212,\n \"mc1\": 0.35006119951040393,\n \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5051246541228124,\n \"mc2_stderr\": 0.015683474268697605,\n \"em\": 0.10371224832214765,\n \"em_stderr\": 0.003122327158910168,\n \"f1\": 0.1647325922818787,\n \"f1_stderr\": 0.003269141000174996\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5401023890784983,\n \"acc_stderr\": 0.014564318856924848,\n \"acc_norm\": 0.5742320819112628,\n \"acc_norm_stderr\": 0.01444946427886881\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6352320254929297,\n \"acc_stderr\": 0.004803812631994952,\n \"acc_norm\": 0.8270264887472615,\n \"acc_norm_stderr\": 0.0037745138826159514\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5657894736842105,\n \"acc_stderr\": 0.04033565667848319,\n \"acc_norm\": 0.5657894736842105,\n \"acc_norm_stderr\": 0.04033565667848319\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.03005258057955785,\n \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955785\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5833333333333334,\n \"acc_stderr\": 0.041227287076512825,\n \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.041227287076512825\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n \"acc_stderr\": 0.03804749744364764,\n \"acc_norm\": 0.5317919075144508,\n \"acc_norm_stderr\": 0.03804749744364764\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4851063829787234,\n \"acc_stderr\": 0.032671518489247764,\n \"acc_norm\": 0.4851063829787234,\n \"acc_norm_stderr\": 0.032671518489247764\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.31746031746031744,\n \"acc_stderr\": 0.023973861998992065,\n \"acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.023973861998992065\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.043255060420170854,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.043255060420170854\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6516129032258065,\n \"acc_stderr\": 0.027104826328100944,\n \"acc_norm\": 0.6516129032258065,\n \"acc_norm_stderr\": 0.027104826328100944\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406795,\n \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406795\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.03663974994391244,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.03663974994391244\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.6919191919191919,\n \"acc_stderr\": 0.032894773300986155,\n \"acc_norm\": 0.6919191919191919,\n \"acc_norm_stderr\": 0.032894773300986155\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8031088082901554,\n \"acc_stderr\": 0.028697873971860688,\n \"acc_norm\": 0.8031088082901554,\n \"acc_norm_stderr\": 0.028697873971860688\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4897435897435897,\n \"acc_stderr\": 0.025345672221942374,\n \"acc_norm\": 0.4897435897435897,\n \"acc_norm_stderr\": 0.025345672221942374\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3037037037037037,\n \"acc_stderr\": 0.02803792996911499,\n \"acc_norm\": 0.3037037037037037,\n \"acc_norm_stderr\": 0.02803792996911499\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.032252942323996406,\n \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.032252942323996406\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.304635761589404,\n \"acc_stderr\": 0.037579499229433426,\n \"acc_norm\": 0.304635761589404,\n \"acc_norm_stderr\": 0.037579499229433426\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7394495412844037,\n \"acc_stderr\": 0.01881918203485007,\n \"acc_norm\": 0.7394495412844037,\n \"acc_norm_stderr\": 0.01881918203485007\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.37037037037037035,\n \"acc_stderr\": 0.03293377139415191,\n \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.03293377139415191\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7794117647058824,\n \"acc_stderr\": 0.02910225438967408,\n \"acc_norm\": 0.7794117647058824,\n \"acc_norm_stderr\": 0.02910225438967408\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.729957805907173,\n \"acc_stderr\": 0.028900721906293426,\n \"acc_norm\": 0.729957805907173,\n \"acc_norm_stderr\": 0.028900721906293426\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.031602951437766785,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.031602951437766785\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6259541984732825,\n \"acc_stderr\": 0.042438692422305246,\n \"acc_norm\": 0.6259541984732825,\n \"acc_norm_stderr\": 0.042438692422305246\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908705,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908705\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6388888888888888,\n \"acc_stderr\": 0.04643454608906275,\n \"acc_norm\": 0.6388888888888888,\n \"acc_norm_stderr\": 0.04643454608906275\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6687116564417178,\n \"acc_stderr\": 0.03697983910025588,\n \"acc_norm\": 0.6687116564417178,\n \"acc_norm_stderr\": 0.03697983910025588\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6699029126213593,\n \"acc_stderr\": 0.0465614711001235,\n \"acc_norm\": 0.6699029126213593,\n \"acc_norm_stderr\": 0.0465614711001235\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8376068376068376,\n \"acc_stderr\": 0.02416161812798774,\n \"acc_norm\": 0.8376068376068376,\n \"acc_norm_stderr\": 0.02416161812798774\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7573435504469987,\n \"acc_stderr\": 0.01532988894089986,\n \"acc_norm\": 0.7573435504469987,\n \"acc_norm_stderr\": 0.01532988894089986\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.02599247202930639,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.02599247202930639\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41899441340782123,\n \"acc_stderr\": 0.016501579306861677,\n \"acc_norm\": 0.41899441340782123,\n \"acc_norm_stderr\": 0.016501579306861677\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.630718954248366,\n \"acc_stderr\": 0.02763417668960266,\n \"acc_norm\": 0.630718954248366,\n \"acc_norm_stderr\": 0.02763417668960266\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6366559485530546,\n \"acc_stderr\": 0.027316847674192707,\n \"acc_norm\": 0.6366559485530546,\n \"acc_norm_stderr\": 0.027316847674192707\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6419753086419753,\n \"acc_stderr\": 0.02667561192603709,\n \"acc_norm\": 0.6419753086419753,\n \"acc_norm_stderr\": 0.02667561192603709\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.39361702127659576,\n \"acc_stderr\": 0.029144544781596136,\n \"acc_norm\": 0.39361702127659576,\n \"acc_norm_stderr\": 0.029144544781596136\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4165580182529335,\n \"acc_stderr\": 0.012591153245057388,\n \"acc_norm\": 0.4165580182529335,\n \"acc_norm_stderr\": 0.012591153245057388\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5147058823529411,\n \"acc_stderr\": 0.03035969707904611,\n \"acc_norm\": 0.5147058823529411,\n \"acc_norm_stderr\": 0.03035969707904611\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.5784313725490197,\n \"acc_stderr\": 0.019977422600227474,\n \"acc_norm\": 0.5784313725490197,\n \"acc_norm_stderr\": 0.019977422600227474\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6122448979591837,\n \"acc_stderr\": 0.031192230726795656,\n \"acc_norm\": 0.6122448979591837,\n \"acc_norm_stderr\": 0.031192230726795656\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7263681592039801,\n \"acc_stderr\": 0.03152439186555402,\n \"acc_norm\": 0.7263681592039801,\n \"acc_norm_stderr\": 0.03152439186555402\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7719298245614035,\n \"acc_stderr\": 0.032180937956023566,\n \"acc_norm\": 0.7719298245614035,\n \"acc_norm_stderr\": 0.032180937956023566\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35006119951040393,\n \"mc1_stderr\": 0.01669794942015103,\n \"mc2\": 0.5051246541228124,\n \"mc2_stderr\": 0.015683474268697605\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7616416732438832,\n \"acc_stderr\": 0.011974948667702311\n },\n \"harness|drop|3\": {\n \"em\": 0.10371224832214765,\n \"em_stderr\": 0.003122327158910168,\n \"f1\": 0.1647325922818787,\n \"f1_stderr\": 0.003269141000174996\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1508718726307809,\n \"acc_stderr\": 0.009859004137305687\n }\n}\n```", "repo_url": "https://huggingface.co/ajibawa-2023/Uncensored-Jordan-13B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_18T18_01_22.350849", "path": ["**/details_harness|arc:challenge|25_2023-11-18T18-01-22.350849.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-18T18-01-22.350849.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_18T18_01_22.350849", "path": ["**/details_harness|drop|3_2023-11-18T18-01-22.350849.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-18T18-01-22.350849.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_18T18_01_22.350849", "path": ["**/details_harness|gsm8k|5_2023-11-18T18-01-22.350849.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-18T18-01-22.350849.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_18T18_01_22.350849", "path": ["**/details_harness|hellaswag|10_2023-11-18T18-01-22.350849.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-18T18-01-22.350849.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_18T18_01_22.350849", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T18-01-22.350849.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T18-01-22.350849.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_18T18_01_22.350849", "path": ["results_2023-11-18T18-01-22.350849.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T18-01-22.350849.parquet"]}]}]} | 2023-11-18T18:05:16+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Jordan-13B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-13B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T18:01:22.350849(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T18:01:22.350849(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"## Dataset Structure",
"### Data Instances",
"### Data Fields",
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"### Curation Rationale",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T18:01:22.350849(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Jordan-13B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-13B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T18:01:22.350849(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
32ee2db5abee7aeca76337ff5250dee48ef7a624 |
# Dataset Card for Evaluation run of KnutJaegersberg/MistralInstructLongish
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/MistralInstructLongish
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [KnutJaegersberg/MistralInstructLongish](https://huggingface.co/KnutJaegersberg/MistralInstructLongish) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KnutJaegersberg__MistralInstructLongish_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T18:06:36.075482](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__MistralInstructLongish_public/blob/main/results_2023-11-18T18-06-36.075482.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5973103007065012,
"acc_stderr": 0.03292863737262349,
"acc_norm": 0.6085223268477976,
"acc_norm_stderr": 0.033764939289429516,
"mc1": 0.2729498164014688,
"mc1_stderr": 0.015594753632006526,
"mc2": 0.4055061003617047,
"mc2_stderr": 0.014261205384601018,
"em": 0.06910654362416108,
"em_stderr": 0.0025974621402952,
"f1": 0.21221371644295373,
"f1_stderr": 0.00318177597759032
},
"harness|arc:challenge|25": {
"acc": 0.5537542662116041,
"acc_stderr": 0.014526705548539978,
"acc_norm": 0.6075085324232082,
"acc_norm_stderr": 0.014269634635670717
},
"harness|hellaswag|10": {
"acc": 0.6246763592909779,
"acc_stderr": 0.004832167854501645,
"acc_norm": 0.8185620394343757,
"acc_norm_stderr": 0.0038459301696437916
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.042320736951515885,
"acc_norm": 0.6,
"acc_norm_stderr": 0.042320736951515885
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.625,
"acc_stderr": 0.039397364351956274,
"acc_norm": 0.625,
"acc_norm_stderr": 0.039397364351956274
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
"acc_stderr": 0.02854479331905533,
"acc_norm": 0.6867924528301886,
"acc_norm_stderr": 0.02854479331905533
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6527777777777778,
"acc_stderr": 0.039812405437178615,
"acc_norm": 0.6527777777777778,
"acc_norm_stderr": 0.039812405437178615
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
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"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.36,
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"harness|hendrycksTest-college_medicine|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_us_history|5": {
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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},
"harness|winogrande|5": {
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},
"harness|drop|3": {
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"f1": 0.21221371644295373,
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},
"harness|gsm8k|5": {
"acc": 0.015163002274450341,
"acc_stderr": 0.00336602294972636
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_KnutJaegersberg__MistralInstructLongish | [
"region:us"
]
| 2023-11-18T18:09:34+00:00 | {"pretty_name": "Evaluation run of KnutJaegersberg/MistralInstructLongish", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/MistralInstructLongish](https://huggingface.co/KnutJaegersberg/MistralInstructLongish) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KnutJaegersberg__MistralInstructLongish_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T18:06:36.075482](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__MistralInstructLongish_public/blob/main/results_2023-11-18T18-06-36.075482.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5973103007065012,\n \"acc_stderr\": 0.03292863737262349,\n \"acc_norm\": 0.6085223268477976,\n \"acc_norm_stderr\": 0.033764939289429516,\n \"mc1\": 0.2729498164014688,\n \"mc1_stderr\": 0.015594753632006526,\n \"mc2\": 0.4055061003617047,\n \"mc2_stderr\": 0.014261205384601018,\n \"em\": 0.06910654362416108,\n \"em_stderr\": 0.0025974621402952,\n \"f1\": 0.21221371644295373,\n \"f1_stderr\": 0.00318177597759032\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5537542662116041,\n \"acc_stderr\": 0.014526705548539978,\n \"acc_norm\": 0.6075085324232082,\n \"acc_norm_stderr\": 0.014269634635670717\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6246763592909779,\n \"acc_stderr\": 0.004832167854501645,\n \"acc_norm\": 0.8185620394343757,\n \"acc_norm_stderr\": 0.0038459301696437916\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 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{"config_name": "results", "data_files": [{"split": "2023_11_18T18_06_36.075482", "path": ["results_2023-11-18T18-06-36.075482.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T18-06-36.075482.parquet"]}]}]} | 2023-11-18T18:10:22+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/MistralInstructLongish
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model KnutJaegersberg/MistralInstructLongish on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T18:06:36.075482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of KnutJaegersberg/MistralInstructLongish",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/MistralInstructLongish on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T18:06:36.075482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Languages",
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"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
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"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/MistralInstructLongish on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T18:06:36.075482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/MistralInstructLongish## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/MistralInstructLongish on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T18:06:36.075482(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
82128bd4c95943e43fb94682f7f9a5b9333b5560 |
## Data Description
Hebrew Speech Recognition dataset from [Campus IL](https://campus.gov.il/).
Data was scraped from the Campus website, which contains video lectures from various courses in Hebrew.
Then subtitles were extracted from the videos and aligned with the audio.
Subtitles that are not on Hebrew were removed (WIP: need to remove non-Hebrew audio as well, e.g. using simple classifier).
Samples with duration less than 3 second were removed.
Total duration of the dataset is 152 hours.
Outliers in terms of the duration/char ratio were not removed, so it's possible to find suspiciously long or short sentences compared to the duration.
Note: if loading is slow, just clone it :
`git clone hebrew_speech_campus && cd hebrew_speech_campus && git lfs pull`
and load it from the folder `load_dataset("./hebrew_speech_campus")`
## Data Format
Audio files are in WAV format, 16kHz sampling rate, 16bit, mono. Ignore `path` field, use `audio.array` field value.
## Data Usage
```python
from datasets import load_dataset
ds = load_dataset("imvladikon/hebrew_speech_campus", split="train", streaming=True)
print(next(iter(ds)))
```
## Data Sample
```
{'uid': '10c3eda27cf173ab25bde755d0023abed301fcfd',
'file_id': '10c3eda27cf173ab25bde755d0023abed301fcfd_13',
'audio': {'path': '/content/hebrew_speech_campus/data/from_another_angle-_mathematics_teaching_practices/10c3eda27cf173ab25bde755d0023abed301fcfd_13.wav',
'array': array([ 5.54326562e-07, 3.60812592e-05, -2.35188054e-04, ...,
2.34067178e-04, 1.55649337e-04, 6.32447700e-05]),
'sampling_rate': 16000},
'sentence': 'הדוברים צריכים לקחת עליו אחריות, ולהיות מחויבים לו כלומר, השיח צריך להיות מחויב',
'n_segment': 13,
'duration_ms': 6607.98193359375,
'language': 'he',
'sample_rate': 16000,
'course': 'from_another_angle-_mathematics_teaching_practices',
'sentence_length': 79,
'n_tokens': 13}
```
## Data Splits and Stats
Split: train
Number of samples: 75924
## Citation
Please cite the following if you use this dataset in your work:
```
@misc{imvladikon2023hebrew_speech_campus,
author = {Gurevich, Vladimir},
title = {Hebrew Speech Recognition Dataset: Campus},
year = {2023},
howpublished = \url{https://huggingface.co/datasets/imvladikon/hebrew_speech_campus},
}
```
| imvladikon/hebrew_speech_campus | [
"task_categories:automatic-speech-recognition",
"size_categories:10K<n<100K",
"language:he",
"region:us"
]
| 2023-11-18T18:39:11+00:00 | {"language": ["he"], "size_categories": ["10K<n<100K"], "task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "uid", "dtype": "string"}, {"name": "file_id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "n_segment", "dtype": "int32"}, {"name": "duration_ms", "dtype": "float32"}, {"name": "language", "dtype": "string"}, {"name": "sample_rate", "dtype": "int32"}, {"name": "course", "dtype": "string"}, {"name": "sentence_length", "dtype": "int32"}, {"name": "n_tokens", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 17559119499.576, "num_examples": 75924}], "download_size": 17274739665, "dataset_size": 17559119499.576}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-20T21:46:41+00:00 | []
| [
"he"
]
| TAGS
#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Hebrew #region-us
|
## Data Description
Hebrew Speech Recognition dataset from Campus IL.
Data was scraped from the Campus website, which contains video lectures from various courses in Hebrew.
Then subtitles were extracted from the videos and aligned with the audio.
Subtitles that are not on Hebrew were removed (WIP: need to remove non-Hebrew audio as well, e.g. using simple classifier).
Samples with duration less than 3 second were removed.
Total duration of the dataset is 152 hours.
Outliers in terms of the duration/char ratio were not removed, so it's possible to find suspiciously long or short sentences compared to the duration.
Note: if loading is slow, just clone it :
'git clone hebrew_speech_campus && cd hebrew_speech_campus && git lfs pull'
and load it from the folder 'load_dataset("./hebrew_speech_campus")'
## Data Format
Audio files are in WAV format, 16kHz sampling rate, 16bit, mono. Ignore 'path' field, use 'URL' field value.
## Data Usage
## Data Sample
## Data Splits and Stats
Split: train
Number of samples: 75924
Please cite the following if you use this dataset in your work:
| [
"## Data Description\n\nHebrew Speech Recognition dataset from Campus IL. \n\nData was scraped from the Campus website, which contains video lectures from various courses in Hebrew. \nThen subtitles were extracted from the videos and aligned with the audio. \nSubtitles that are not on Hebrew were removed (WIP: need to remove non-Hebrew audio as well, e.g. using simple classifier). \nSamples with duration less than 3 second were removed. \nTotal duration of the dataset is 152 hours. \nOutliers in terms of the duration/char ratio were not removed, so it's possible to find suspiciously long or short sentences compared to the duration. \nNote: if loading is slow, just clone it : \n'git clone hebrew_speech_campus && cd hebrew_speech_campus && git lfs pull' \nand load it from the folder 'load_dataset(\"./hebrew_speech_campus\")'",
"## Data Format\n\nAudio files are in WAV format, 16kHz sampling rate, 16bit, mono. Ignore 'path' field, use 'URL' field value.",
"## Data Usage",
"## Data Sample",
"## Data Splits and Stats\nSplit: train \nNumber of samples: 75924 \n\nPlease cite the following if you use this dataset in your work:"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Hebrew #region-us \n",
"## Data Description\n\nHebrew Speech Recognition dataset from Campus IL. \n\nData was scraped from the Campus website, which contains video lectures from various courses in Hebrew. \nThen subtitles were extracted from the videos and aligned with the audio. \nSubtitles that are not on Hebrew were removed (WIP: need to remove non-Hebrew audio as well, e.g. using simple classifier). \nSamples with duration less than 3 second were removed. \nTotal duration of the dataset is 152 hours. \nOutliers in terms of the duration/char ratio were not removed, so it's possible to find suspiciously long or short sentences compared to the duration. \nNote: if loading is slow, just clone it : \n'git clone hebrew_speech_campus && cd hebrew_speech_campus && git lfs pull' \nand load it from the folder 'load_dataset(\"./hebrew_speech_campus\")'",
"## Data Format\n\nAudio files are in WAV format, 16kHz sampling rate, 16bit, mono. Ignore 'path' field, use 'URL' field value.",
"## Data Usage",
"## Data Sample",
"## Data Splits and Stats\nSplit: train \nNumber of samples: 75924 \n\nPlease cite the following if you use this dataset in your work:"
]
| [
39,
216,
39,
4,
4,
32
]
| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Hebrew #region-us \n## Data Description\n\nHebrew Speech Recognition dataset from Campus IL. \n\nData was scraped from the Campus website, which contains video lectures from various courses in Hebrew. \nThen subtitles were extracted from the videos and aligned with the audio. \nSubtitles that are not on Hebrew were removed (WIP: need to remove non-Hebrew audio as well, e.g. using simple classifier). \nSamples with duration less than 3 second were removed. \nTotal duration of the dataset is 152 hours. \nOutliers in terms of the duration/char ratio were not removed, so it's possible to find suspiciously long or short sentences compared to the duration. \nNote: if loading is slow, just clone it : \n'git clone hebrew_speech_campus && cd hebrew_speech_campus && git lfs pull' \nand load it from the folder 'load_dataset(\"./hebrew_speech_campus\")'## Data Format\n\nAudio files are in WAV format, 16kHz sampling rate, 16bit, mono. Ignore 'path' field, use 'URL' field value.## Data Usage## Data Sample## Data Splits and Stats\nSplit: train \nNumber of samples: 75924 \n\nPlease cite the following if you use this dataset in your work:"
]
|
0ffeea714bbede2f0c1a5c9445a09a7385f905d0 |
# Dataset Card for Evaluation run of lgaalves/mistral-7b_open_platypus
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lgaalves/mistral-7b_open_platypus
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [lgaalves/mistral-7b_open_platypus](https://huggingface.co/lgaalves/mistral-7b_open_platypus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lgaalves__mistral-7b_open_platypus_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T19:20:26.136874](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__mistral-7b_open_platypus_public/blob/main/results_2023-11-18T19-20-26.136874.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.5921618091275235,
"acc_stderr": 0.033165593817109554,
"acc_norm": 0.6007436240197009,
"acc_norm_stderr": 0.03392093055241413,
"mc1": 0.3292533659730722,
"mc1_stderr": 0.016451264440068232,
"mc2": 0.48869138188349615,
"mc2_stderr": 0.0147358552004315,
"em": 0.0036703020134228187,
"em_stderr": 0.0006192871806511272,
"f1": 0.06589450503355675,
"f1_stderr": 0.0014663770308574477
},
"harness|arc:challenge|25": {
"acc": 0.5332764505119454,
"acc_stderr": 0.014578995859605808,
"acc_norm": 0.5580204778156996,
"acc_norm_stderr": 0.014512682523128343
},
"harness|hellaswag|10": {
"acc": 0.6120294761999602,
"acc_stderr": 0.004862919176408075,
"acc_norm": 0.8212507468631747,
"acc_norm_stderr": 0.003823591814133036
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5703703703703704,
"acc_stderr": 0.042763494943765995,
"acc_norm": 0.5703703703703704,
"acc_norm_stderr": 0.042763494943765995
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6381578947368421,
"acc_stderr": 0.03910525752849724,
"acc_norm": 0.6381578947368421,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6566037735849056,
"acc_stderr": 0.02922452646912479,
"acc_norm": 0.6566037735849056,
"acc_norm_stderr": 0.02922452646912479
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6458333333333334,
"acc_stderr": 0.039994111357535424,
"acc_norm": 0.6458333333333334,
"acc_norm_stderr": 0.039994111357535424
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709390974,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709390974
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.03750757044895536,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.03750757044895536
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3137254901960784,
"acc_stderr": 0.04617034827006717,
"acc_norm": 0.3137254901960784,
"acc_norm_stderr": 0.04617034827006717
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816506,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816506
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4978723404255319,
"acc_stderr": 0.03268572658667492,
"acc_norm": 0.4978723404255319,
"acc_norm_stderr": 0.03268572658667492
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.04692008381368909,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.04692008381368909
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5103448275862069,
"acc_stderr": 0.041657747757287644,
"acc_norm": 0.5103448275862069,
"acc_norm_stderr": 0.041657747757287644
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.025446365634406776,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.025446365634406776
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.373015873015873,
"acc_stderr": 0.04325506042017086,
"acc_norm": 0.373015873015873,
"acc_norm_stderr": 0.04325506042017086
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6806451612903226,
"acc_stderr": 0.026522709674667765,
"acc_norm": 0.6806451612903226,
"acc_norm_stderr": 0.026522709674667765
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.46798029556650245,
"acc_stderr": 0.03510766597959217,
"acc_norm": 0.46798029556650245,
"acc_norm_stderr": 0.03510766597959217
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.64,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.64,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7393939393939394,
"acc_stderr": 0.034277431758165236,
"acc_norm": 0.7393939393939394,
"acc_norm_stderr": 0.034277431758165236
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7272727272727273,
"acc_stderr": 0.03173071239071724,
"acc_norm": 0.7272727272727273,
"acc_norm_stderr": 0.03173071239071724
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8497409326424871,
"acc_stderr": 0.025787723180723872,
"acc_norm": 0.8497409326424871,
"acc_norm_stderr": 0.025787723180723872
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5512820512820513,
"acc_stderr": 0.025217315184846486,
"acc_norm": 0.5512820512820513,
"acc_norm_stderr": 0.025217315184846486
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2814814814814815,
"acc_stderr": 0.02742001935094528,
"acc_norm": 0.2814814814814815,
"acc_norm_stderr": 0.02742001935094528
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5672268907563025,
"acc_stderr": 0.032183581077426124,
"acc_norm": 0.5672268907563025,
"acc_norm_stderr": 0.032183581077426124
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.03879687024073327,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.03879687024073327
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7724770642201835,
"acc_stderr": 0.017974463578776502,
"acc_norm": 0.7724770642201835,
"acc_norm_stderr": 0.017974463578776502
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.39814814814814814,
"acc_stderr": 0.033384734032074016,
"acc_norm": 0.39814814814814814,
"acc_norm_stderr": 0.033384734032074016
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7696078431372549,
"acc_stderr": 0.02955429260569507,
"acc_norm": 0.7696078431372549,
"acc_norm_stderr": 0.02955429260569507
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7848101265822784,
"acc_stderr": 0.026750826994676177,
"acc_norm": 0.7848101265822784,
"acc_norm_stderr": 0.026750826994676177
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.726457399103139,
"acc_stderr": 0.029918586707798834,
"acc_norm": 0.726457399103139,
"acc_norm_stderr": 0.029918586707798834
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6564885496183206,
"acc_stderr": 0.041649760719448786,
"acc_norm": 0.6564885496183206,
"acc_norm_stderr": 0.041649760719448786
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8429752066115702,
"acc_stderr": 0.03321244842547128,
"acc_norm": 0.8429752066115702,
"acc_norm_stderr": 0.03321244842547128
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.044143436668549335,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.044143436668549335
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.6990291262135923,
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"acc_norm": 0.6990291262135923,
"acc_norm_stderr": 0.04541609446503948
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"harness|hendrycksTest-marketing|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.024414947304543678,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.024414947304543678
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7956577266922095,
"acc_stderr": 0.014419123980931895,
"acc_norm": 0.7956577266922095,
"acc_norm_stderr": 0.014419123980931895
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.024818350129436593,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.024818350129436593
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.376536312849162,
"acc_stderr": 0.016204672385106603,
"acc_norm": 0.376536312849162,
"acc_norm_stderr": 0.016204672385106603
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.027363593284684972,
"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.027363593284684972
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7041800643086816,
"acc_stderr": 0.025922371788818763,
"acc_norm": 0.7041800643086816,
"acc_norm_stderr": 0.025922371788818763
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.02517104191530968,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.02517104191530968
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4858156028368794,
"acc_stderr": 0.02981549448368206,
"acc_norm": 0.4858156028368794,
"acc_norm_stderr": 0.02981549448368206
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.44589308996088656,
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"acc_norm": 0.44589308996088656,
"acc_norm_stderr": 0.012695244711379774
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5919117647058824,
"acc_stderr": 0.029855261393483924,
"acc_norm": 0.5919117647058824,
"acc_norm_stderr": 0.029855261393483924
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6209150326797386,
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"acc_norm": 0.6209150326797386,
"acc_norm_stderr": 0.01962744474841223
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6272727272727273,
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"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.04631381319425465
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6244897959183674,
"acc_stderr": 0.03100120903989484,
"acc_norm": 0.6244897959183674,
"acc_norm_stderr": 0.03100120903989484
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7512437810945274,
"acc_stderr": 0.030567675938916714,
"acc_norm": 0.7512437810945274,
"acc_norm_stderr": 0.030567675938916714
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4879518072289157,
"acc_stderr": 0.0389136449583582,
"acc_norm": 0.4879518072289157,
"acc_norm_stderr": 0.0389136449583582
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.031885780176863984,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.031885780176863984
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3292533659730722,
"mc1_stderr": 0.016451264440068232,
"mc2": 0.48869138188349615,
"mc2_stderr": 0.0147358552004315
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090254
},
"harness|drop|3": {
"em": 0.0036703020134228187,
"em_stderr": 0.0006192871806511272,
"f1": 0.06589450503355675,
"f1_stderr": 0.0014663770308574477
},
"harness|gsm8k|5": {
"acc": 0.12585291887793784,
"acc_stderr": 0.009136212598406307
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_lgaalves__mistral-7b_open_platypus | [
"region:us"
]
| 2023-11-18T19:23:24+00:00 | {"pretty_name": "Evaluation run of lgaalves/mistral-7b_open_platypus", "dataset_summary": "Dataset automatically created during the evaluation run of model [lgaalves/mistral-7b_open_platypus](https://huggingface.co/lgaalves/mistral-7b_open_platypus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lgaalves__mistral-7b_open_platypus_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T19:20:26.136874](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__mistral-7b_open_platypus_public/blob/main/results_2023-11-18T19-20-26.136874.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5921618091275235,\n \"acc_stderr\": 0.033165593817109554,\n \"acc_norm\": 0.6007436240197009,\n \"acc_norm_stderr\": 0.03392093055241413,\n \"mc1\": 0.3292533659730722,\n \"mc1_stderr\": 0.016451264440068232,\n \"mc2\": 0.48869138188349615,\n \"mc2_stderr\": 0.0147358552004315,\n \"em\": 0.0036703020134228187,\n \"em_stderr\": 0.0006192871806511272,\n \"f1\": 0.06589450503355675,\n \"f1_stderr\": 0.0014663770308574477\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5332764505119454,\n \"acc_stderr\": 0.014578995859605808,\n \"acc_norm\": 0.5580204778156996,\n \"acc_norm_stderr\": 0.014512682523128343\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6120294761999602,\n \"acc_stderr\": 0.004862919176408075,\n \"acc_norm\": 0.8212507468631747,\n \"acc_norm_stderr\": 0.003823591814133036\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5703703703703704,\n \"acc_stderr\": 0.042763494943765995,\n \"acc_norm\": 0.5703703703703704,\n \"acc_norm_stderr\": 0.042763494943765995\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6381578947368421,\n \"acc_stderr\": 0.03910525752849724,\n \"acc_norm\": 0.6381578947368421,\n \"acc_norm_stderr\": 0.03910525752849724\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.02922452646912479,\n \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.02922452646912479\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6458333333333334,\n \"acc_stderr\": 0.039994111357535424,\n \"acc_norm\": 0.6458333333333334,\n \"acc_norm_stderr\": 0.039994111357535424\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709390974,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709390974\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895536,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895536\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3137254901960784,\n \"acc_stderr\": 0.04617034827006717,\n \"acc_norm\": 0.3137254901960784,\n \"acc_norm_stderr\": 0.04617034827006717\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.77,\n \"acc_stderr\": 0.04229525846816506,\n \"acc_norm\": 0.77,\n \"acc_norm_stderr\": 0.04229525846816506\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4978723404255319,\n \"acc_stderr\": 0.03268572658667492,\n \"acc_norm\": 0.4978723404255319,\n \"acc_norm_stderr\": 0.03268572658667492\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5103448275862069,\n \"acc_stderr\": 0.041657747757287644,\n \"acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.041657747757287644\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406776,\n \"acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406776\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.373015873015873,\n \"acc_stderr\": 0.04325506042017086,\n \"acc_norm\": 0.373015873015873,\n \"acc_norm_stderr\": 0.04325506042017086\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.6806451612903226,\n \"acc_stderr\": 0.026522709674667765,\n \"acc_norm\": 0.6806451612903226,\n \"acc_norm_stderr\": 0.026522709674667765\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.46798029556650245,\n \"acc_stderr\": 0.03510766597959217,\n \"acc_norm\": 0.46798029556650245,\n \"acc_norm_stderr\": 0.03510766597959217\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7393939393939394,\n \"acc_stderr\": 0.034277431758165236,\n \"acc_norm\": 0.7393939393939394,\n \"acc_norm_stderr\": 0.034277431758165236\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.03173071239071724,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.03173071239071724\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8497409326424871,\n \"acc_stderr\": 0.025787723180723872,\n \"acc_norm\": 0.8497409326424871,\n \"acc_norm_stderr\": 0.025787723180723872\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.5512820512820513,\n \"acc_stderr\": 0.025217315184846486,\n \"acc_norm\": 0.5512820512820513,\n \"acc_norm_stderr\": 0.025217315184846486\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094528,\n \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.02742001935094528\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5672268907563025,\n \"acc_stderr\": 0.032183581077426124,\n \"acc_norm\": 0.5672268907563025,\n \"acc_norm_stderr\": 0.032183581077426124\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3443708609271523,\n \"acc_stderr\": 0.03879687024073327,\n \"acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.03879687024073327\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7724770642201835,\n \"acc_stderr\": 0.017974463578776502,\n \"acc_norm\": 0.7724770642201835,\n \"acc_norm_stderr\": 0.017974463578776502\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.39814814814814814,\n \"acc_stderr\": 0.033384734032074016,\n \"acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.033384734032074016\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7696078431372549,\n \"acc_stderr\": 0.02955429260569507,\n \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.02955429260569507\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7848101265822784,\n \"acc_stderr\": 0.026750826994676177,\n \"acc_norm\": 0.7848101265822784,\n \"acc_norm_stderr\": 0.026750826994676177\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.726457399103139,\n \"acc_stderr\": 0.029918586707798834,\n \"acc_norm\": 0.726457399103139,\n \"acc_norm_stderr\": 0.029918586707798834\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6564885496183206,\n \"acc_stderr\": 0.041649760719448786,\n \"acc_norm\": 0.6564885496183206,\n \"acc_norm_stderr\": 0.041649760719448786\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.8429752066115702,\n \"acc_stderr\": 0.03321244842547128,\n \"acc_norm\": 0.8429752066115702,\n \"acc_norm_stderr\": 0.03321244842547128\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.044143436668549335,\n \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.044143436668549335\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6990291262135923,\n \"acc_stderr\": 0.04541609446503948,\n \"acc_norm\": 0.6990291262135923,\n \"acc_norm_stderr\": 0.04541609446503948\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.024414947304543678,\n \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.024414947304543678\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7956577266922095,\n \"acc_stderr\": 0.014419123980931895,\n \"acc_norm\": 0.7956577266922095,\n \"acc_norm_stderr\": 0.014419123980931895\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.376536312849162,\n \"acc_stderr\": 0.016204672385106603,\n \"acc_norm\": 0.376536312849162,\n \"acc_norm_stderr\": 0.016204672385106603\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.027363593284684972,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.027363593284684972\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7129629629629629,\n \"acc_stderr\": 0.02517104191530968,\n \"acc_norm\": 0.7129629629629629,\n \"acc_norm_stderr\": 0.02517104191530968\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4858156028368794,\n \"acc_stderr\": 0.02981549448368206,\n \"acc_norm\": 0.4858156028368794,\n \"acc_norm_stderr\": 0.02981549448368206\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44589308996088656,\n \"acc_stderr\": 0.012695244711379774,\n \"acc_norm\": 0.44589308996088656,\n \"acc_norm_stderr\": 0.012695244711379774\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5919117647058824,\n \"acc_stderr\": 0.029855261393483924,\n \"acc_norm\": 0.5919117647058824,\n \"acc_norm_stderr\": 0.029855261393483924\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6209150326797386,\n \"acc_stderr\": 0.01962744474841223,\n \"acc_norm\": 0.6209150326797386,\n \"acc_norm_stderr\": 0.01962744474841223\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6272727272727273,\n \"acc_stderr\": 0.04631381319425465,\n \"acc_norm\": 0.6272727272727273,\n \"acc_norm_stderr\": 0.04631381319425465\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6244897959183674,\n \"acc_stderr\": 0.03100120903989484,\n \"acc_norm\": 0.6244897959183674,\n \"acc_norm_stderr\": 0.03100120903989484\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7512437810945274,\n \"acc_stderr\": 0.030567675938916714,\n \"acc_norm\": 0.7512437810945274,\n \"acc_norm_stderr\": 0.030567675938916714\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4879518072289157,\n \"acc_stderr\": 0.0389136449583582,\n \"acc_norm\": 0.4879518072289157,\n \"acc_norm_stderr\": 0.0389136449583582\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.031885780176863984,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.031885780176863984\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3292533659730722,\n \"mc1_stderr\": 0.016451264440068232,\n \"mc2\": 0.48869138188349615,\n \"mc2_stderr\": 0.0147358552004315\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090254\n },\n \"harness|drop|3\": {\n \"em\": 0.0036703020134228187,\n \"em_stderr\": 0.0006192871806511272,\n \"f1\": 0.06589450503355675,\n \"f1_stderr\": 0.0014663770308574477\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12585291887793784,\n \"acc_stderr\": 0.009136212598406307\n }\n}\n```", "repo_url": "https://huggingface.co/lgaalves/mistral-7b_open_platypus", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_18T19_20_26.136874", "path": ["**/details_harness|arc:challenge|25_2023-11-18T19-20-26.136874.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-18T19-20-26.136874.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_18T19_20_26.136874", "path": ["**/details_harness|drop|3_2023-11-18T19-20-26.136874.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-18T19-20-26.136874.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_18T19_20_26.136874", "path": ["**/details_harness|gsm8k|5_2023-11-18T19-20-26.136874.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-18T19-20-26.136874.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_18T19_20_26.136874", "path": ["**/details_harness|hellaswag|10_2023-11-18T19-20-26.136874.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-18T19-20-26.136874.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_18T19_20_26.136874", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T19-20-26.136874.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-18T19-20-26.136874.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_18T19_20_26.136874", "path": ["results_2023-11-18T19-20-26.136874.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T19-20-26.136874.parquet"]}]}]} | 2023-11-18T19:24:14+00:00 | []
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| TAGS
#region-us
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# Dataset Card for Evaluation run of lgaalves/mistral-7b_open_platypus
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model lgaalves/mistral-7b_open_platypus on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T19:20:26.136874(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lgaalves/mistral-7b_open_platypus on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of lgaalves/mistral-7b_open_platypus## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model lgaalves/mistral-7b_open_platypus on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T19:20:26.136874(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
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|
fafb4c92b1a26e1d548ba8ffb3fadb33bef33963 | # Dataset Card for "sentence-alignment-tib-eng"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | buddhist-nlp/sentence-alignment-tib-eng | [
"region:us"
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| 2023-11-18T19:24:28+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 739829565, "num_examples": 427999}, {"name": "validation", "num_bytes": 173302, "num_examples": 100}, {"name": "test", "num_bytes": 172985, "num_examples": 100}], "download_size": 461575214, "dataset_size": 740175852}} | 2023-11-18T19:25:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sentence-alignment-tib-eng"
More Information needed | [
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]
|
d87767f0d12e206cd55c67a0b9b08e386c585e67 | Started with:
https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1
(GPT-3.5 Turbo)
Randomly selected 1000 where output contained "```python" in output
Generated GPT-4 answers to those for the sake of LIMA-like "Python Tutor" Instruct fine-tuning as well as validate DPO Fine-Tuning (where GPT-4 answers will be preferred to GPT-3.5 Turbo)
Then filtered refusals (looking for "impossible" or "sorry")
GPT-4 System Prompt:
You are an intelligent assistant that generates Python code. Start generation with ```python and end with ``` and nothing else. Just content between ```python and ```. The generated code should be wrapped in triple backticks and language identifier. Each line of code should be accompanied by a comment explaining it, and every function definition should be followed by a docstring describing the function, solution approach, and any edge cases considered. Try to wrap code in a function. | KrisPi/PythonTutor-Evol-1k-DPO-GPT4_vs_35 | [
"size_categories:n<1K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-11-18T19:30:08+00:00 | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["n<1K"]} | 2023-11-18T19:32:35+00:00 | []
| [
"en"
]
| TAGS
#size_categories-n<1K #language-English #license-cc-by-nc-sa-4.0 #region-us
| Started with:
URL
(GPT-3.5 Turbo)
Randomly selected 1000 where output contained "python and end with python and '''. The generated code should be wrapped in triple backticks and language identifier. Each line of code should be accompanied by a comment explaining it, and every function definition should be followed by a docstring describing the function, solution approach, and any edge cases considered. Try to wrap code in a function. | []
| [
"TAGS\n#size_categories-n<1K #language-English #license-cc-by-nc-sa-4.0 #region-us \n"
]
| [
33
]
| [
"passage: TAGS\n#size_categories-n<1K #language-English #license-cc-by-nc-sa-4.0 #region-us \n"
]
|
f168780a43cc0130a76e9364b7c2c820c5f0442a |
# Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Jordan-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ajibawa-2023/Uncensored-Jordan-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ajibawa-2023/Uncensored-Jordan-7B](https://huggingface.co/ajibawa-2023/Uncensored-Jordan-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-7B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T19:37:27.743703](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-7B_public/blob/main/results_2023-11-18T19-37-27.743703.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4574910452896481,
"acc_stderr": 0.03440657715128802,
"acc_norm": 0.4632598229794625,
"acc_norm_stderr": 0.03522730896735207,
"mc1": 0.32558139534883723,
"mc1_stderr": 0.01640398946990783,
"mc2": 0.47497547233950527,
"mc2_stderr": 0.01568331719502122,
"em": 0.2236786912751678,
"em_stderr": 0.004267491957607617,
"f1": 0.2846486996644306,
"f1_stderr": 0.00427403120655588
},
"harness|arc:challenge|25": {
"acc": 0.49573378839590443,
"acc_stderr": 0.014610858923956955,
"acc_norm": 0.5127986348122867,
"acc_norm_stderr": 0.014606603181012538
},
"harness|hellaswag|10": {
"acc": 0.5867357100179247,
"acc_stderr": 0.0049141308554317776,
"acc_norm": 0.7736506671977693,
"acc_norm_stderr": 0.004176125850955359
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45185185185185184,
"acc_stderr": 0.04299268905480864,
"acc_norm": 0.45185185185185184,
"acc_norm_stderr": 0.04299268905480864
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.42105263157894735,
"acc_stderr": 0.04017901275981749,
"acc_norm": 0.42105263157894735,
"acc_norm_stderr": 0.04017901275981749
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.5056603773584906,
"acc_stderr": 0.030770900763851302,
"acc_norm": 0.5056603773584906,
"acc_norm_stderr": 0.030770900763851302
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4791666666666667,
"acc_stderr": 0.041775789507399935,
"acc_norm": 0.4791666666666667,
"acc_norm_stderr": 0.041775789507399935
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.42,
"acc_stderr": 0.04960449637488584,
"acc_norm": 0.42,
"acc_norm_stderr": 0.04960449637488584
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3988439306358382,
"acc_stderr": 0.037336266553835096,
"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.037336266553835096
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.27450980392156865,
"acc_stderr": 0.044405219061793275,
"acc_norm": 0.27450980392156865,
"acc_norm_stderr": 0.044405219061793275
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3829787234042553,
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"acc_norm": 0.3829787234042553,
"acc_norm_stderr": 0.03177821250236922
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.22807017543859648,
"acc_stderr": 0.03947152782669415,
"acc_norm": 0.22807017543859648,
"acc_norm_stderr": 0.03947152782669415
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4413793103448276,
"acc_stderr": 0.04137931034482757,
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"acc_norm_stderr": 0.04137931034482757
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2830687830687831,
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"acc_norm": 0.2830687830687831,
"acc_norm_stderr": 0.023201392938194974
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2698412698412698,
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"acc_norm": 0.2698412698412698,
"acc_norm_stderr": 0.03970158273235173
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5032258064516129,
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"acc_norm": 0.5032258064516129,
"acc_norm_stderr": 0.028443414226438323
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.32019704433497537,
"acc_stderr": 0.032826493853041504,
"acc_norm": 0.32019704433497537,
"acc_norm_stderr": 0.032826493853041504
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.5757575757575758,
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},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.5656565656565656,
"acc_norm_stderr": 0.03531505879359183
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.6528497409326425,
"acc_stderr": 0.03435696168361355,
"acc_norm": 0.6528497409326425,
"acc_norm_stderr": 0.03435696168361355
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4358974358974359,
"acc_stderr": 0.02514180151117749,
"acc_norm": 0.4358974358974359,
"acc_norm_stderr": 0.02514180151117749
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.23703703703703705,
"acc_stderr": 0.025928876132766114,
"acc_norm": 0.23703703703703705,
"acc_norm_stderr": 0.025928876132766114
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.39915966386554624,
"acc_stderr": 0.03181110032413925,
"acc_norm": 0.39915966386554624,
"acc_norm_stderr": 0.03181110032413925
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.03802039760107903,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.03802039760107903
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.5486238532110091,
"acc_stderr": 0.02133571471126879,
"acc_norm": 0.5486238532110091,
"acc_norm_stderr": 0.02133571471126879
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.03388857118502326,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.03388857118502326
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6029411764705882,
"acc_stderr": 0.03434131164719129,
"acc_norm": 0.6029411764705882,
"acc_norm_stderr": 0.03434131164719129
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.5822784810126582,
"acc_stderr": 0.032103530322412685,
"acc_norm": 0.5822784810126582,
"acc_norm_stderr": 0.032103530322412685
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5381165919282511,
"acc_stderr": 0.033460150119732274,
"acc_norm": 0.5381165919282511,
"acc_norm_stderr": 0.033460150119732274
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.4961832061068702,
"acc_stderr": 0.043851623256015534,
"acc_norm": 0.4961832061068702,
"acc_norm_stderr": 0.043851623256015534
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.5950413223140496,
"acc_stderr": 0.04481137755942469,
"acc_norm": 0.5950413223140496,
"acc_norm_stderr": 0.04481137755942469
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.5648148148148148,
"acc_stderr": 0.04792898170907061,
"acc_norm": 0.5648148148148148,
"acc_norm_stderr": 0.04792898170907061
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5153374233128835,
"acc_stderr": 0.039265223787088445,
"acc_norm": 0.5153374233128835,
"acc_norm_stderr": 0.039265223787088445
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.26785714285714285,
"acc_stderr": 0.04203277291467764,
"acc_norm": 0.26785714285714285,
"acc_norm_stderr": 0.04203277291467764
},
"harness|hendrycksTest-management|5": {
"acc": 0.5631067961165048,
"acc_stderr": 0.04911147107365777,
"acc_norm": 0.5631067961165048,
"acc_norm_stderr": 0.04911147107365777
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.688034188034188,
"acc_stderr": 0.03035152732334493,
"acc_norm": 0.688034188034188,
"acc_norm_stderr": 0.03035152732334493
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.6462324393358876,
"acc_stderr": 0.017098184708161906,
"acc_norm": 0.6462324393358876,
"acc_norm_stderr": 0.017098184708161906
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.5173410404624278,
"acc_stderr": 0.026902900458666647,
"acc_norm": 0.5173410404624278,
"acc_norm_stderr": 0.026902900458666647
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2435754189944134,
"acc_stderr": 0.014355911964767864,
"acc_norm": 0.2435754189944134,
"acc_norm_stderr": 0.014355911964767864
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4673202614379085,
"acc_stderr": 0.02856869975222587,
"acc_norm": 0.4673202614379085,
"acc_norm_stderr": 0.02856869975222587
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.49517684887459806,
"acc_stderr": 0.028396770444111298,
"acc_norm": 0.49517684887459806,
"acc_norm_stderr": 0.028396770444111298
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.5123456790123457,
"acc_stderr": 0.027812262269327228,
"acc_norm": 0.5123456790123457,
"acc_norm_stderr": 0.027812262269327228
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.34397163120567376,
"acc_stderr": 0.02833801742861132,
"acc_norm": 0.34397163120567376,
"acc_norm_stderr": 0.02833801742861132
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.34615384615384615,
"acc_norm_stderr": 0.012150699768228579
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4632352941176471,
"acc_stderr": 0.030290619180485697,
"acc_norm": 0.4632352941176471,
"acc_norm_stderr": 0.030290619180485697
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.41830065359477125,
"acc_stderr": 0.019955975145835542,
"acc_norm": 0.41830065359477125,
"acc_norm_stderr": 0.019955975145835542
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.4909090909090909,
"acc_stderr": 0.04788339768702861,
"acc_norm": 0.4909090909090909,
"acc_norm_stderr": 0.04788339768702861
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.4857142857142857,
"acc_stderr": 0.03199615232806286,
"acc_norm": 0.4857142857142857,
"acc_norm_stderr": 0.03199615232806286
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6218905472636815,
"acc_stderr": 0.034288678487786564,
"acc_norm": 0.6218905472636815,
"acc_norm_stderr": 0.034288678487786564
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-virology|5": {
"acc": 0.35542168674698793,
"acc_stderr": 0.03726214354322416,
"acc_norm": 0.35542168674698793,
"acc_norm_stderr": 0.03726214354322416
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.6374269005847953,
"acc_stderr": 0.0368713061556206,
"acc_norm": 0.6374269005847953,
"acc_norm_stderr": 0.0368713061556206
},
"harness|truthfulqa:mc|0": {
"mc1": 0.32558139534883723,
"mc1_stderr": 0.01640398946990783,
"mc2": 0.47497547233950527,
"mc2_stderr": 0.01568331719502122
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.012738241271018434
},
"harness|drop|3": {
"em": 0.2236786912751678,
"em_stderr": 0.004267491957607617,
"f1": 0.2846486996644306,
"f1_stderr": 0.00427403120655588
},
"harness|gsm8k|5": {
"acc": 0.06747536012130402,
"acc_stderr": 0.006909475136357452
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-7B | [
"region:us"
]
| 2023-11-18T19:39:52+00:00 | {"pretty_name": "Evaluation run of ajibawa-2023/Uncensored-Jordan-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [ajibawa-2023/Uncensored-Jordan-7B](https://huggingface.co/ajibawa-2023/Uncensored-Jordan-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-7B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T19:37:27.743703](https://huggingface.co/datasets/open-llm-leaderboard/details_ajibawa-2023__Uncensored-Jordan-7B_public/blob/main/results_2023-11-18T19-37-27.743703.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.4574910452896481,\n \"acc_stderr\": 0.03440657715128802,\n \"acc_norm\": 0.4632598229794625,\n \"acc_norm_stderr\": 0.03522730896735207,\n \"mc1\": 0.32558139534883723,\n \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.47497547233950527,\n \"mc2_stderr\": 0.01568331719502122,\n \"em\": 0.2236786912751678,\n \"em_stderr\": 0.004267491957607617,\n \"f1\": 0.2846486996644306,\n \"f1_stderr\": 0.00427403120655588\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.49573378839590443,\n \"acc_stderr\": 0.014610858923956955,\n \"acc_norm\": 0.5127986348122867,\n \"acc_norm_stderr\": 0.014606603181012538\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5867357100179247,\n \"acc_stderr\": 0.0049141308554317776,\n \"acc_norm\": 0.7736506671977693,\n \"acc_norm_stderr\": 0.004176125850955359\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45185185185185184,\n \"acc_stderr\": 0.04299268905480864,\n \"acc_norm\": 0.45185185185185184,\n \"acc_norm_stderr\": 0.04299268905480864\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.42105263157894735,\n \"acc_stderr\": 0.04017901275981749,\n \"acc_norm\": 0.42105263157894735,\n \"acc_norm_stderr\": 0.04017901275981749\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5056603773584906,\n \"acc_stderr\": 0.030770900763851302,\n \"acc_norm\": 0.5056603773584906,\n \"acc_norm_stderr\": 0.030770900763851302\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4791666666666667,\n \"acc_stderr\": 0.041775789507399935,\n \"acc_norm\": 0.4791666666666667,\n \"acc_norm_stderr\": 0.041775789507399935\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488584,\n \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488584\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.037336266553835096,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.037336266553835096\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.27450980392156865,\n \"acc_stderr\": 0.044405219061793275,\n \"acc_norm\": 0.27450980392156865,\n \"acc_norm_stderr\": 0.044405219061793275\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3829787234042553,\n \"acc_stderr\": 0.03177821250236922,\n \"acc_norm\": 0.3829787234042553,\n \"acc_norm_stderr\": 0.03177821250236922\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.22807017543859648,\n \"acc_stderr\": 0.03947152782669415,\n \"acc_norm\": 0.22807017543859648,\n \"acc_norm_stderr\": 0.03947152782669415\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482757,\n \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482757\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2830687830687831,\n \"acc_stderr\": 0.023201392938194974,\n \"acc_norm\": 0.2830687830687831,\n \"acc_norm_stderr\": 0.023201392938194974\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2698412698412698,\n \"acc_stderr\": 0.03970158273235173,\n \"acc_norm\": 0.2698412698412698,\n \"acc_norm_stderr\": 0.03970158273235173\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5032258064516129,\n \"acc_stderr\": 0.028443414226438323,\n \"acc_norm\": 0.5032258064516129,\n \"acc_norm_stderr\": 0.028443414226438323\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.32019704433497537,\n \"acc_stderr\": 0.032826493853041504,\n \"acc_norm\": 0.32019704433497537,\n \"acc_norm_stderr\": 0.032826493853041504\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.5757575757575758,\n \"acc_stderr\": 0.03859268142070265,\n \"acc_norm\": 0.5757575757575758,\n \"acc_norm_stderr\": 0.03859268142070265\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5656565656565656,\n \"acc_stderr\": 0.03531505879359183,\n \"acc_norm\": 0.5656565656565656,\n \"acc_norm_stderr\": 0.03531505879359183\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6528497409326425,\n \"acc_stderr\": 0.03435696168361355,\n \"acc_norm\": 0.6528497409326425,\n \"acc_norm_stderr\": 0.03435696168361355\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4358974358974359,\n \"acc_stderr\": 0.02514180151117749,\n \"acc_norm\": 0.4358974358974359,\n \"acc_norm_stderr\": 0.02514180151117749\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.23703703703703705,\n \"acc_stderr\": 0.025928876132766114,\n \"acc_norm\": 0.23703703703703705,\n \"acc_norm_stderr\": 0.025928876132766114\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.39915966386554624,\n \"acc_stderr\": 0.03181110032413925,\n \"acc_norm\": 0.39915966386554624,\n \"acc_norm_stderr\": 0.03181110032413925\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.5486238532110091,\n \"acc_stderr\": 0.02133571471126879,\n \"acc_norm\": 0.5486238532110091,\n \"acc_norm_stderr\": 0.02133571471126879\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502326,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502326\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.03434131164719129,\n \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.03434131164719129\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5822784810126582,\n \"acc_stderr\": 0.032103530322412685,\n \"acc_norm\": 0.5822784810126582,\n \"acc_norm_stderr\": 0.032103530322412685\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5381165919282511,\n \"acc_stderr\": 0.033460150119732274,\n \"acc_norm\": 0.5381165919282511,\n \"acc_norm_stderr\": 0.033460150119732274\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.4961832061068702,\n \"acc_stderr\": 0.043851623256015534,\n \"acc_norm\": 0.4961832061068702,\n \"acc_norm_stderr\": 0.043851623256015534\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.5950413223140496,\n \"acc_stderr\": 0.04481137755942469,\n \"acc_norm\": 0.5950413223140496,\n \"acc_norm_stderr\": 0.04481137755942469\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5648148148148148,\n \"acc_stderr\": 0.04792898170907061,\n \"acc_norm\": 0.5648148148148148,\n \"acc_norm_stderr\": 0.04792898170907061\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.5153374233128835,\n \"acc_stderr\": 0.039265223787088445,\n \"acc_norm\": 0.5153374233128835,\n \"acc_norm_stderr\": 0.039265223787088445\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.26785714285714285,\n \"acc_stderr\": 0.04203277291467764,\n \"acc_norm\": 0.26785714285714285,\n \"acc_norm_stderr\": 0.04203277291467764\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.5631067961165048,\n \"acc_stderr\": 0.04911147107365777,\n \"acc_norm\": 0.5631067961165048,\n \"acc_norm_stderr\": 0.04911147107365777\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.688034188034188,\n \"acc_stderr\": 0.03035152732334493,\n \"acc_norm\": 0.688034188034188,\n \"acc_norm_stderr\": 0.03035152732334493\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6462324393358876,\n \"acc_stderr\": 0.017098184708161906,\n \"acc_norm\": 0.6462324393358876,\n \"acc_norm_stderr\": 0.017098184708161906\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.5173410404624278,\n \"acc_stderr\": 0.026902900458666647,\n \"acc_norm\": 0.5173410404624278,\n \"acc_norm_stderr\": 0.026902900458666647\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n \"acc_stderr\": 0.014355911964767864,\n \"acc_norm\": 0.2435754189944134,\n \"acc_norm_stderr\": 0.014355911964767864\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4673202614379085,\n \"acc_stderr\": 0.02856869975222587,\n \"acc_norm\": 0.4673202614379085,\n \"acc_norm_stderr\": 0.02856869975222587\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.49517684887459806,\n \"acc_stderr\": 0.028396770444111298,\n \"acc_norm\": 0.49517684887459806,\n \"acc_norm_stderr\": 0.028396770444111298\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5123456790123457,\n \"acc_stderr\": 0.027812262269327228,\n \"acc_norm\": 0.5123456790123457,\n \"acc_norm_stderr\": 0.027812262269327228\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.34397163120567376,\n \"acc_stderr\": 0.02833801742861132,\n \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.02833801742861132\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.34615384615384615,\n \"acc_stderr\": 0.012150699768228579,\n \"acc_norm\": 0.34615384615384615,\n \"acc_norm_stderr\": 0.012150699768228579\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4632352941176471,\n \"acc_stderr\": 0.030290619180485697,\n \"acc_norm\": 0.4632352941176471,\n \"acc_norm_stderr\": 0.030290619180485697\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.41830065359477125,\n \"acc_stderr\": 0.019955975145835542,\n \"acc_norm\": 0.41830065359477125,\n \"acc_norm_stderr\": 0.019955975145835542\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4909090909090909,\n \"acc_stderr\": 0.04788339768702861,\n \"acc_norm\": 0.4909090909090909,\n \"acc_norm_stderr\": 0.04788339768702861\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.4857142857142857,\n \"acc_stderr\": 0.03199615232806286,\n \"acc_norm\": 0.4857142857142857,\n \"acc_norm_stderr\": 0.03199615232806286\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6218905472636815,\n \"acc_stderr\": 0.034288678487786564,\n \"acc_norm\": 0.6218905472636815,\n \"acc_norm_stderr\": 0.034288678487786564\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.35542168674698793,\n \"acc_stderr\": 0.03726214354322416,\n \"acc_norm\": 0.35542168674698793,\n \"acc_norm_stderr\": 0.03726214354322416\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.6374269005847953,\n \"acc_stderr\": 0.0368713061556206,\n \"acc_norm\": 0.6374269005847953,\n \"acc_norm_stderr\": 0.0368713061556206\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.32558139534883723,\n \"mc1_stderr\": 0.01640398946990783,\n \"mc2\": 0.47497547233950527,\n \"mc2_stderr\": 0.01568331719502122\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7111286503551697,\n \"acc_stderr\": 0.012738241271018434\n },\n \"harness|drop|3\": {\n \"em\": 0.2236786912751678,\n \"em_stderr\": 0.004267491957607617,\n \"f1\": 0.2846486996644306,\n \"f1_stderr\": 0.00427403120655588\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06747536012130402,\n \"acc_stderr\": 0.006909475136357452\n }\n}\n```", "repo_url": "https://huggingface.co/ajibawa-2023/Uncensored-Jordan-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_18T19_37_27.743703", "path": ["**/details_harness|arc:challenge|25_2023-11-18T19-37-27.743703.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-18T19-37-27.743703.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_18T19_37_27.743703", "path": ["**/details_harness|drop|3_2023-11-18T19-37-27.743703.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-18T19-37-27.743703.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_18T19_37_27.743703", "path": ["**/details_harness|gsm8k|5_2023-11-18T19-37-27.743703.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-18T19-37-27.743703.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_18T19_37_27.743703", "path": ["**/details_harness|hellaswag|10_2023-11-18T19-37-27.743703.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-18T19-37-27.743703.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_18T19_37_27.743703", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-18T19-37-27.743703.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T19-37-27.743703.parquet", 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["**/details_harness|winogrande|5_2023-11-18T19-37-27.743703.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-18T19-37-27.743703.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T19_37_27.743703", "path": ["results_2023-11-18T19-37-27.743703.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T19-37-27.743703.parquet"]}]}]} | 2023-11-18T19:40:39+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Jordan-7B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-7B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T19:37:27.743703(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T19:37:27.743703(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Data Instances",
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"### Social Impact of Dataset",
"### Discussion of Biases",
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"## Additional Information",
"### Dataset Curators",
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]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T19:37:27.743703(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ajibawa-2023/Uncensored-Jordan-7B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ajibawa-2023/Uncensored-Jordan-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T19:37:27.743703(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
1ce17a4c682b556a5b49e15e56ae123e077a2a95 |
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-20b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-20b-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b-instruct_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T19:55:37.406086](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b-instruct_public/blob/main/results_2023-11-18T19-55-37.406086.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.320676822366553,
"acc_stderr": 0.03280575656070825,
"acc_norm": 0.3219577309602372,
"acc_norm_stderr": 0.03355424008379778,
"mc1": 0.25458996328029376,
"mc1_stderr": 0.015250117079156494,
"mc2": 0.4101922343540469,
"mc2_stderr": 0.014529149906569373,
"em": 0.014471476510067114,
"em_stderr": 0.0012230118709417176,
"f1": 0.051461828859060935,
"f1_stderr": 0.0016503207117057528
},
"harness|arc:challenge|25": {
"acc": 0.42150170648464164,
"acc_stderr": 0.014430197069326012,
"acc_norm": 0.431740614334471,
"acc_norm_stderr": 0.014474591427196207
},
"harness|hellaswag|10": {
"acc": 0.5312686715793666,
"acc_stderr": 0.004980014536539819,
"acc_norm": 0.7109141605257917,
"acc_norm_stderr": 0.004524113671259695
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.2565789473684211,
"acc_stderr": 0.0355418036802569,
"acc_norm": 0.2565789473684211,
"acc_norm_stderr": 0.0355418036802569
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252604,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252604
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3283018867924528,
"acc_stderr": 0.02890159361241178,
"acc_norm": 0.3283018867924528,
"acc_norm_stderr": 0.02890159361241178
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_stderr": 0.042295258468165044,
"acc_norm": 0.23,
"acc_norm_stderr": 0.042295258468165044
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2658959537572254,
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"acc_norm": 0.2658959537572254,
"acc_norm_stderr": 0.033687629322594316
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.16666666666666666,
"acc_stderr": 0.03708284662416544,
"acc_norm": 0.16666666666666666,
"acc_norm_stderr": 0.03708284662416544
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.34893617021276596,
"acc_stderr": 0.031158522131357783,
"acc_norm": 0.34893617021276596,
"acc_norm_stderr": 0.031158522131357783
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2719298245614035,
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"acc_norm_stderr": 0.04185774424022056
},
"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.02256989707491841
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.20634920634920634,
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},
"harness|hendrycksTest-global_facts|5": {
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},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.3161290322580645,
"acc_norm_stderr": 0.026450874489042774
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"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.31527093596059114,
"acc_norm_stderr": 0.03269080871970187
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"acc_norm": 0.29797979797979796,
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.33678756476683935,
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"acc_norm": 0.33678756476683935,
"acc_norm_stderr": 0.03410780251836184
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.26153846153846155,
"acc_stderr": 0.02228214120420442,
"acc_norm": 0.26153846153846155,
"acc_norm_stderr": 0.02228214120420442
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.02534809746809786
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"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.27310924369747897,
"acc_stderr": 0.028942004040998167,
"acc_norm": 0.27310924369747897,
"acc_norm_stderr": 0.028942004040998167
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"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2251655629139073,
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"acc_norm": 0.2251655629139073,
"acc_norm_stderr": 0.03410435282008936
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.3577981651376147,
"acc_stderr": 0.020552060784827818,
"acc_norm": 0.3577981651376147,
"acc_norm_stderr": 0.020552060784827818
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"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.18981481481481483,
"acc_stderr": 0.026744714834691936,
"acc_norm": 0.18981481481481483,
"acc_norm_stderr": 0.026744714834691936
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.35784313725490197,
"acc_stderr": 0.03364487286088299,
"acc_norm": 0.35784313725490197,
"acc_norm_stderr": 0.03364487286088299
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.37962962962962965,
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"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.294478527607362,
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"harness|hendrycksTest-machine_learning|5": {
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.37037037037037035,
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.34104046242774566,
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},
"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.27009646302250806,
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.32098765432098764,
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},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.2624113475177305,
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"acc_norm": 0.2624113475177305,
"acc_norm_stderr": 0.02624492034984302
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.29044117647058826,
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"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.32727272727272727,
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.2653061224489796,
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},
"harness|hendrycksTest-sociology|5": {
"acc": 0.34328358208955223,
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"acc_norm": 0.34328358208955223,
"acc_norm_stderr": 0.03357379665433431
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.42,
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"acc_norm": 0.42,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3373493975903614,
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"acc_norm": 0.3373493975903614,
"acc_norm_stderr": 0.03680783690727581
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.36257309941520466,
"acc_stderr": 0.036871306155620606,
"acc_norm": 0.36257309941520466,
"acc_norm_stderr": 0.036871306155620606
},
"harness|truthfulqa:mc|0": {
"mc1": 0.25458996328029376,
"mc1_stderr": 0.015250117079156494,
"mc2": 0.4101922343540469,
"mc2_stderr": 0.014529149906569373
},
"harness|winogrande|5": {
"acc": 0.6677190213101816,
"acc_stderr": 0.013238316554236525
},
"harness|drop|3": {
"em": 0.014471476510067114,
"em_stderr": 0.0012230118709417176,
"f1": 0.051461828859060935,
"f1_stderr": 0.0016503207117057528
},
"harness|gsm8k|5": {
"acc": 0.08794541319181198,
"acc_stderr": 0.007801162197487717
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b-instruct | [
"region:us"
]
| 2023-11-18T19:58:04+00:00 | {"pretty_name": "Evaluation run of AI-Sweden-Models/gpt-sw3-20b-instruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-20b-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b-instruct_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T19:55:37.406086](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b-instruct_public/blob/main/results_2023-11-18T19-55-37.406086.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.320676822366553,\n \"acc_stderr\": 0.03280575656070825,\n \"acc_norm\": 0.3219577309602372,\n \"acc_norm_stderr\": 0.03355424008379778,\n \"mc1\": 0.25458996328029376,\n \"mc1_stderr\": 0.015250117079156494,\n \"mc2\": 0.4101922343540469,\n \"mc2_stderr\": 0.014529149906569373,\n \"em\": 0.014471476510067114,\n \"em_stderr\": 0.0012230118709417176,\n \"f1\": 0.051461828859060935,\n \"f1_stderr\": 0.0016503207117057528\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.42150170648464164,\n \"acc_stderr\": 0.014430197069326012,\n \"acc_norm\": 0.431740614334471,\n \"acc_norm_stderr\": 0.014474591427196207\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5312686715793666,\n \"acc_stderr\": 0.004980014536539819,\n \"acc_norm\": 0.7109141605257917,\n \"acc_norm_stderr\": 0.004524113671259695\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-20b-instruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-20b-instruct on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T19:55:37.406086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"## Latest results\n\nThese are the latest results from run 2023-11-18T19:55:37.406086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-20b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-20b-instruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-20b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T19:55:37.406086(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
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|
e8db8d2eb9a30a5647fcda6aa0d5085bcc784f16 |
Attempt to dataset for LIMA fine-tuning on the top of the Phind model, which would result in:
- New system prompt that will preference for using docstring under each function, use multiple functions even if it doesn't make sense, and comment on every line of the code, it should also greatly reduce explanations before and after code block.
- As a result model will improve readability by Junior Python Developers and additionally do step-by-step reasoning by default to improve code & HumanEval results.
Shuffled 1050 rows:
300 rows - LIMA Python Tutor
200 rows - LeetCode submissions
250 rows from Airoboros coding/python
150 rows from Airoboros orca
150 rows from Airboros COT+TOM
Originally fine-tuning was supposed to be based on:
https://huggingface.co/datasets/KrisPi/PythonTutor-Evol-1k-DPO-GPT4_vs_35
However here I ended up:
1. Formatted output for the new System Prompt I wanted to introduce:
df['text'] = df.apply(lambda row: f"### System Prompt\nYou are an intelligent assistant. Act as expert Python tutor, thoroughly commenting the code.\n\n### User Message\n{row['instruction']}\n\n### Assistant\n{row['gpt4_output']}", axis=1)3. Sampled 300 largest rows under 1024 tokens limit (Llama tokenizer)
2. Formated LeetCode submissions in Phind-like prompt format
https://github.com/Nan-Do/LeetCodeContestsDataset/
#Replace the instruction placeholder with actual instruction
new_instruction = f"Using Python, solve following challenge: {entry['input']}"
#Format the output with the given template
text_prompt = f"### System Prompt\nYou are an intelligent assistant. Always wrap output between ```python and ```. Only code nothing else.\n\n### User Message\n{new_instruction}\n\n### Assistant\n```python\n{entry['output']}\n```"
3. Tokenized each, filtered where Tokens > 1024
4. Sampled 200 largest outputs from the remaining rows
5. In a similar way, I formatted Airoboros 2.2.1 Dataset:
https://huggingface.co/datasets/jondurbin/airoboros-2.2.1
250 Largest rows under 1024 tokens, where: Category = coding, "python" in instruction, removed "PLAINFORMAT", if ```python & ``` in response I removed everything else, if missing ``` then whole response got wrapped in ```python & ```
150 Largest rows under 1024 tokens, where: Category = orca
150 Random rows under 1024 tokens, where Category = cot or theory_of_mind
| KrisPi/PythonTutor-LIMA-Finetune | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-11-18T19:59:57+00:00 | {"license": "cc-by-nc-sa-4.0"} | 2023-11-18T20:03:24+00:00 | []
| []
| TAGS
#license-cc-by-nc-sa-4.0 #region-us
|
Attempt to dataset for LIMA fine-tuning on the top of the Phind model, which would result in:
- New system prompt that will preference for using docstring under each function, use multiple functions even if it doesn't make sense, and comment on every line of the code, it should also greatly reduce explanations before and after code block.
- As a result model will improve readability by Junior Python Developers and additionally do step-by-step reasoning by default to improve code & HumanEval results.
Shuffled 1050 rows:
300 rows - LIMA Python Tutor
200 rows - LeetCode submissions
250 rows from Airoboros coding/python
150 rows from Airoboros orca
150 rows from Airboros COT+TOM
Originally fine-tuning was supposed to be based on:
URL
However here I ended up:
1. Formatted output for the new System Prompt I wanted to introduce:
df['text'] = URL(lambda row: f"### System Prompt\nYou are an intelligent assistant. Act as expert Python tutor, thoroughly commenting the code.\n\n### User Message\n{row['instruction']}\n\n### Assistant\n{row['gpt4_output']}", axis=1)3. Sampled 300 largest rows under 1024 tokens limit (Llama tokenizer)
2. Formated LeetCode submissions in Phind-like prompt format
URL
#Replace the instruction placeholder with actual instruction
new_instruction = f"Using Python, solve following challenge: {entry['input']}"
#Format the output with the given template
text_prompt = f"### System Prompt\nYou are an intelligent assistant. Always wrap output between . Only code nothing else.\n\n### User Message\n{new_instruction}\n\n### Assistant\n"
3. Tokenized each, filtered where Tokens > 1024
4. Sampled 200 largest outputs from the remaining rows
5. In a similar way, I formatted Airoboros 2.2.1 Dataset:
URL
250 Largest rows under 1024 tokens, where: Category = coding, "python" in instruction, removed "PLAINFORMAT", if in response I removed everything else, if missing python & '''
150 Largest rows under 1024 tokens, where: Category = orca
150 Random rows under 1024 tokens, where Category = cot or theory_of_mind
| [
"### System Prompt\\nYou are an intelligent assistant. Act as expert Python tutor, thoroughly commenting the code.\\n\\n### User Message\\n{row['instruction']}\\n\\n### Assistant\\n{row['gpt4_output']}\", axis=1)3. Sampled 300 largest rows under 1024 tokens limit (Llama tokenizer)\n\n2. Formated LeetCode submissions in Phind-like prompt format\nURL",
"### System Prompt\\nYou are an intelligent assistant. Always wrap output between . Only code nothing else.\\n\\n### User Message\\n{new_instruction}\\n\\n### Assistant\\n\"\n\n3. Tokenized each, filtered where Tokens > 1024\n4. Sampled 200 largest outputs from the remaining rows\n\n5. In a similar way, I formatted Airoboros 2.2.1 Dataset:\nURL\n250 Largest rows under 1024 tokens, where: Category = coding, \"python\" in instruction, removed \"PLAINFORMAT\", if in response I removed everything else, if missing python & '''\n150 Largest rows under 1024 tokens, where: Category = orca\n150 Random rows under 1024 tokens, where Category = cot or theory_of_mind"
]
| [
"TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n",
"### System Prompt\\nYou are an intelligent assistant. Act as expert Python tutor, thoroughly commenting the code.\\n\\n### User Message\\n{row['instruction']}\\n\\n### Assistant\\n{row['gpt4_output']}\", axis=1)3. Sampled 300 largest rows under 1024 tokens limit (Llama tokenizer)\n\n2. Formated LeetCode submissions in Phind-like prompt format\nURL",
"### System Prompt\\nYou are an intelligent assistant. Always wrap output between . Only code nothing else.\\n\\n### User Message\\n{new_instruction}\\n\\n### Assistant\\n\"\n\n3. Tokenized each, filtered where Tokens > 1024\n4. Sampled 200 largest outputs from the remaining rows\n\n5. In a similar way, I formatted Airoboros 2.2.1 Dataset:\nURL\n250 Largest rows under 1024 tokens, where: Category = coding, \"python\" in instruction, removed \"PLAINFORMAT\", if in response I removed everything else, if missing python & '''\n150 Largest rows under 1024 tokens, where: Category = orca\n150 Random rows under 1024 tokens, where Category = cot or theory_of_mind"
]
| [
19,
109,
181
]
| [
"passage: TAGS\n#license-cc-by-nc-sa-4.0 #region-us \n### System Prompt\\nYou are an intelligent assistant. Act as expert Python tutor, thoroughly commenting the code.\\n\\n### User Message\\n{row['instruction']}\\n\\n### Assistant\\n{row['gpt4_output']}\", axis=1)3. Sampled 300 largest rows under 1024 tokens limit (Llama tokenizer)\n\n2. Formated LeetCode submissions in Phind-like prompt format\nURL### System Prompt\\nYou are an intelligent assistant. Always wrap output between . Only code nothing else.\\n\\n### User Message\\n{new_instruction}\\n\\n### Assistant\\n\"\n\n3. Tokenized each, filtered where Tokens > 1024\n4. Sampled 200 largest outputs from the remaining rows\n\n5. In a similar way, I formatted Airoboros 2.2.1 Dataset:\nURL\n250 Largest rows under 1024 tokens, where: Category = coding, \"python\" in instruction, removed \"PLAINFORMAT\", if in response I removed everything else, if missing python & '''\n150 Largest rows under 1024 tokens, where: Category = orca\n150 Random rows under 1024 tokens, where Category = cot or theory_of_mind"
]
|
54d7a697411e45d8668b518e5d5ee399eb56c914 |
# Dataset Card for Evaluation run of nnpy/Nape-0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nnpy/Nape-0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [nnpy/Nape-0](https://huggingface.co/nnpy/Nape-0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_nnpy__Nape-0_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T20:32:43.776654](https://huggingface.co/datasets/open-llm-leaderboard/details_nnpy__Nape-0_public/blob/main/results_2023-11-18T20-32-43.776654.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.2543128094432495,
"acc_stderr": 0.03062072612679358,
"acc_norm": 0.2558387672983551,
"acc_norm_stderr": 0.031412385111215566,
"mc1": 0.23745410036719705,
"mc1_stderr": 0.014896277441041836,
"mc2": 0.3899188336745711,
"mc2_stderr": 0.014581462986356814,
"em": 0.0017827181208053692,
"em_stderr": 0.0004320097346039005,
"f1": 0.03894714765100676,
"f1_stderr": 0.0011174860838397392
},
"harness|arc:challenge|25": {
"acc": 0.31143344709897613,
"acc_stderr": 0.013532472099850939,
"acc_norm": 0.3267918088737201,
"acc_norm_stderr": 0.013706665975587331
},
"harness|hellaswag|10": {
"acc": 0.4470225054769966,
"acc_stderr": 0.004961693567208816,
"acc_norm": 0.5868352917745469,
"acc_norm_stderr": 0.004913955705080124
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.04094376269996793,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.04094376269996793
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.034597776068105365,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.034597776068105365
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.2,
"acc_stderr": 0.040201512610368445,
"acc_norm": 0.2,
"acc_norm_stderr": 0.040201512610368445
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.26037735849056604,
"acc_stderr": 0.027008766090708097,
"acc_norm": 0.26037735849056604,
"acc_norm_stderr": 0.027008766090708097
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2152777777777778,
"acc_stderr": 0.03437079344106132,
"acc_norm": 0.2152777777777778,
"acc_norm_stderr": 0.03437079344106132
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536955,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536955
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.16,
"acc_stderr": 0.03684529491774709,
"acc_norm": 0.16,
"acc_norm_stderr": 0.03684529491774709
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.23121387283236994,
"acc_stderr": 0.03214737302029471,
"acc_norm": 0.23121387283236994,
"acc_norm_stderr": 0.03214737302029471
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.24509803921568626,
"acc_stderr": 0.04280105837364396,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.04280105837364396
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909282,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909282
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.251063829787234,
"acc_stderr": 0.028346963777162452,
"acc_norm": 0.251063829787234,
"acc_norm_stderr": 0.028346963777162452
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2719298245614035,
"acc_stderr": 0.041857744240220575,
"acc_norm": 0.2719298245614035,
"acc_norm_stderr": 0.041857744240220575
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2896551724137931,
"acc_stderr": 0.03780019230438014,
"acc_norm": 0.2896551724137931,
"acc_norm_stderr": 0.03780019230438014
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2671957671957672,
"acc_stderr": 0.02278967314577656,
"acc_norm": 0.2671957671957672,
"acc_norm_stderr": 0.02278967314577656
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.14285714285714285,
"acc_stderr": 0.0312984318574381,
"acc_norm": 0.14285714285714285,
"acc_norm_stderr": 0.0312984318574381
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.24838709677419354,
"acc_stderr": 0.02458002892148101,
"acc_norm": 0.24838709677419354,
"acc_norm_stderr": 0.02458002892148101
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2561576354679803,
"acc_stderr": 0.0307127300709826,
"acc_norm": 0.2561576354679803,
"acc_norm_stderr": 0.0307127300709826
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.296969696969697,
"acc_stderr": 0.035679697722680474,
"acc_norm": 0.296969696969697,
"acc_norm_stderr": 0.035679697722680474
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.25252525252525254,
"acc_stderr": 0.030954055470365904,
"acc_norm": 0.25252525252525254,
"acc_norm_stderr": 0.030954055470365904
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.22279792746113988,
"acc_stderr": 0.030031147977641545,
"acc_norm": 0.22279792746113988,
"acc_norm_stderr": 0.030031147977641545
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.23076923076923078,
"acc_stderr": 0.02136202772522271,
"acc_norm": 0.23076923076923078,
"acc_norm_stderr": 0.02136202772522271
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24814814814814815,
"acc_stderr": 0.0263357394040558,
"acc_norm": 0.24814814814814815,
"acc_norm_stderr": 0.0263357394040558
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.21428571428571427,
"acc_stderr": 0.026653531596715477,
"acc_norm": 0.21428571428571427,
"acc_norm_stderr": 0.026653531596715477
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.26490066225165565,
"acc_stderr": 0.036030385453603854,
"acc_norm": 0.26490066225165565,
"acc_norm_stderr": 0.036030385453603854
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.23119266055045873,
"acc_stderr": 0.01807575024163315,
"acc_norm": 0.23119266055045873,
"acc_norm_stderr": 0.01807575024163315
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.19444444444444445,
"acc_stderr": 0.026991454502036737,
"acc_norm": 0.19444444444444445,
"acc_norm_stderr": 0.026991454502036737
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.20098039215686275,
"acc_stderr": 0.02812597226565437,
"acc_norm": 0.20098039215686275,
"acc_norm_stderr": 0.02812597226565437
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.26582278481012656,
"acc_stderr": 0.02875679962965835,
"acc_norm": 0.26582278481012656,
"acc_norm_stderr": 0.02875679962965835
},
"harness|hendrycksTest-human_aging|5": {
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"acc_stderr": 0.03160295143776679,
"acc_norm": 0.33183856502242154,
"acc_norm_stderr": 0.03160295143776679
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.1984732824427481,
"acc_stderr": 0.034981493854624714,
"acc_norm": 0.1984732824427481,
"acc_norm_stderr": 0.034981493854624714
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.2809917355371901,
"acc_stderr": 0.04103203830514512,
"acc_norm": 0.2809917355371901,
"acc_norm_stderr": 0.04103203830514512
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.04236511258094633,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.04236511258094633
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.2883435582822086,
"acc_stderr": 0.035590395316173425,
"acc_norm": 0.2883435582822086,
"acc_norm_stderr": 0.035590395316173425
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2767857142857143,
"acc_stderr": 0.04246624336697624,
"acc_norm": 0.2767857142857143,
"acc_norm_stderr": 0.04246624336697624
},
"harness|hendrycksTest-management|5": {
"acc": 0.24271844660194175,
"acc_stderr": 0.04245022486384495,
"acc_norm": 0.24271844660194175,
"acc_norm_stderr": 0.04245022486384495
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.23076923076923078,
"acc_stderr": 0.027601921381417604,
"acc_norm": 0.23076923076923078,
"acc_norm_stderr": 0.027601921381417604
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.22,
"acc_stderr": 0.0416333199893227,
"acc_norm": 0.22,
"acc_norm_stderr": 0.0416333199893227
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.2669220945083014,
"acc_stderr": 0.015818450894777562,
"acc_norm": 0.2669220945083014,
"acc_norm_stderr": 0.015818450894777562
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.2658959537572254,
"acc_stderr": 0.023786203255508277,
"acc_norm": 0.2658959537572254,
"acc_norm_stderr": 0.023786203255508277
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.25921787709497207,
"acc_stderr": 0.014655780837497724,
"acc_norm": 0.25921787709497207,
"acc_norm_stderr": 0.014655780837497724
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.025829163272757482,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.025829163272757482
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.3022508038585209,
"acc_stderr": 0.02608270069539965,
"acc_norm": 0.3022508038585209,
"acc_norm_stderr": 0.02608270069539965
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.25925925925925924,
"acc_stderr": 0.02438366553103545,
"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.02438366553103545
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.23404255319148937,
"acc_stderr": 0.025257861359432414,
"acc_norm": 0.23404255319148937,
"acc_norm_stderr": 0.025257861359432414
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2379400260756193,
"acc_stderr": 0.010875700787694233,
"acc_norm": 0.2379400260756193,
"acc_norm_stderr": 0.010875700787694233
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.22794117647058823,
"acc_stderr": 0.025483081468029804,
"acc_norm": 0.22794117647058823,
"acc_norm_stderr": 0.025483081468029804
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.25326797385620914,
"acc_stderr": 0.017593486895366835,
"acc_norm": 0.25326797385620914,
"acc_norm_stderr": 0.017593486895366835
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.2545454545454545,
"acc_stderr": 0.04172343038705382,
"acc_norm": 0.2545454545454545,
"acc_norm_stderr": 0.04172343038705382
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.18775510204081633,
"acc_stderr": 0.025000256039546212,
"acc_norm": 0.18775510204081633,
"acc_norm_stderr": 0.025000256039546212
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.20398009950248755,
"acc_stderr": 0.02849317624532608,
"acc_norm": 0.20398009950248755,
"acc_norm_stderr": 0.02849317624532608
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-virology|5": {
"acc": 0.3132530120481928,
"acc_stderr": 0.036108050180310235,
"acc_norm": 0.3132530120481928,
"acc_norm_stderr": 0.036108050180310235
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.25146198830409355,
"acc_stderr": 0.033275044238468436,
"acc_norm": 0.25146198830409355,
"acc_norm_stderr": 0.033275044238468436
},
"harness|truthfulqa:mc|0": {
"mc1": 0.23745410036719705,
"mc1_stderr": 0.014896277441041836,
"mc2": 0.3899188336745711,
"mc2_stderr": 0.014581462986356814
},
"harness|winogrande|5": {
"acc": 0.5730071033938438,
"acc_stderr": 0.013901878072575055
},
"harness|drop|3": {
"em": 0.0017827181208053692,
"em_stderr": 0.0004320097346039005,
"f1": 0.03894714765100676,
"f1_stderr": 0.0011174860838397392
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225202
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_nnpy__Nape-0 | [
"region:us"
]
| 2023-11-18T20:35:14+00:00 | {"pretty_name": "Evaluation run of nnpy/Nape-0", "dataset_summary": "Dataset automatically created during the evaluation run of model [nnpy/Nape-0](https://huggingface.co/nnpy/Nape-0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_nnpy__Nape-0_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T20:32:43.776654](https://huggingface.co/datasets/open-llm-leaderboard/details_nnpy__Nape-0_public/blob/main/results_2023-11-18T20-32-43.776654.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2543128094432495,\n \"acc_stderr\": 0.03062072612679358,\n \"acc_norm\": 0.2558387672983551,\n \"acc_norm_stderr\": 0.031412385111215566,\n \"mc1\": 0.23745410036719705,\n \"mc1_stderr\": 0.014896277441041836,\n \"mc2\": 0.3899188336745711,\n \"mc2_stderr\": 0.014581462986356814,\n \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.0004320097346039005,\n \"f1\": 0.03894714765100676,\n \"f1_stderr\": 0.0011174860838397392\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.31143344709897613,\n \"acc_stderr\": 0.013532472099850939,\n \"acc_norm\": 0.3267918088737201,\n \"acc_norm_stderr\": 0.013706665975587331\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4470225054769966,\n \"acc_stderr\": 0.004961693567208816,\n \"acc_norm\": 0.5868352917745469,\n \"acc_norm_stderr\": 0.004913955705080124\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.04094376269996793,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.04094376269996793\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.034597776068105365,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.034597776068105365\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.040201512610368445\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.26037735849056604,\n \"acc_stderr\": 0.027008766090708097,\n \"acc_norm\": 0.26037735849056604,\n \"acc_norm_stderr\": 0.027008766090708097\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2152777777777778,\n \"acc_stderr\": 0.03437079344106132,\n \"acc_norm\": 0.2152777777777778,\n \"acc_norm_stderr\": 0.03437079344106132\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.16,\n \"acc_stderr\": 0.03684529491774709,\n \"acc_norm\": 0.16,\n \"acc_norm_stderr\": 0.03684529491774709\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.23121387283236994,\n \"acc_stderr\": 0.03214737302029471,\n \"acc_norm\": 0.23121387283236994,\n \"acc_norm_stderr\": 0.03214737302029471\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364396,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364396\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.251063829787234,\n \"acc_stderr\": 0.028346963777162452,\n \"acc_norm\": 0.251063829787234,\n \"acc_norm_stderr\": 0.028346963777162452\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2719298245614035,\n \"acc_stderr\": 0.041857744240220575,\n \"acc_norm\": 0.2719298245614035,\n \"acc_norm_stderr\": 0.041857744240220575\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2896551724137931,\n \"acc_stderr\": 0.03780019230438014,\n \"acc_norm\": 0.2896551724137931,\n \"acc_norm_stderr\": 0.03780019230438014\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2671957671957672,\n \"acc_stderr\": 0.02278967314577656,\n \"acc_norm\": 0.2671957671957672,\n \"acc_norm_stderr\": 0.02278967314577656\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.14285714285714285,\n \"acc_stderr\": 0.0312984318574381,\n \"acc_norm\": 0.14285714285714285,\n \"acc_norm_stderr\": 0.0312984318574381\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24838709677419354,\n \"acc_stderr\": 0.02458002892148101,\n \"acc_norm\": 0.24838709677419354,\n \"acc_norm_stderr\": 0.02458002892148101\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.0307127300709826,\n \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.0307127300709826\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.296969696969697,\n \"acc_stderr\": 0.035679697722680474,\n \"acc_norm\": 0.296969696969697,\n \"acc_norm_stderr\": 0.035679697722680474\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.25252525252525254,\n \"acc_stderr\": 0.030954055470365904,\n \"acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.030954055470365904\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.22279792746113988,\n \"acc_stderr\": 0.030031147977641545,\n \"acc_norm\": 0.22279792746113988,\n \"acc_norm_stderr\": 0.030031147977641545\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.23076923076923078,\n \"acc_stderr\": 0.02136202772522271,\n \"acc_norm\": 0.23076923076923078,\n \"acc_norm_stderr\": 0.02136202772522271\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24814814814814815,\n \"acc_stderr\": 0.0263357394040558,\n \"acc_norm\": 0.24814814814814815,\n \"acc_norm_stderr\": 0.0263357394040558\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.21428571428571427,\n \"acc_stderr\": 0.026653531596715477,\n \"acc_norm\": 0.21428571428571427,\n \"acc_norm_stderr\": 0.026653531596715477\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.26490066225165565,\n \"acc_stderr\": 0.036030385453603854,\n \"acc_norm\": 0.26490066225165565,\n \"acc_norm_stderr\": 0.036030385453603854\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.23119266055045873,\n \"acc_stderr\": 0.01807575024163315,\n \"acc_norm\": 0.23119266055045873,\n \"acc_norm_stderr\": 0.01807575024163315\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.19444444444444445,\n \"acc_stderr\": 0.026991454502036737,\n \"acc_norm\": 0.19444444444444445,\n \"acc_norm_stderr\": 0.026991454502036737\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.20098039215686275,\n \"acc_stderr\": 0.02812597226565437,\n \"acc_norm\": 0.20098039215686275,\n \"acc_norm_stderr\": 0.02812597226565437\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.26582278481012656,\n \"acc_stderr\": 0.02875679962965835,\n \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.02875679962965835\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.33183856502242154,\n \"acc_stderr\": 0.03160295143776679,\n \"acc_norm\": 0.33183856502242154,\n \"acc_norm_stderr\": 0.03160295143776679\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.1984732824427481,\n \"acc_stderr\": 0.034981493854624714,\n \"acc_norm\": 0.1984732824427481,\n \"acc_norm_stderr\": 0.034981493854624714\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.2809917355371901,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\": 0.2809917355371901,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2883435582822086,\n \"acc_stderr\": 0.035590395316173425,\n \"acc_norm\": 0.2883435582822086,\n \"acc_norm_stderr\": 0.035590395316173425\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2767857142857143,\n \"acc_stderr\": 0.04246624336697624,\n \"acc_norm\": 0.2767857142857143,\n \"acc_norm_stderr\": 0.04246624336697624\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.24271844660194175,\n \"acc_stderr\": 0.04245022486384495,\n \"acc_norm\": 0.24271844660194175,\n \"acc_norm_stderr\": 0.04245022486384495\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23076923076923078,\n \"acc_stderr\": 0.027601921381417604,\n \"acc_norm\": 0.23076923076923078,\n \"acc_norm_stderr\": 0.027601921381417604\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.0416333199893227,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.0416333199893227\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2669220945083014,\n \"acc_stderr\": 0.015818450894777562,\n \"acc_norm\": 0.2669220945083014,\n \"acc_norm_stderr\": 0.015818450894777562\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2658959537572254,\n \"acc_stderr\": 0.023786203255508277,\n \"acc_norm\": 0.2658959537572254,\n \"acc_norm_stderr\": 0.023786203255508277\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.25921787709497207,\n \"acc_stderr\": 0.014655780837497724,\n \"acc_norm\": 0.25921787709497207,\n \"acc_norm_stderr\": 0.014655780837497724\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.025829163272757482,\n \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.025829163272757482\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3022508038585209,\n \"acc_stderr\": 0.02608270069539965,\n \"acc_norm\": 0.3022508038585209,\n \"acc_norm_stderr\": 0.02608270069539965\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.02438366553103545,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.02438366553103545\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432414,\n \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432414\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2379400260756193,\n \"acc_stderr\": 0.010875700787694233,\n \"acc_norm\": 0.2379400260756193,\n \"acc_norm_stderr\": 0.010875700787694233\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.22794117647058823,\n \"acc_stderr\": 0.025483081468029804,\n \"acc_norm\": 0.22794117647058823,\n \"acc_norm_stderr\": 0.025483081468029804\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.25326797385620914,\n \"acc_stderr\": 0.017593486895366835,\n \"acc_norm\": 0.25326797385620914,\n \"acc_norm_stderr\": 0.017593486895366835\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.04172343038705382,\n \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.04172343038705382\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n \"acc_stderr\": 0.025000256039546212,\n \"acc_norm\": 0.18775510204081633,\n \"acc_norm_stderr\": 0.025000256039546212\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.20398009950248755,\n \"acc_stderr\": 0.02849317624532608,\n \"acc_norm\": 0.20398009950248755,\n \"acc_norm_stderr\": 0.02849317624532608\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3132530120481928,\n \"acc_stderr\": 0.036108050180310235,\n \"acc_norm\": 0.3132530120481928,\n \"acc_norm_stderr\": 0.036108050180310235\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.25146198830409355,\n \"acc_stderr\": 0.033275044238468436,\n \"acc_norm\": 0.25146198830409355,\n \"acc_norm_stderr\": 0.033275044238468436\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23745410036719705,\n \"mc1_stderr\": 0.014896277441041836,\n \"mc2\": 0.3899188336745711,\n \"mc2_stderr\": 0.014581462986356814\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5730071033938438,\n \"acc_stderr\": 0.013901878072575055\n },\n \"harness|drop|3\": {\n \"em\": 0.0017827181208053692,\n \"em_stderr\": 0.0004320097346039005,\n \"f1\": 0.03894714765100676,\n \"f1_stderr\": 0.0011174860838397392\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225202\n }\n}\n```", "repo_url": "https://huggingface.co/nnpy/Nape-0", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|arc:challenge|25_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|drop|3_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|gsm8k|5_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|hellaswag|10_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-18T20-32-43.776654.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T20-32-43.776654.parquet", 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{"config_name": "harness_hendrycksTest_jurisprudence_5", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-jurisprudence|5_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "harness_hendrycksTest_logical_fallacies_5", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "harness_hendrycksTest_machine_learning_5", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": 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["**/details_harness|winogrande|5_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-18T20-32-43.776654.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T20_32_43.776654", "path": ["results_2023-11-18T20-32-43.776654.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T20-32-43.776654.parquet"]}]}]} | 2023-11-18T20:36:01+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of nnpy/Nape-0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model nnpy/Nape-0 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T20:32:43.776654(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model nnpy/Nape-0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T20:32:43.776654(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model nnpy/Nape-0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T20:32:43.776654(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of nnpy/Nape-0## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model nnpy/Nape-0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T20:32:43.776654(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
973fff81da52ad423f460dc5ec81c58f34f2353f |
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T21:04:21.939404](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public/blob/main/results_2023-11-18T21-04-21.939404.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.32058974654497724,
"acc_stderr": 0.03287256745618845,
"acc_norm": 0.3233939935906761,
"acc_norm_stderr": 0.03364411678813401,
"mc1": 0.26193390452876375,
"mc1_stderr": 0.015392118805015023,
"mc2": 0.4032485125499964,
"mc2_stderr": 0.014292284301112663,
"em": 0.22766359060402686,
"em_stderr": 0.004294273453162853,
"f1": 0.266680998322148,
"f1_stderr": 0.00428696034436648
},
"harness|arc:challenge|25": {
"acc": 0.3575085324232082,
"acc_stderr": 0.014005494275916576,
"acc_norm": 0.40784982935153585,
"acc_norm_stderr": 0.014361097288449707
},
"harness|hellaswag|10": {
"acc": 0.5046803425612428,
"acc_stderr": 0.004989562798280523,
"acc_norm": 0.6776538538139812,
"acc_norm_stderr": 0.004664195159393912
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct | [
"region:us"
]
| 2023-11-18T21:06:43+00:00 | {"pretty_name": "Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T21:04:21.939404](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2-instruct_public/blob/main/results_2023-11-18T21-04-21.939404.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.32058974654497724,\n \"acc_stderr\": 0.03287256745618845,\n \"acc_norm\": 0.3233939935906761,\n \"acc_norm_stderr\": 0.03364411678813401,\n \"mc1\": 0.26193390452876375,\n \"mc1_stderr\": 0.015392118805015023,\n \"mc2\": 0.4032485125499964,\n \"mc2_stderr\": 0.014292284301112663,\n \"em\": 0.22766359060402686,\n \"em_stderr\": 0.004294273453162853,\n \"f1\": 0.266680998322148,\n \"f1_stderr\": 0.00428696034436648\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.3575085324232082,\n \"acc_stderr\": 0.014005494275916576,\n \"acc_norm\": 0.40784982935153585,\n \"acc_norm_stderr\": 0.014361097288449707\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5046803425612428,\n \"acc_stderr\": 0.004989562798280523,\n \"acc_norm\": 0.6776538538139812,\n \"acc_norm_stderr\": 0.004664195159393912\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.362962962962963,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.362962962962963,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.32894736842105265,\n \"acc_stderr\": 0.03823428969926604,\n \"acc_norm\": 0.32894736842105265,\n \"acc_norm_stderr\": 0.03823428969926604\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.33962264150943394,\n \"acc_stderr\": 0.029146904747798335,\n \"acc_norm\": 0.33962264150943394,\n \"acc_norm_stderr\": 0.029146904747798335\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3680555555555556,\n \"acc_stderr\": 0.04032999053960718,\n \"acc_norm\": 0.3680555555555556,\n \"acc_norm_stderr\": 0.04032999053960718\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847415,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847415\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3352601156069364,\n \"acc_stderr\": 0.03599586301247078,\n \"acc_norm\": 0.3352601156069364,\n \"acc_norm_stderr\": 0.03599586301247078\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.0433643270799318,\n \"acc_norm\": 0.2549019607843137,\n 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["**/details_harness|hendrycksTest-machine_learning|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_management_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-management|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-management|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_marketing_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-marketing|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-marketing|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_medical_genetics_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-medical_genetics|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_miscellaneous_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-miscellaneous|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_disputes_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_disputes|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_moral_scenarios_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_nutrition_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-nutrition|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_philosophy_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-philosophy|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_prehistory_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-prehistory|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["**/details_harness|winogrande|5_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-18T21-04-21.939404.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T21_04_21.939404", "path": ["results_2023-11-18T21-04-21.939404.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T21-04-21.939404.parquet"]}]}]} | 2023-11-18T21:07:33+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T21:04:21.939404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T21:04:21.939404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
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"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
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]
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T21:04:21.939404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
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"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T21:04:21.939404(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
a7888d2ade6419b42b9937f5bc4b847be8279b21 |
# Dataset Card for Evaluation run of vihangd/shearedplats-1.3b-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/vihangd/shearedplats-1.3b-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [vihangd/shearedplats-1.3b-v1](https://huggingface.co/vihangd/shearedplats-1.3b-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_vihangd__shearedplats-1.3b-v1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T21:27:03.574383](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__shearedplats-1.3b-v1_public/blob/main/results_2023-11-18T21-27-03.574383.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.253847590681609,
"acc_stderr": 0.030523099331108815,
"acc_norm": 0.25573186704882195,
"acc_norm_stderr": 0.031292815233613276,
"mc1": 0.204406364749082,
"mc1_stderr": 0.014117174337432618,
"mc2": 0.3392533208873607,
"mc2_stderr": 0.014078645743359227,
"em": 0.003355704697986577,
"em_stderr": 0.0005922452850005238,
"f1": 0.0555180369127516,
"f1_stderr": 0.0013765753121727882
},
"harness|arc:challenge|25": {
"acc": 0.3174061433447099,
"acc_stderr": 0.013602239088038173,
"acc_norm": 0.35409556313993173,
"acc_norm_stderr": 0.013975454122756557
},
"harness|hellaswag|10": {
"acc": 0.4705238000398327,
"acc_stderr": 0.0049811031579404495,
"acc_norm": 0.6274646484763992,
"acc_norm_stderr": 0.004824917516374187
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.04408440022768081,
"acc_norm": 0.26,
"acc_norm_stderr": 0.04408440022768081
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03591444084196969,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03591444084196969
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3223684210526316,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.3223684210526316,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2528301886792453,
"acc_stderr": 0.026749899771241238,
"acc_norm": 0.2528301886792453,
"acc_norm_stderr": 0.026749899771241238
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.03476590104304134,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.15,
"acc_stderr": 0.035887028128263714,
"acc_norm": 0.15,
"acc_norm_stderr": 0.035887028128263714
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909281,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909281
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.24855491329479767,
"acc_stderr": 0.03295304696818318,
"acc_norm": 0.24855491329479767,
"acc_norm_stderr": 0.03295304696818318
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.04389869956808778,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.04389869956808778
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384739,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384739
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3276595744680851,
"acc_stderr": 0.030683020843231004,
"acc_norm": 0.3276595744680851,
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_vihangd__shearedplats-1.3b-v1 | [
"region:us"
]
| 2023-11-18T21:30:07+00:00 | {"pretty_name": "Evaluation run of vihangd/shearedplats-1.3b-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [vihangd/shearedplats-1.3b-v1](https://huggingface.co/vihangd/shearedplats-1.3b-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_vihangd__shearedplats-1.3b-v1_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T21:27:03.574383](https://huggingface.co/datasets/open-llm-leaderboard/details_vihangd__shearedplats-1.3b-v1_public/blob/main/results_2023-11-18T21-27-03.574383.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.253847590681609,\n \"acc_stderr\": 0.030523099331108815,\n \"acc_norm\": 0.25573186704882195,\n \"acc_norm_stderr\": 0.031292815233613276,\n \"mc1\": 0.204406364749082,\n \"mc1_stderr\": 0.014117174337432618,\n \"mc2\": 0.3392533208873607,\n \"mc2_stderr\": 0.014078645743359227,\n \"em\": 0.003355704697986577,\n \"em_stderr\": 0.0005922452850005238,\n \"f1\": 0.0555180369127516,\n \"f1_stderr\": 0.0013765753121727882\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.3174061433447099,\n \"acc_stderr\": 0.013602239088038173,\n \"acc_norm\": 0.35409556313993173,\n \"acc_norm_stderr\": 0.013975454122756557\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4705238000398327,\n \"acc_stderr\": 0.0049811031579404495,\n \"acc_norm\": 0.6274646484763992,\n \"acc_norm_stderr\": 0.004824917516374187\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03591444084196969,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03591444084196969\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.3223684210526316,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.3223684210526316,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.026749899771241238,\n \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.026749899771241238\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.15,\n \"acc_stderr\": 0.035887028128263714,\n \"acc_norm\": 0.15,\n \"acc_norm_stderr\": 0.035887028128263714\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909281,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909281\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3276595744680851,\n \"acc_stderr\": 0.030683020843231004,\n \"acc_norm\": 0.3276595744680851,\n \"acc_norm_stderr\": 0.030683020843231004\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21929824561403508,\n \"acc_stderr\": 0.03892431106518754,\n \"acc_norm\": 0.21929824561403508,\n \"acc_norm_stderr\": 0.03892431106518754\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.23448275862068965,\n \"acc_stderr\": 0.035306258743465914,\n \"acc_norm\": 0.23448275862068965,\n \"acc_norm_stderr\": 0.035306258743465914\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.022569897074918417,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.022569897074918417\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.15873015873015872,\n \"acc_stderr\": 0.03268454013011744,\n \"acc_norm\": 0.15873015873015872,\n \"acc_norm_stderr\": 0.03268454013011744\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.27419354838709675,\n \"acc_stderr\": 0.025378139970885196,\n \"acc_norm\": 0.27419354838709675,\n \"acc_norm_stderr\": 0.025378139970885196\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.270935960591133,\n \"acc_stderr\": 0.031270907132976984,\n \"acc_norm\": 0.270935960591133,\n \"acc_norm_stderr\": 0.031270907132976984\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\": 0.22,\n 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["**/details_harness|winogrande|5_2023-11-18T21-27-03.574383.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-18T21-27-03.574383.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T21_27_03.574383", "path": ["results_2023-11-18T21-27-03.574383.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T21-27-03.574383.parquet"]}]}]} | 2023-11-18T21:30:55+00:00 | []
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# Dataset Card for Evaluation run of vihangd/shearedplats-1.3b-v1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model vihangd/shearedplats-1.3b-v1 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T21:27:03.574383(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
62e477479e5903e95ab49fe9f1b8c970d53dc8a4 | # Dataset Card for "openai_summarize_unlabelled"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arianhosseini/openai_summarize_unlabelled | [
"region:us"
]
| 2023-11-18T21:33:58+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 167037359, "num_examples": 107543}], "download_size": 101979854, "dataset_size": 167037359}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-18T21:34:06+00:00 | []
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#region-us
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More Information needed | [
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2033415894b74bab601bd22eb5897732ea6a9984 | # Dataset Card for "midascontrolour"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dputilov/midascontrolour | [
"region:us"
]
| 2023-11-18T21:50:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "mask", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4893199529.264, "num_examples": 8488}], "download_size": 4880098057, "dataset_size": 4893199529.264}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-18T21:54:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "midascontrolour"
More Information needed | [
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|
35b6ad55a059bfc33ec4da2df2a141e2c91dded4 |
# Dataset Card for Evaluation run of ehartford/dolphin-2.2.1-mistral-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [ehartford/dolphin-2.2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ehartford__dolphin-2.2.1-mistral-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T17:18:36.579196](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.2.1-mistral-7b/blob/main/results_2023-12-04T17-18-36.579196.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6314567324183159,
"acc_stderr": 0.032318316802746,
"acc_norm": 0.6352434028495076,
"acc_norm_stderr": 0.032961647633460475,
"mc1": 0.3659730722154223,
"mc1_stderr": 0.016862941684088365,
"mc2": 0.5311447373702662,
"mc2_stderr": 0.015062742496541512
},
"harness|arc:challenge|25": {
"acc": 0.6049488054607508,
"acc_stderr": 0.014285898292938167,
"acc_norm": 0.6331058020477816,
"acc_norm_stderr": 0.014084133118104301
},
"harness|hellaswag|10": {
"acc": 0.6431985660227046,
"acc_stderr": 0.004780764443411322,
"acc_norm": 0.8375821549492133,
"acc_norm_stderr": 0.0036807989505319113
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.04218506215368879,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.04218506215368879
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.03803510248351585,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.03803510248351585
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880267,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880267
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416907,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416907
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.04755129616062947,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.04755129616062947
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5446808510638298,
"acc_stderr": 0.03255525359340354,
"acc_norm": 0.5446808510638298,
"acc_norm_stderr": 0.03255525359340354
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.593103448275862,
"acc_stderr": 0.04093793981266236,
"acc_norm": 0.593103448275862,
"acc_norm_stderr": 0.04093793981266236
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3862433862433862,
"acc_stderr": 0.025075981767601684,
"acc_norm": 0.3862433862433862,
"acc_norm_stderr": 0.025075981767601684
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3968253968253968,
"acc_stderr": 0.04375888492727062,
"acc_norm": 0.3968253968253968,
"acc_norm_stderr": 0.04375888492727062
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
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"harness|gsm8k|5": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_ehartford__dolphin-2.2.1-mistral-7b | [
"region:us"
]
| 2023-11-18T21:56:51+00:00 | {"pretty_name": "Evaluation run of ehartford/dolphin-2.2.1-mistral-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [ehartford/dolphin-2.2.1-mistral-7b](https://huggingface.co/ehartford/dolphin-2.2.1-mistral-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ehartford__dolphin-2.2.1-mistral-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T17:18:36.579196](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__dolphin-2.2.1-mistral-7b/blob/main/results_2023-12-04T17-18-36.579196.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6314567324183159,\n \"acc_stderr\": 0.032318316802746,\n \"acc_norm\": 0.6352434028495076,\n \"acc_norm_stderr\": 0.032961647633460475,\n \"mc1\": 0.3659730722154223,\n \"mc1_stderr\": 0.016862941684088365,\n \"mc2\": 0.5311447373702662,\n \"mc2_stderr\": 0.015062742496541512\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6049488054607508,\n \"acc_stderr\": 0.014285898292938167,\n \"acc_norm\": 0.6331058020477816,\n \"acc_norm_stderr\": 0.014084133118104301\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6431985660227046,\n \"acc_stderr\": 0.004780764443411322,\n \"acc_norm\": 0.8375821549492133,\n \"acc_norm_stderr\": 0.0036807989505319113\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.04218506215368879,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.04218506215368879\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.03803510248351585,\n \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.03803510248351585\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062947,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062947\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n 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"latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-12-04T17-18-36.579196.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_18T21_53_53.398955", "path": ["**/details_harness|winogrande|5_2023-11-18T21-53-53.398955.parquet"]}, {"split": "2023_12_04T17_18_36.579196", "path": ["**/details_harness|winogrande|5_2023-12-04T17-18-36.579196.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-12-04T17-18-36.579196.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T21_53_53.398955", "path": ["results_2023-11-18T21-53-53.398955.parquet"]}, {"split": "2023_12_04T17_18_36.579196", "path": ["results_2023-12-04T17-18-36.579196.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T17-18-36.579196.parquet"]}]}]} | 2023-12-04T17:22:13+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of ehartford/dolphin-2.2.1-mistral-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ehartford/dolphin-2.2.1-mistral-7b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-04T17:18:36.579196(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of ehartford/dolphin-2.2.1-mistral-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ehartford/dolphin-2.2.1-mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T17:18:36.579196(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model ehartford/dolphin-2.2.1-mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T17:18:36.579196(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of ehartford/dolphin-2.2.1-mistral-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model ehartford/dolphin-2.2.1-mistral-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-04T17:18:36.579196(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
9f66cf9db016708c44c7c5bf199b005840062645 |
# Dataset Card for Evaluation run of migtissera/Tess-XS-v1.0
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Tess-XS-v1.0
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [migtissera/Tess-XS-v1.0](https://huggingface.co/migtissera/Tess-XS-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_migtissera__Tess-XS-v1.0_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T21:55:23.260774](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-XS-v1.0_public/blob/main/results_2023-11-18T21-55-23.260774.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6348258267763893,
"acc_stderr": 0.03230372610827704,
"acc_norm": 0.6439271893072561,
"acc_norm_stderr": 0.03300134321723649,
"mc1": 0.3157894736842105,
"mc1_stderr": 0.016272287957916916,
"mc2": 0.4712323822712203,
"mc2_stderr": 0.014554223298121486,
"em": 0.0018875838926174498,
"em_stderr": 0.0004445109990558992,
"f1": 0.061799496644295286,
"f1_stderr": 0.0013795660027086077
},
"harness|arc:challenge|25": {
"acc": 0.5750853242320819,
"acc_stderr": 0.014445698968520769,
"acc_norm": 0.6143344709897611,
"acc_norm_stderr": 0.014224250973257182
},
"harness|hellaswag|10": {
"acc": 0.6381198964349731,
"acc_stderr": 0.00479562275732714,
"acc_norm": 0.8381796454889464,
"acc_norm_stderr": 0.003675332590681066
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6644736842105263,
"acc_stderr": 0.03842498559395268,
"acc_norm": 0.6644736842105263,
"acc_norm_stderr": 0.03842498559395268
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
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},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-college_physics|5": {
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},
"harness|hendrycksTest-computer_security|5": {
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"harness|hendrycksTest-conceptual_physics|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"acc_norm_stderr": 0.026160584450140453
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm_stderr": 0.02465968518596728
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"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.5212765957446809,
"acc_norm_stderr": 0.029800481645628693
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"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.4485006518904824,
"acc_norm_stderr": 0.012702317490559807
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"acc_norm_stderr": 0.02783302387139968
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.02553843336857833
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"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.84,
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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"harness|drop|3": {
"em": 0.0018875838926174498,
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"f1": 0.061799496644295286,
"f1_stderr": 0.0013795660027086077
},
"harness|gsm8k|5": {
"acc": 0.18271417740712662,
"acc_stderr": 0.010644258206326236
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_migtissera__Tess-XS-v1.0 | [
"region:us"
]
| 2023-11-18T21:58:26+00:00 | {"pretty_name": "Evaluation run of migtissera/Tess-XS-v1.0", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/Tess-XS-v1.0](https://huggingface.co/migtissera/Tess-XS-v1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_migtissera__Tess-XS-v1.0_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T21:55:23.260774](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-XS-v1.0_public/blob/main/results_2023-11-18T21-55-23.260774.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6348258267763893,\n \"acc_stderr\": 0.03230372610827704,\n \"acc_norm\": 0.6439271893072561,\n \"acc_norm_stderr\": 0.03300134321723649,\n \"mc1\": 0.3157894736842105,\n \"mc1_stderr\": 0.016272287957916916,\n \"mc2\": 0.4712323822712203,\n \"mc2_stderr\": 0.014554223298121486,\n \"em\": 0.0018875838926174498,\n \"em_stderr\": 0.0004445109990558992,\n \"f1\": 0.061799496644295286,\n \"f1_stderr\": 0.0013795660027086077\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520769,\n \"acc_norm\": 0.6143344709897611,\n \"acc_norm_stderr\": 0.014224250973257182\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6381198964349731,\n \"acc_stderr\": 0.00479562275732714,\n \"acc_norm\": 0.8381796454889464,\n \"acc_norm_stderr\": 0.003675332590681066\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.03842498559395268,\n \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.03842498559395268\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7013888888888888,\n \"acc_stderr\": 0.03827052357950756,\n \"acc_norm\": 0.7013888888888888,\n \"acc_norm_stderr\": 0.03827052357950756\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416906\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5829787234042553,\n \"acc_stderr\": 0.03223276266711712,\n \"acc_norm\": 0.5829787234042553,\n \"acc_norm_stderr\": 0.03223276266711712\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.04697085136647863,\n \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.04697085136647863\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3994708994708995,\n \"acc_stderr\": 0.02522545028406788,\n \"acc_norm\": 0.3994708994708995,\n \"acc_norm_stderr\": 0.02522545028406788\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.043758884927270605,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.043758884927270605\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7677419354838709,\n \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.035179450386910616,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.035179450386910616\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8860103626943006,\n \"acc_stderr\": 0.022935144053919443,\n \"acc_norm\": 0.8860103626943006,\n \"acc_norm_stderr\": 0.022935144053919443\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635477,\n \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635477\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616258,\n \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616258\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.030388353551886793,\n \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.030388353551886793\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8275229357798165,\n \"acc_stderr\": 0.01619780795684805,\n \"acc_norm\": 0.8275229357798165,\n \"acc_norm_stderr\": 0.01619780795684805\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5324074074074074,\n \"acc_stderr\": 0.03402801581358966,\n \"acc_norm\": 0.5324074074074074,\n \"acc_norm_stderr\": 0.03402801581358966\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8137254901960784,\n \"acc_stderr\": 0.027325470966716312,\n \"acc_norm\": 0.8137254901960784,\n \"acc_norm_stderr\": 0.027325470966716312\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069425,\n \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069425\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.8098159509202454,\n \"acc_stderr\": 0.030833491146281235,\n \"acc_norm\": 0.8098159509202454,\n \"acc_norm_stderr\": 0.030833491146281235\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822585,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822585\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n \"acc_stderr\": 0.013816335389973136,\n \"acc_norm\": 0.8173690932311622,\n \"acc_norm_stderr\": 0.013816335389973136\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.024257901705323374,\n \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.024257901705323374\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33519553072625696,\n \"acc_stderr\": 0.015788007190185884,\n \"acc_norm\": 0.33519553072625696,\n \"acc_norm_stderr\": 0.015788007190185884\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.738562091503268,\n \"acc_stderr\": 0.025160998214292456,\n \"acc_norm\": 0.738562091503268,\n \"acc_norm_stderr\": 0.025160998214292456\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6945337620578779,\n \"acc_stderr\": 0.026160584450140453,\n \"acc_norm\": 0.6945337620578779,\n \"acc_norm_stderr\": 0.026160584450140453\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.02465968518596728,\n \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.02465968518596728\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.5212765957446809,\n \"acc_stderr\": 0.029800481645628693,\n \"acc_norm\": 0.5212765957446809,\n \"acc_norm_stderr\": 0.029800481645628693\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4485006518904824,\n \"acc_stderr\": 0.012702317490559807,\n \"acc_norm\": 0.4485006518904824,\n \"acc_norm_stderr\": 0.012702317490559807\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6801470588235294,\n \"acc_stderr\": 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of migtissera/Tess-XS-v1.0
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model migtissera/Tess-XS-v1.0 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T21:55:23.260774(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of migtissera/Tess-XS-v1.0",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model migtissera/Tess-XS-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"### Supported Tasks and Leaderboards",
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"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of migtissera/Tess-XS-v1.0## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model migtissera/Tess-XS-v1.0 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T21:55:23.260774(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
ee05a30502dc85c0a3dbbd199f907b43b953b5a2 |
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-6.7b-v2](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T22:06:22.388098](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2_public/blob/main/results_2023-11-18T22-06-22.388098.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.3057412972161726,
"acc_stderr": 0.0324287022369866,
"acc_norm": 0.3085950645885834,
"acc_norm_stderr": 0.03324907491950671,
"mc1": 0.2215422276621787,
"mc1_stderr": 0.014537867601301137,
"mc2": 0.35596210654913335,
"mc2_stderr": 0.01360076088943332,
"em": 0.00985738255033557,
"em_stderr": 0.001011740962658443,
"f1": 0.06222839765100698,
"f1_stderr": 0.0016287678100739308
},
"harness|arc:challenge|25": {
"acc": 0.35409556313993173,
"acc_stderr": 0.01397545412275656,
"acc_norm": 0.39419795221843,
"acc_norm_stderr": 0.014280522667467327
},
"harness|hellaswag|10": {
"acc": 0.492531368253336,
"acc_stderr": 0.004989224715784535,
"acc_norm": 0.6639115714001195,
"acc_norm_stderr": 0.004714041652598593
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542129,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542129
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.04094376269996794,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.04094376269996794
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.23026315789473684,
"acc_stderr": 0.03426059424403165,
"acc_norm": 0.23026315789473684,
"acc_norm_stderr": 0.03426059424403165
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.37,
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"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
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},
"harness|hendrycksTest-college_biology|5": {
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},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.2,
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},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.3,
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"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
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},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.0349610148119118
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.14705882352941177,
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},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.32,
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"acc_norm": 0.32,
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"acc_norm": 0.3404255319148936,
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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},
"harness|hendrycksTest-high_school_psychology|5": {
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},
"harness|hendrycksTest-management|5": {
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},
"harness|hendrycksTest-marketing|5": {
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},
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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"harness|drop|3": {
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"harness|gsm8k|5": {
"acc": 0.012130401819560273,
"acc_stderr": 0.003015294242890953
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2 | [
"region:us"
]
| 2023-11-18T22:08:42+00:00 | {"pretty_name": "Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-6.7b-v2](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T22:06:22.388098](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b-v2_public/blob/main/results_2023-11-18T22-06-22.388098.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.3057412972161726,\n \"acc_stderr\": 0.0324287022369866,\n \"acc_norm\": 0.3085950645885834,\n \"acc_norm_stderr\": 0.03324907491950671,\n \"mc1\": 0.2215422276621787,\n \"mc1_stderr\": 0.014537867601301137,\n \"mc2\": 0.35596210654913335,\n \"mc2_stderr\": 0.01360076088943332,\n \"em\": 0.00985738255033557,\n \"em_stderr\": 0.001011740962658443,\n \"f1\": 0.06222839765100698,\n \"f1_stderr\": 0.0016287678100739308\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.35409556313993173,\n \"acc_stderr\": 0.01397545412275656,\n \"acc_norm\": 0.39419795221843,\n \"acc_norm_stderr\": 0.014280522667467327\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.492531368253336,\n \"acc_stderr\": 0.004989224715784535,\n \"acc_norm\": 0.6639115714001195,\n \"acc_norm_stderr\": 0.004714041652598593\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.04094376269996794,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.04094376269996794\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.23026315789473684,\n \"acc_stderr\": 0.03426059424403165,\n \"acc_norm\": 0.23026315789473684,\n \"acc_norm_stderr\": 0.03426059424403165\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.39622641509433965,\n \"acc_stderr\": 0.030102793781791194,\n \"acc_norm\": 0.39622641509433965,\n \"acc_norm_stderr\": 0.030102793781791194\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3194444444444444,\n \"acc_stderr\": 0.038990736873573344,\n \"acc_norm\": 0.3194444444444444,\n \"acc_norm_stderr\": 0.038990736873573344\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.2,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.30057803468208094,\n \"acc_stderr\": 0.0349610148119118,\n \"acc_norm\": 0.30057803468208094,\n \"acc_norm_stderr\": 0.0349610148119118\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.14705882352941177,\n \"acc_stderr\": 0.03524068951567447,\n \"acc_norm\": 0.14705882352941177,\n \"acc_norm_stderr\": 0.03524068951567447\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.3404255319148936,\n \"acc_stderr\": 0.030976692998534432,\n \"acc_norm\": 0.3404255319148936,\n \"acc_norm_stderr\": 0.030976692998534432\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.03999423879281336,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.03999423879281336\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.31724137931034485,\n \"acc_stderr\": 0.03878352372138621,\n \"acc_norm\": 0.31724137931034485,\n \"acc_norm_stderr\": 0.03878352372138621\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.26455026455026454,\n \"acc_stderr\": 0.02271746789770861,\n \"acc_norm\": 0.26455026455026454,\n \"acc_norm_stderr\": 0.02271746789770861\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2777777777777778,\n \"acc_stderr\": 0.04006168083848878,\n \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.04006168083848878\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3064516129032258,\n \"acc_stderr\": 0.026226485652553883,\n \"acc_norm\": 0.3064516129032258,\n \"acc_norm_stderr\": 0.026226485652553883\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.030712730070982592,\n \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.030712730070982592\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.34545454545454546,\n \"acc_stderr\": 0.037131580674819135,\n \"acc_norm\": 0.34545454545454546,\n \"acc_norm_stderr\": 0.037131580674819135\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.36363636363636365,\n \"acc_stderr\": 0.034273086529999344,\n \"acc_norm\": 0.36363636363636365,\n \"acc_norm_stderr\": 0.034273086529999344\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.29015544041450775,\n \"acc_stderr\": 0.03275264467791516,\n \"acc_norm\": 0.29015544041450775,\n \"acc_norm_stderr\": 0.03275264467791516\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.26666666666666666,\n \"acc_stderr\": 0.022421273612923714,\n \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.022421273612923714\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275798,\n \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275798\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.23109243697478993,\n \"acc_stderr\": 0.027381406927868956,\n \"acc_norm\": 0.23109243697478993,\n \"acc_norm_stderr\": 0.027381406927868956\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.23178807947019867,\n \"acc_stderr\": 0.03445406271987054,\n \"acc_norm\": 0.23178807947019867,\n \"acc_norm_stderr\": 0.03445406271987054\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.3688073394495413,\n \"acc_stderr\": 0.020686227560729544,\n \"acc_norm\": 0.3688073394495413,\n \"acc_norm_stderr\": 0.020686227560729544\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.18981481481481483,\n \"acc_stderr\": 0.02674471483469192,\n \"acc_norm\": 0.18981481481481483,\n \"acc_norm_stderr\": 0.02674471483469192\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.27941176470588236,\n \"acc_stderr\": 0.031493281045079556,\n \"acc_norm\": 0.27941176470588236,\n \"acc_norm_stderr\": 0.031493281045079556\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.3459915611814346,\n \"acc_stderr\": 0.030964810588786713,\n \"acc_norm\": 0.3459915611814346,\n \"acc_norm_stderr\": 0.030964810588786713\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.3435114503816794,\n \"acc_stderr\": 0.041649760719448786,\n \"acc_norm\": 0.3435114503816794,\n \"acc_norm_stderr\": 0.041649760719448786\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.371900826446281,\n \"acc_stderr\": 0.04412015806624503,\n \"acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.04412015806624503\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.3055555555555556,\n \"acc_stderr\": 0.044531975073749834,\n \"acc_norm\": 0.3055555555555556,\n \"acc_norm_stderr\": 0.044531975073749834\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.26993865030674846,\n \"acc_stderr\": 0.03487825168497892,\n \"acc_norm\": 0.26993865030674846,\n \"acc_norm_stderr\": 0.03487825168497892\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n \"acc_stderr\": 0.04059867246952685,\n \"acc_norm\": 0.24107142857142858,\n \"acc_norm_stderr\": 0.04059867246952685\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.2815533980582524,\n \"acc_stderr\": 0.04453254836326469,\n \"acc_norm\": 0.2815533980582524,\n \"acc_norm_stderr\": 0.04453254836326469\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.32051282051282054,\n \"acc_stderr\": 0.030572811310299607,\n \"acc_norm\": 0.32051282051282054,\n \"acc_norm_stderr\": 0.030572811310299607\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720683,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720683\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.3371647509578544,\n \"acc_stderr\": 0.016905207420803543,\n \"acc_norm\": 0.3371647509578544,\n \"acc_norm_stderr\": 0.016905207420803543\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.31213872832369943,\n \"acc_stderr\": 0.024946792225272314,\n \"acc_norm\": 0.31213872832369943,\n \"acc_norm_stderr\": 0.024946792225272314\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.02718449890994162,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.02718449890994162\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.36012861736334406,\n \"acc_stderr\": 0.027264297599804015,\n \"acc_norm\": 0.36012861736334406,\n \"acc_norm_stderr\": 0.027264297599804015\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.3148148148148148,\n \"acc_stderr\": 0.025842248700902182,\n \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.025842248700902182\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2907801418439716,\n \"acc_stderr\": 0.027090664368353178,\n \"acc_norm\": 0.2907801418439716,\n \"acc_norm_stderr\": 0.027090664368353178\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.27509778357235987,\n \"acc_stderr\": 0.01140544362099692,\n \"acc_norm\": 0.27509778357235987,\n \"acc_norm_stderr\": 0.01140544362099692\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.2867647058823529,\n \"acc_stderr\": 0.027472274473233818,\n \"acc_norm\": 0.2867647058823529,\n \"acc_norm_stderr\": 0.027472274473233818\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.2957516339869281,\n \"acc_stderr\": 0.018463154132632824,\n \"acc_norm\": 0.2957516339869281,\n \"acc_norm_stderr\": 0.018463154132632824\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.34545454545454546,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.34545454545454546,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.2653061224489796,\n \"acc_stderr\": 0.028263889943784606,\n \"acc_norm\": 0.2653061224489796,\n \"acc_norm_stderr\": 0.028263889943784606\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.3582089552238806,\n \"acc_stderr\": 0.03390393042268814,\n \"acc_norm\": 0.3582089552238806,\n \"acc_norm_stderr\": 0.03390393042268814\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.3313253012048193,\n \"acc_stderr\": 0.036643147772880864,\n \"acc_norm\": 0.3313253012048193,\n \"acc_norm_stderr\": 0.036643147772880864\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.4152046783625731,\n \"acc_stderr\": 0.03779275945503201,\n \"acc_norm\": 0.4152046783625731,\n \"acc_norm_stderr\": 0.03779275945503201\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2215422276621787,\n \"mc1_stderr\": 0.014537867601301137,\n \"mc2\": 0.35596210654913335,\n \"mc2_stderr\": 0.01360076088943332\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6424625098658248,\n \"acc_stderr\": 0.01347000744392069\n },\n \"harness|drop|3\": {\n \"em\": 0.00985738255033557,\n \"em_stderr\": 0.001011740962658443,\n \"f1\": 0.06222839765100698,\n \"f1_stderr\": 0.0016287678100739308\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.012130401819560273,\n \"acc_stderr\": 0.003015294242890953\n }\n}\n```", "repo_url": "https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_18T22_06_22.388098", "path": ["**/details_harness|arc:challenge|25_2023-11-18T22-06-22.388098.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-18T22-06-22.388098.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_18T22_06_22.388098", "path": ["**/details_harness|drop|3_2023-11-18T22-06-22.388098.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-18T22-06-22.388098.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_18T22_06_22.388098", "path": ["**/details_harness|gsm8k|5_2023-11-18T22-06-22.388098.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-18T22-06-22.388098.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_18T22_06_22.388098", "path": ["**/details_harness|hellaswag|10_2023-11-18T22-06-22.388098.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-18T22-06-22.388098.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_18T22_06_22.388098", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-18T22-06-22.388098.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-18T22-06-22.388098.parquet", 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T22:06:22.388098(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T22:06:22.388098(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-6.7b-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-6.7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T22:06:22.388098(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
295764240234a72cd2eaeea227d17bc6fa82959c | # Celine web scraped data
## About the website
Celine operates within the **fashion industry** in the EMEA region, particularly in **Germany**. This industry is currently undergoing digital transformation with a focus on **Ecommerce**, creating a competitive space for established and emerging fashion brands. In Germany, the local customer using various online stores has shown significant growth, displaying an increased interest in online fashion shopping. This pattern presents the fashion brands with a unique set of challenges and opportunities. The dataset observed includes **Ecommerce product-list page (PLP) data** on **Celine** in Germany, offering priceless insights into consumer behavior and preferences, allowing the brand to reach their target market more effectively.
## Link to **dataset**
[Germany - Celine - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Celine%20Product-prices%20Germany/r/rec14W2uH4yIsDx2F)
| DBQ/Celine.Product.prices.Germany | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Celine",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-18T22:12:33+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Germany - Celine - Product-level price list", "tags": ["webscraping", "ecommerce", "Celine", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 319426, "num_examples": 655}], "download_size": 78524, "dataset_size": 319426}} | 2023-11-18T22:12:38+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Celine #fashion #fashion product #image #fashion image #region-us
| # Celine web scraped data
## About the website
Celine operates within the fashion industry in the EMEA region, particularly in Germany. This industry is currently undergoing digital transformation with a focus on Ecommerce, creating a competitive space for established and emerging fashion brands. In Germany, the local customer using various online stores has shown significant growth, displaying an increased interest in online fashion shopping. This pattern presents the fashion brands with a unique set of challenges and opportunities. The dataset observed includes Ecommerce product-list page (PLP) data on Celine in Germany, offering priceless insights into consumer behavior and preferences, allowing the brand to reach their target market more effectively.
## Link to dataset
Germany - Celine - Product-level price list dataset
| [
"# Celine web scraped data",
"## About the website\n\nCeline operates within the fashion industry in the EMEA region, particularly in Germany. This industry is currently undergoing digital transformation with a focus on Ecommerce, creating a competitive space for established and emerging fashion brands. In Germany, the local customer using various online stores has shown significant growth, displaying an increased interest in online fashion shopping. This pattern presents the fashion brands with a unique set of challenges and opportunities. The dataset observed includes Ecommerce product-list page (PLP) data on Celine in Germany, offering priceless insights into consumer behavior and preferences, allowing the brand to reach their target market more effectively.",
"## Link to dataset\n\nGermany - Celine - Product-level price list dataset"
]
| [
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"# Celine web scraped data",
"## About the website\n\nCeline operates within the fashion industry in the EMEA region, particularly in Germany. This industry is currently undergoing digital transformation with a focus on Ecommerce, creating a competitive space for established and emerging fashion brands. In Germany, the local customer using various online stores has shown significant growth, displaying an increased interest in online fashion shopping. This pattern presents the fashion brands with a unique set of challenges and opportunities. The dataset observed includes Ecommerce product-list page (PLP) data on Celine in Germany, offering priceless insights into consumer behavior and preferences, allowing the brand to reach their target market more effectively.",
"## Link to dataset\n\nGermany - Celine - Product-level price list dataset"
]
| [
178,
7,
141,
17
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Celine #fashion #fashion product #image #fashion image #region-us \n# Celine web scraped data## About the website\n\nCeline operates within the fashion industry in the EMEA region, particularly in Germany. This industry is currently undergoing digital transformation with a focus on Ecommerce, creating a competitive space for established and emerging fashion brands. In Germany, the local customer using various online stores has shown significant growth, displaying an increased interest in online fashion shopping. This pattern presents the fashion brands with a unique set of challenges and opportunities. The dataset observed includes Ecommerce product-list page (PLP) data on Celine in Germany, offering priceless insights into consumer behavior and preferences, allowing the brand to reach their target market more effectively.## Link to dataset\n\nGermany - Celine - Product-level price list dataset"
]
|
03a53777b0dcb59e2a2153adf4c718ae0e8a7f0c | # Net-a-Porter web scraped data
## About the website
Net-a-Porter operates within the thriving **Ecommerce industry** across the EMEA region, with a strong foothold in the market of **Portugal** particularly. The brand is renowned for its luxury fashion offerings online, making it a key player in the digital retail landscape. They cater to tastes ranging from high street to haute couture, offering a wide variety of products. A data analysis has been conducted on Net-a-Porters **Ecommerce product-list page (PLP)** in Portugal. These PLP data sets offer crucial insights on product performance, customer preferences, and market trends, which are critical towards shaping the brand’s strategies in the competitive online fashion space.
## Link to **dataset**
[Portugal - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Portugal/r/recA0xr8F85lVPMgr)
| DBQ/Net.a.Porter.Product.prices.Portugal | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-18T22:12:46+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Portugal - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Net-a-Porter", "dtype": "string"}, {"name": "2023-11-08", "dtype": "string"}, {"name": "PRT", "dtype": "string"}, {"name": "EUR", "dtype": "string"}, {"name": "SAINT LAURENT", "dtype": "string"}, {"name": "BAGS", "dtype": "string"}, {"name": "SHOULDER BAGS", "dtype": "string"}, {"name": "CROSS BODY", "dtype": "string"}, {"name": "33258524072235985", "dtype": "int64"}, {"name": "Loulou Toy quilted leather shoulder bag", "dtype": "string"}, {"name": "https://www.net-a-porter.com/pt/en/shop/product/saint-laurent/bags/cross-body/loulou-toy-quilted-leather-shoulder-bag/33258524072235985", "dtype": "string"}, {"name": "https://www.net-a-porter.com/variants/images/33258524072235985/ou/w1000.jpg", "dtype": "string"}, {"name": "1490.00", "dtype": "float64"}, {"name": "1490.00.1", "dtype": "float64"}, {"name": "1490.00.2", "dtype": "float64"}, {"name": "1490.00.3", "dtype": "float64"}, {"name": "0", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 18057389, "num_examples": 44280}], "download_size": 5682167, "dataset_size": 18057389}} | 2023-11-18T22:12:54+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
Net-a-Porter operates within the thriving Ecommerce industry across the EMEA region, with a strong foothold in the market of Portugal particularly. The brand is renowned for its luxury fashion offerings online, making it a key player in the digital retail landscape. They cater to tastes ranging from high street to haute couture, offering a wide variety of products. A data analysis has been conducted on Net-a-Porters Ecommerce product-list page (PLP) in Portugal. These PLP data sets offer crucial insights on product performance, customer preferences, and market trends, which are critical towards shaping the brand’s strategies in the competitive online fashion space.
## Link to dataset
Portugal - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nNet-a-Porter operates within the thriving Ecommerce industry across the EMEA region, with a strong foothold in the market of Portugal particularly. The brand is renowned for its luxury fashion offerings online, making it a key player in the digital retail landscape. They cater to tastes ranging from high street to haute couture, offering a wide variety of products. A data analysis has been conducted on Net-a-Porters Ecommerce product-list page (PLP) in Portugal. These PLP data sets offer crucial insights on product performance, customer preferences, and market trends, which are critical towards shaping the brand’s strategies in the competitive online fashion space.",
"## Link to dataset\n\nPortugal - Net-a-Porter - Product-level price list dataset"
]
| [
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"# Net-a-Porter web scraped data",
"## About the website\n\nNet-a-Porter operates within the thriving Ecommerce industry across the EMEA region, with a strong foothold in the market of Portugal particularly. The brand is renowned for its luxury fashion offerings online, making it a key player in the digital retail landscape. They cater to tastes ranging from high street to haute couture, offering a wide variety of products. A data analysis has been conducted on Net-a-Porters Ecommerce product-list page (PLP) in Portugal. These PLP data sets offer crucial insights on product performance, customer preferences, and market trends, which are critical towards shaping the brand’s strategies in the competitive online fashion space.",
"## Link to dataset\n\nPortugal - Net-a-Porter - Product-level price list dataset"
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nNet-a-Porter operates within the thriving Ecommerce industry across the EMEA region, with a strong foothold in the market of Portugal particularly. The brand is renowned for its luxury fashion offerings online, making it a key player in the digital retail landscape. They cater to tastes ranging from high street to haute couture, offering a wide variety of products. A data analysis has been conducted on Net-a-Porters Ecommerce product-list page (PLP) in Portugal. These PLP data sets offer crucial insights on product performance, customer preferences, and market trends, which are critical towards shaping the brand’s strategies in the competitive online fashion space.## Link to dataset\n\nPortugal - Net-a-Porter - Product-level price list dataset"
]
|
85a185a10ae997dd5735801d640128dcec35659c |
# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-beta
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T23:27:56.473641](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta/blob/main/results_2023-12-04T23-27-56.473641.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6046654337307571,
"acc_stderr": 0.03331208745152503,
"acc_norm": 0.6113529654673323,
"acc_norm_stderr": 0.034010916290269214,
"mc1": 0.4222766217870257,
"mc1_stderr": 0.017290733254248174,
"mc2": 0.5783301386651128,
"mc2_stderr": 0.01580070269822175
},
"harness|arc:challenge|25": {
"acc": 0.5921501706484642,
"acc_stderr": 0.0143610972884497,
"acc_norm": 0.6245733788395904,
"acc_norm_stderr": 0.014150631435111728
},
"harness|hellaswag|10": {
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"acc_norm": 0.8434574785899224,
"acc_norm_stderr": 0.0036262628054422106
},
"harness|hendrycksTest-abstract_algebra|5": {
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},
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},
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},
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},
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},
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-marketing|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm": 0.48936170212765956,
"acc_norm_stderr": 0.029820747191422473
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.027962677604768917
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.5783301386651128,
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"harness|winogrande|5": {
"acc": 0.771112865035517,
"acc_stderr": 0.011807360224025397
},
"harness|gsm8k|5": {
"acc": 0.27065959059893857,
"acc_stderr": 0.012238245006183405
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta | [
"region:us"
]
| 2023-11-18T22:12:54+00:00 | {"pretty_name": "Evaluation run of HuggingFaceH4/zephyr-7b-beta", "dataset_summary": "Dataset automatically created during the evaluation run of model [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T23:27:56.473641](https://huggingface.co/datasets/open-llm-leaderboard/details_HuggingFaceH4__zephyr-7b-beta/blob/main/results_2023-12-04T23-27-56.473641.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6046654337307571,\n \"acc_stderr\": 0.03331208745152503,\n \"acc_norm\": 0.6113529654673323,\n \"acc_norm_stderr\": 0.034010916290269214,\n \"mc1\": 0.4222766217870257,\n \"mc1_stderr\": 0.017290733254248174,\n \"mc2\": 0.5783301386651128,\n \"mc2_stderr\": 0.01580070269822175\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5921501706484642,\n \"acc_stderr\": 0.0143610972884497,\n \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.014150631435111728\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6488747261501693,\n \"acc_stderr\": 0.004763465139038561,\n \"acc_norm\": 0.8434574785899224,\n \"acc_norm_stderr\": 0.0036262628054422106\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5855263157894737,\n \"acc_stderr\": 0.04008973785779206,\n \"acc_norm\": 0.5855263157894737,\n \"acc_norm_stderr\": 0.04008973785779206\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6566037735849056,\n \"acc_stderr\": 0.029224526469124792,\n \"acc_norm\": 0.6566037735849056,\n \"acc_norm_stderr\": 0.029224526469124792\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n \"acc_stderr\": 0.03669072477416906,\n \"acc_norm\": 0.6358381502890174,\n \"acc_norm_stderr\": 0.03669072477416906\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.04928099597287534,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.04928099597287534\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5148936170212766,\n \"acc_stderr\": 0.03267151848924777,\n \"acc_norm\": 0.5148936170212766,\n \"acc_norm_stderr\": 0.03267151848924777\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n \"acc_stderr\": 0.046570472605949625,\n \"acc_norm\": 0.4298245614035088,\n \"acc_norm_stderr\": 0.046570472605949625\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.496551724137931,\n \"acc_stderr\": 0.041665675771015785,\n \"acc_norm\": 0.496551724137931,\n \"acc_norm_stderr\": 0.041665675771015785\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.38095238095238093,\n \"acc_stderr\": 0.0250107491161376,\n \"acc_norm\": 0.38095238095238093,\n \"acc_norm_stderr\": 0.0250107491161376\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7451612903225806,\n \"acc_stderr\": 0.02479011845933221,\n \"acc_norm\": 0.7451612903225806,\n \"acc_norm_stderr\": 0.02479011845933221\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365907,\n \"acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365907\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8290155440414507,\n \"acc_stderr\": 0.02717121368316453,\n \"acc_norm\": 0.8290155440414507,\n \"acc_norm_stderr\": 0.02717121368316453\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878934,\n \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878934\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6512605042016807,\n \"acc_stderr\": 0.030956636328566545,\n \"acc_norm\": 0.6512605042016807,\n \"acc_norm_stderr\": 0.030956636328566545\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.32450331125827814,\n \"acc_stderr\": 0.038227469376587525,\n \"acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.038227469376587525\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8110091743119267,\n \"acc_stderr\": 0.016785481159203627,\n \"acc_norm\": 0.8110091743119267,\n \"acc_norm_stderr\": 0.016785481159203627\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5462962962962963,\n \"acc_stderr\": 0.03395322726375798,\n \"acc_norm\": 0.5462962962962963,\n \"acc_norm_stderr\": 0.03395322726375798\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7745098039215687,\n \"acc_stderr\": 0.029331162294251735,\n \"acc_norm\": 0.7745098039215687,\n \"acc_norm_stderr\": 0.029331162294251735\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7215189873417721,\n \"acc_stderr\": 0.029178682304842538,\n \"acc_norm\": 0.7215189873417721,\n \"acc_norm_stderr\": 0.029178682304842538\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6322869955156951,\n \"acc_stderr\": 0.03236198350928276,\n \"acc_norm\": 0.6322869955156951,\n \"acc_norm_stderr\": 0.03236198350928276\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7107438016528925,\n \"acc_stderr\": 0.04139112727635463,\n \"acc_norm\": 0.7107438016528925,\n \"acc_norm_stderr\": 0.04139112727635463\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.35714285714285715,\n \"acc_stderr\": 0.04547960999764376,\n \"acc_norm\": 0.35714285714285715,\n \"acc_norm_stderr\": 0.04547960999764376\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7281553398058253,\n \"acc_stderr\": 0.044052680241409216,\n \"acc_norm\": 0.7281553398058253,\n \"acc_norm_stderr\": 0.044052680241409216\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.02158649400128137,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.02158649400128137\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7828863346104725,\n \"acc_stderr\": 0.014743125394823297,\n \"acc_norm\": 0.7828863346104725,\n \"acc_norm_stderr\": 0.014743125394823297\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6878612716763006,\n \"acc_stderr\": 0.02494679222527231,\n \"acc_norm\": 0.6878612716763006,\n \"acc_norm_stderr\": 0.02494679222527231\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.33519553072625696,\n \"acc_stderr\": 0.015788007190185884,\n \"acc_norm\": 0.33519553072625696,\n \"acc_norm_stderr\": 0.015788007190185884\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281413,\n \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281413\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6688102893890675,\n \"acc_stderr\": 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-beta
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-beta on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-04T23:27:56.473641(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-beta",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T23:27:56.473641(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-beta",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T23:27:56.473641(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of HuggingFaceH4/zephyr-7b-beta## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model HuggingFaceH4/zephyr-7b-beta on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-04T23:27:56.473641(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
caccf9b0ed2a0682e15f60aca54496212596bdbd | # Prada web scraped data
## About the website
The **fashion industry** in the Asia Pacific, specifically in **Singapore**, continues to evolve and flourish. This growth is mainly driven by **luxury fashion brands** such as **Prada**. With the increasing accessibility due to digital platforms, many customers now are more inclined towards online shopping. Hence, Prada, like many other high-end retailers, has placed significant emphasis on its **Ecommerce strategy**. The brand is constantly aiming to improve the online shopping experience by enhancing product visibility and accessibility for its potential customers. The dataset observed contains **Ecommerce product-list page (PLP) data** on Prada in Singapore, which provides valuable insights into the performance and customer interaction with the digital platform.
## Link to **dataset**
[Singapore - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Singapore/r/recmztprvWS6saZ2U)
| DBQ/Prada.Product.prices.Singapore | [
"region:us"
]
| 2023-11-18T22:12:58+00:00 | {} | 2023-11-18T22:12:59+00:00 | []
| []
| TAGS
#region-us
| # Prada web scraped data
## About the website
The fashion industry in the Asia Pacific, specifically in Singapore, continues to evolve and flourish. This growth is mainly driven by luxury fashion brands such as Prada. With the increasing accessibility due to digital platforms, many customers now are more inclined towards online shopping. Hence, Prada, like many other high-end retailers, has placed significant emphasis on its Ecommerce strategy. The brand is constantly aiming to improve the online shopping experience by enhancing product visibility and accessibility for its potential customers. The dataset observed contains Ecommerce product-list page (PLP) data on Prada in Singapore, which provides valuable insights into the performance and customer interaction with the digital platform.
## Link to dataset
Singapore - Prada - Product-level price list dataset
| [
"# Prada web scraped data",
"## About the website\n\nThe fashion industry in the Asia Pacific, specifically in Singapore, continues to evolve and flourish. This growth is mainly driven by luxury fashion brands such as Prada. With the increasing accessibility due to digital platforms, many customers now are more inclined towards online shopping. Hence, Prada, like many other high-end retailers, has placed significant emphasis on its Ecommerce strategy. The brand is constantly aiming to improve the online shopping experience by enhancing product visibility and accessibility for its potential customers. The dataset observed contains Ecommerce product-list page (PLP) data on Prada in Singapore, which provides valuable insights into the performance and customer interaction with the digital platform.",
"## Link to dataset\n\nSingapore - Prada - Product-level price list dataset"
]
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"## About the website\n\nThe fashion industry in the Asia Pacific, specifically in Singapore, continues to evolve and flourish. This growth is mainly driven by luxury fashion brands such as Prada. With the increasing accessibility due to digital platforms, many customers now are more inclined towards online shopping. Hence, Prada, like many other high-end retailers, has placed significant emphasis on its Ecommerce strategy. The brand is constantly aiming to improve the online shopping experience by enhancing product visibility and accessibility for its potential customers. The dataset observed contains Ecommerce product-list page (PLP) data on Prada in Singapore, which provides valuable insights into the performance and customer interaction with the digital platform.",
"## Link to dataset\n\nSingapore - Prada - Product-level price list dataset"
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"passage: TAGS\n#region-us \n# Prada web scraped data## About the website\n\nThe fashion industry in the Asia Pacific, specifically in Singapore, continues to evolve and flourish. This growth is mainly driven by luxury fashion brands such as Prada. With the increasing accessibility due to digital platforms, many customers now are more inclined towards online shopping. Hence, Prada, like many other high-end retailers, has placed significant emphasis on its Ecommerce strategy. The brand is constantly aiming to improve the online shopping experience by enhancing product visibility and accessibility for its potential customers. The dataset observed contains Ecommerce product-list page (PLP) data on Prada in Singapore, which provides valuable insights into the performance and customer interaction with the digital platform.## Link to dataset\n\nSingapore - Prada - Product-level price list dataset"
]
|
1a867b1e2477b96ea0c37ed518578661fa1c12da | # Net-a-Porter web scraped data
## About the website
The **Ecommerce** industry in the EMEA region, particularly in **Italy**, has seen significant growth due to digital transformation and increased web shopping habits. Within this sector, the luxury fashion industry is a standout, where **Net-a-Porter** operates. This platform effectively fuses traditional haute couture and digital luxury shopping, offering an extensive range of high-end clothing and accessories. The observed dataset contains **Ecommerce product-list page (PLP)** data on Net-a-Porter in Italy, providing insight on user behavior, product attractiveness and market trends. This type of data enables customization and effectiveness of digital marketing strategies for the brand.
## Link to **dataset**
[Italy - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Italy/r/recAG83il5B3oEP18)
| DBQ/Net.a.Porter.Product.prices.Italy | [
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| 2023-11-18T22:14:27+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Italy - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Net-a-Porter", "dtype": "string"}, {"name": "2023-11-08", "dtype": "string"}, {"name": "ITA", "dtype": "string"}, {"name": "EUR", "dtype": "string"}, {"name": "SAINT LAURENT", "dtype": "string"}, {"name": "CLOTHING", "dtype": "string"}, {"name": "DRESSES", "dtype": "string"}, {"name": "MIDI DRESSES", "dtype": "string"}, {"name": "1647597276844592", "dtype": "int64"}, {"name": "Lace-trimmed silk-satin midi dress", "dtype": "string"}, {"name": "https://www.net-a-porter.com/it/en/shop/product/saint-laurent/clothing/midi-dresses/lace-trimmed-silk-satin-midi-dress/1647597276844592", "dtype": "string"}, {"name": "https://www.net-a-porter.com/variants/images/1647597276844592/ou/w1000.jpg", "dtype": "string"}, {"name": "3490.00", "dtype": "float64"}, {"name": "3490.00.1", "dtype": "float64"}, {"name": "3490.00.2", "dtype": "float64"}, {"name": "3490.00.3", "dtype": "float64"}, {"name": "0", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17591923, "num_examples": 43148}], "download_size": 5140785, "dataset_size": 17591923}} | 2023-11-18T22:14:35+00:00 | []
| [
"en"
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| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The Ecommerce industry in the EMEA region, particularly in Italy, has seen significant growth due to digital transformation and increased web shopping habits. Within this sector, the luxury fashion industry is a standout, where Net-a-Porter operates. This platform effectively fuses traditional haute couture and digital luxury shopping, offering an extensive range of high-end clothing and accessories. The observed dataset contains Ecommerce product-list page (PLP) data on Net-a-Porter in Italy, providing insight on user behavior, product attractiveness and market trends. This type of data enables customization and effectiveness of digital marketing strategies for the brand.
## Link to dataset
Italy - Net-a-Porter - Product-level price list dataset
| [
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"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Italy, has seen significant growth due to digital transformation and increased web shopping habits. Within this sector, the luxury fashion industry is a standout, where Net-a-Porter operates. This platform effectively fuses traditional haute couture and digital luxury shopping, offering an extensive range of high-end clothing and accessories. The observed dataset contains Ecommerce product-list page (PLP) data on Net-a-Porter in Italy, providing insight on user behavior, product attractiveness and market trends. This type of data enables customization and effectiveness of digital marketing strategies for the brand.",
"## Link to dataset\n\nItaly - Net-a-Porter - Product-level price list dataset"
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| [
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"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Italy, has seen significant growth due to digital transformation and increased web shopping habits. Within this sector, the luxury fashion industry is a standout, where Net-a-Porter operates. This platform effectively fuses traditional haute couture and digital luxury shopping, offering an extensive range of high-end clothing and accessories. The observed dataset contains Ecommerce product-list page (PLP) data on Net-a-Porter in Italy, providing insight on user behavior, product attractiveness and market trends. This type of data enables customization and effectiveness of digital marketing strategies for the brand.",
"## Link to dataset\n\nItaly - Net-a-Porter - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Italy, has seen significant growth due to digital transformation and increased web shopping habits. Within this sector, the luxury fashion industry is a standout, where Net-a-Porter operates. This platform effectively fuses traditional haute couture and digital luxury shopping, offering an extensive range of high-end clothing and accessories. The observed dataset contains Ecommerce product-list page (PLP) data on Net-a-Porter in Italy, providing insight on user behavior, product attractiveness and market trends. This type of data enables customization and effectiveness of digital marketing strategies for the brand.## Link to dataset\n\nItaly - Net-a-Porter - Product-level price list dataset"
]
|
6aa22ba9a1a016a79d8b03e88f0d54ebf871a31e | # Net-a-Porter web scraped data
## About the website
In the Asia Pacific region, particularly India, retail industries are witnessing a significant digital transformation. The **Ecommerce industry** is particularly flourishing, revolutionised by advanced technology, the proliferation of smartphones, and improved internet infrastructure. The **online fashion retail** sector is a key player in this surge, making a strong foothold in the world of Ecommerce. **Net-a-Porter**, a premier luxury online fashion retailer operates within this industry. The dataset examined, provides **Ecommerce product-list page (PLP) data** on Net-a-Porters offerings in India, revealing significant insights on product range, consumer preferences, price points and trends in the Indian online luxury fashion retail landscape.
## Link to **dataset**
[India - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20India/r/rec5aEBQ3a7hC1MIp)
| DBQ/Net.a.Porter.Product.prices.India | [
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| 2023-11-18T22:14:41+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "India - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Net-a-Porter", "dtype": "string"}, {"name": "2023-11-08", "dtype": "string"}, {"name": "IND", "dtype": "string"}, {"name": "USD", "dtype": "string"}, {"name": "KHAITE", "dtype": "string"}, {"name": "CLOTHING", "dtype": "string"}, {"name": "DRESSES", "dtype": "string"}, {"name": "MINI DRESSES", "dtype": "string"}, {"name": "1647597303269708", "dtype": "int64"}, {"name": "Janna strapless duchesse cotton-blend satin mini dress", "dtype": "string"}, {"name": "https://www.net-a-porter.com/in/en/shop/product/khaite/clothing/mini-dresses/janna-strapless-duchesse-cotton-blend-satin-mini-dress/1647597303269708", "dtype": "string"}, {"name": "https://www.net-a-porter.com/variants/images/1647597303269708/ou/w1000.jpg", "dtype": "string"}, {"name": "2094.00", "dtype": "float64"}, {"name": "1047.00", "dtype": "float64"}, {"name": "1958.84", "dtype": "float64"}, {"name": "979.42", "dtype": "float64"}, {"name": "1", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17965087, "num_examples": 44420}], "download_size": 5762779, "dataset_size": 17965087}} | 2023-11-18T22:14:49+00:00 | []
| [
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| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
In the Asia Pacific region, particularly India, retail industries are witnessing a significant digital transformation. The Ecommerce industry is particularly flourishing, revolutionised by advanced technology, the proliferation of smartphones, and improved internet infrastructure. The online fashion retail sector is a key player in this surge, making a strong foothold in the world of Ecommerce. Net-a-Porter, a premier luxury online fashion retailer operates within this industry. The dataset examined, provides Ecommerce product-list page (PLP) data on Net-a-Porters offerings in India, revealing significant insights on product range, consumer preferences, price points and trends in the Indian online luxury fashion retail landscape.
## Link to dataset
India - Net-a-Porter - Product-level price list dataset
| [
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"## About the website\n\nIn the Asia Pacific region, particularly India, retail industries are witnessing a significant digital transformation. The Ecommerce industry is particularly flourishing, revolutionised by advanced technology, the proliferation of smartphones, and improved internet infrastructure. The online fashion retail sector is a key player in this surge, making a strong foothold in the world of Ecommerce. Net-a-Porter, a premier luxury online fashion retailer operates within this industry. The dataset examined, provides Ecommerce product-list page (PLP) data on Net-a-Porters offerings in India, revealing significant insights on product range, consumer preferences, price points and trends in the Indian online luxury fashion retail landscape.",
"## Link to dataset\n\nIndia - Net-a-Porter - Product-level price list dataset"
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"# Net-a-Porter web scraped data",
"## About the website\n\nIn the Asia Pacific region, particularly India, retail industries are witnessing a significant digital transformation. The Ecommerce industry is particularly flourishing, revolutionised by advanced technology, the proliferation of smartphones, and improved internet infrastructure. The online fashion retail sector is a key player in this surge, making a strong foothold in the world of Ecommerce. Net-a-Porter, a premier luxury online fashion retailer operates within this industry. The dataset examined, provides Ecommerce product-list page (PLP) data on Net-a-Porters offerings in India, revealing significant insights on product range, consumer preferences, price points and trends in the Indian online luxury fashion retail landscape.",
"## Link to dataset\n\nIndia - Net-a-Porter - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nIn the Asia Pacific region, particularly India, retail industries are witnessing a significant digital transformation. The Ecommerce industry is particularly flourishing, revolutionised by advanced technology, the proliferation of smartphones, and improved internet infrastructure. The online fashion retail sector is a key player in this surge, making a strong foothold in the world of Ecommerce. Net-a-Porter, a premier luxury online fashion retailer operates within this industry. The dataset examined, provides Ecommerce product-list page (PLP) data on Net-a-Porters offerings in India, revealing significant insights on product range, consumer preferences, price points and trends in the Indian online luxury fashion retail landscape.## Link to dataset\n\nIndia - Net-a-Porter - Product-level price list dataset"
]
|
8bb6afe93f1b2fa623ca08f4a7d59beefc15b131 | # Chloe web scraped data
## About the website
The **Ecommerce industry** in America, particularly in the United States, has been showing rapid growth and substantial advancements amidst digitalization and increased online shopping trends. One vital sector within this industry is the **fashion industry**, where renowned brands like **Chloe** record significant sales. The brand operates through both physical outlets and online platforms. The dataset observed has **Ecommerce product-list page (PLP) data on Chloe** in United States, providing insightful observations on their digital performance, customer preferences, and shopping behavior. Such data is instrumental to marketers and analysts in understanding market trends, designing strategies, and focusing on customer segments to increase revenue and profits.
## Link to **dataset**
[United States - Chloe - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Chloe%20Product-prices%20United%20States/r/recHvXlrm9VQZj0kM)
| DBQ/Chloe.Product.prices.United.States | [
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| 2023-11-18T22:14:54+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "United States - Chloe - Product-level price list", "tags": ["webscraping", "ecommerce", "Chloe", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Chloe", "dtype": "string"}, {"name": "2023-11-08", "dtype": "string"}, {"name": "USA", "dtype": "string"}, {"name": "USD", "dtype": "string"}, {"name": "CHLOE", "dtype": "string"}, {"name": "WOMEN", "dtype": "string"}, {"name": "NEW ARRIVALS", "dtype": "string"}, {"name": "WINTER 2023", "dtype": "string"}, {"name": "45800720AE", "dtype": "string"}, {"name": "Hana mini bag", "dtype": "string"}, {"name": "https://www.chloe.com/us/shoulder-bag_cod45800720ae.html", "dtype": "string"}, {"name": "https://www.chloe.com/product_image/45800720AE/f/w282.jpg", "dtype": "string"}, {"name": "430.00", "dtype": "float64"}, {"name": "430.00.1", "dtype": "float64"}, {"name": "402.24", "dtype": "float64"}, {"name": "402.24.1", "dtype": "float64"}, {"name": "0", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 714750, "num_examples": 2569}], "download_size": 162722, "dataset_size": 714750}} | 2023-11-18T22:14:59+00:00 | []
| [
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| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Chloe #fashion #fashion product #image #fashion image #region-us
| # Chloe web scraped data
## About the website
The Ecommerce industry in America, particularly in the United States, has been showing rapid growth and substantial advancements amidst digitalization and increased online shopping trends. One vital sector within this industry is the fashion industry, where renowned brands like Chloe record significant sales. The brand operates through both physical outlets and online platforms. The dataset observed has Ecommerce product-list page (PLP) data on Chloe in United States, providing insightful observations on their digital performance, customer preferences, and shopping behavior. Such data is instrumental to marketers and analysts in understanding market trends, designing strategies, and focusing on customer segments to increase revenue and profits.
## Link to dataset
United States - Chloe - Product-level price list dataset
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"## About the website\n\nThe Ecommerce industry in America, particularly in the United States, has been showing rapid growth and substantial advancements amidst digitalization and increased online shopping trends. One vital sector within this industry is the fashion industry, where renowned brands like Chloe record significant sales. The brand operates through both physical outlets and online platforms. The dataset observed has Ecommerce product-list page (PLP) data on Chloe in United States, providing insightful observations on their digital performance, customer preferences, and shopping behavior. Such data is instrumental to marketers and analysts in understanding market trends, designing strategies, and focusing on customer segments to increase revenue and profits.",
"## Link to dataset\n\nUnited States - Chloe - Product-level price list dataset"
]
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"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Chloe #fashion #fashion product #image #fashion image #region-us \n",
"# Chloe web scraped data",
"## About the website\n\nThe Ecommerce industry in America, particularly in the United States, has been showing rapid growth and substantial advancements amidst digitalization and increased online shopping trends. One vital sector within this industry is the fashion industry, where renowned brands like Chloe record significant sales. The brand operates through both physical outlets and online platforms. The dataset observed has Ecommerce product-list page (PLP) data on Chloe in United States, providing insightful observations on their digital performance, customer preferences, and shopping behavior. Such data is instrumental to marketers and analysts in understanding market trends, designing strategies, and focusing on customer segments to increase revenue and profits.",
"## Link to dataset\n\nUnited States - Chloe - Product-level price list dataset"
]
| [
179,
6,
150,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Chloe #fashion #fashion product #image #fashion image #region-us \n# Chloe web scraped data## About the website\n\nThe Ecommerce industry in America, particularly in the United States, has been showing rapid growth and substantial advancements amidst digitalization and increased online shopping trends. One vital sector within this industry is the fashion industry, where renowned brands like Chloe record significant sales. The brand operates through both physical outlets and online platforms. The dataset observed has Ecommerce product-list page (PLP) data on Chloe in United States, providing insightful observations on their digital performance, customer preferences, and shopping behavior. Such data is instrumental to marketers and analysts in understanding market trends, designing strategies, and focusing on customer segments to increase revenue and profits.## Link to dataset\n\nUnited States - Chloe - Product-level price list dataset"
]
|
2bde2d46c48f2992e9740c49ef1ce5bde5d655d9 | # Bottega Veneta web scraped data
## About the website
**Bottega Veneta** operates in the luxury fashion industry within the EMEA region, specifically in **Italy**. This industry is recognized worldwide for its high-end, quality products that range from clothing, accessories to fragrances and home furnishings. Italy, in particular, holds a significant place in this market, given its long-standing reputation and excellence in the design and manufacturing of luxurious goods. With the steady increase in digital transformation, **Ecommerce** has become a crucial part of this sector, providing opportunities for brands like Bottega Veneta to reach global consumers more efficiently. The dataset observed contains **Ecommerce product-list page (PLP) data** on Bottega Veneta in Italy.
## Link to **dataset**
[Italy - Bottega Veneta - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Bottega%20Veneta%20Product-prices%20Italy/r/recoQt2LVCwv0kJbV)
| DBQ/Bottega.Veneta.Product.prices.Italy | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Bottega Veneta",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-18T22:15:04+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Italy - Bottega Veneta - Product-level price list", "tags": ["webscraping", "ecommerce", "Bottega Veneta", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Bottega Veneta", "dtype": "string"}, {"name": "2023-11-08", "dtype": "string"}, {"name": "ITA", "dtype": "string"}, {"name": "EUR", "dtype": "string"}, {"name": "BOTTEGA VENETA", "dtype": "string"}, {"name": "N.A.", "dtype": "string"}, {"name": "N.A..1", "dtype": "string"}, {"name": "N.A..2", "dtype": "string"}, {"name": "765857VMAY02145", "dtype": "string"}, {"name": "Portachiavi Intreccio con gancio a goccia", "dtype": "string"}, {"name": "https://www.bottegaveneta.com/it-it/portachiavi-intreccio-con-gancio-a-goccia-fondant-765857VMAY02145.html", "dtype": "string"}, {"name": "https://bottega-veneta.dam.kering.com/m/4351e9501f1673da/Thumbnail-765857VMAY02145_A.jpg?v=1", "dtype": "string"}, {"name": "390.00", "dtype": "float64"}, {"name": "390.00.1", "dtype": "float64"}, {"name": "390.00.2", "dtype": "float64"}, {"name": "390.00.3", "dtype": "float64"}, {"name": "0", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1629316, "num_examples": 4394}], "download_size": 467120, "dataset_size": 1629316}} | 2023-11-18T22:15:08+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Bottega Veneta #fashion #fashion product #image #fashion image #region-us
| # Bottega Veneta web scraped data
## About the website
Bottega Veneta operates in the luxury fashion industry within the EMEA region, specifically in Italy. This industry is recognized worldwide for its high-end, quality products that range from clothing, accessories to fragrances and home furnishings. Italy, in particular, holds a significant place in this market, given its long-standing reputation and excellence in the design and manufacturing of luxurious goods. With the steady increase in digital transformation, Ecommerce has become a crucial part of this sector, providing opportunities for brands like Bottega Veneta to reach global consumers more efficiently. The dataset observed contains Ecommerce product-list page (PLP) data on Bottega Veneta in Italy.
## Link to dataset
Italy - Bottega Veneta - Product-level price list dataset
| [
"# Bottega Veneta web scraped data",
"## About the website\n\nBottega Veneta operates in the luxury fashion industry within the EMEA region, specifically in Italy. This industry is recognized worldwide for its high-end, quality products that range from clothing, accessories to fragrances and home furnishings. Italy, in particular, holds a significant place in this market, given its long-standing reputation and excellence in the design and manufacturing of luxurious goods. With the steady increase in digital transformation, Ecommerce has become a crucial part of this sector, providing opportunities for brands like Bottega Veneta to reach global consumers more efficiently. The dataset observed contains Ecommerce product-list page (PLP) data on Bottega Veneta in Italy.",
"## Link to dataset\n\nItaly - Bottega Veneta - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Bottega Veneta #fashion #fashion product #image #fashion image #region-us \n",
"# Bottega Veneta web scraped data",
"## About the website\n\nBottega Veneta operates in the luxury fashion industry within the EMEA region, specifically in Italy. This industry is recognized worldwide for its high-end, quality products that range from clothing, accessories to fragrances and home furnishings. Italy, in particular, holds a significant place in this market, given its long-standing reputation and excellence in the design and manufacturing of luxurious goods. With the steady increase in digital transformation, Ecommerce has become a crucial part of this sector, providing opportunities for brands like Bottega Veneta to reach global consumers more efficiently. The dataset observed contains Ecommerce product-list page (PLP) data on Bottega Veneta in Italy.",
"## Link to dataset\n\nItaly - Bottega Veneta - Product-level price list dataset"
]
| [
180,
9,
157,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Bottega Veneta #fashion #fashion product #image #fashion image #region-us \n# Bottega Veneta web scraped data## About the website\n\nBottega Veneta operates in the luxury fashion industry within the EMEA region, specifically in Italy. This industry is recognized worldwide for its high-end, quality products that range from clothing, accessories to fragrances and home furnishings. Italy, in particular, holds a significant place in this market, given its long-standing reputation and excellence in the design and manufacturing of luxurious goods. With the steady increase in digital transformation, Ecommerce has become a crucial part of this sector, providing opportunities for brands like Bottega Veneta to reach global consumers more efficiently. The dataset observed contains Ecommerce product-list page (PLP) data on Bottega Veneta in Italy.## Link to dataset\n\nItaly - Bottega Veneta - Product-level price list dataset"
]
|
54149b46093e14a2eaf3102b7d8ef634db619a75 | # Net-a-Porter web scraped data
## About the website
The **Ecommerce** industry in the **Asia Pacific**, particularly in **Hong Kong**, has shown significant growth with a rising number of online shoppers. Companies, such as **Net-a-Porter**, have been thriving in this robust market, offering luxury fashion and accessories through their digital platforms. Hong Kongs high internet penetration and mobile device usage rates provide an excellent environment for Ecommerce businesses. Additionally, its consumers high purchasing power and desire for luxury brands have made this region a vital location for digital retail. The dataset observed encompassed the **Ecommerce product-list page (PLP) data** of Net-a-Porters operation in **Hong Kong**.
## Link to **dataset**
[Hong Kong - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Hong%20Kong/r/recLMteOOeDjt4LbK)
| DBQ/Net.a.Porter.Product.prices.Hong.Kong | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-18T22:15:16+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hong Kong - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Net-a-Porter", "dtype": "string"}, {"name": "2023-11-08", "dtype": "string"}, {"name": "HKG", "dtype": "string"}, {"name": "HKD", "dtype": "string"}, {"name": "GIANVITO ROSSI", "dtype": "string"}, {"name": "SHOES", "dtype": "string"}, {"name": "SANDALS", "dtype": "string"}, {"name": "FLAT", "dtype": "string"}, {"name": "10163292707857611", "dtype": "int64"}, {"name": "Portofino 20 suede sandals", "dtype": "string"}, {"name": "https://www.net-a-porter.com/hk/en/shop/product/gianvito-rossi/shoes/flat/portofino-20-suede-sandals/10163292707857611", "dtype": "string"}, {"name": "https://www.net-a-porter.com/variants/images/10163292707857611/ou/w1000.jpg", "dtype": "string"}, {"name": "6900.00", "dtype": "float64"}, {"name": "6900.00.1", "dtype": "float64"}, {"name": "825.16", "dtype": "float64"}, {"name": "825.16.1", "dtype": "float64"}, {"name": "0", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 21075726, "num_examples": 51659}], "download_size": 6495108, "dataset_size": 21075726}} | 2023-11-18T22:15:26+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The Ecommerce industry in the Asia Pacific, particularly in Hong Kong, has shown significant growth with a rising number of online shoppers. Companies, such as Net-a-Porter, have been thriving in this robust market, offering luxury fashion and accessories through their digital platforms. Hong Kongs high internet penetration and mobile device usage rates provide an excellent environment for Ecommerce businesses. Additionally, its consumers high purchasing power and desire for luxury brands have made this region a vital location for digital retail. The dataset observed encompassed the Ecommerce product-list page (PLP) data of Net-a-Porters operation in Hong Kong.
## Link to dataset
Hong Kong - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce industry in the Asia Pacific, particularly in Hong Kong, has shown significant growth with a rising number of online shoppers. Companies, such as Net-a-Porter, have been thriving in this robust market, offering luxury fashion and accessories through their digital platforms. Hong Kongs high internet penetration and mobile device usage rates provide an excellent environment for Ecommerce businesses. Additionally, its consumers high purchasing power and desire for luxury brands have made this region a vital location for digital retail. The dataset observed encompassed the Ecommerce product-list page (PLP) data of Net-a-Porters operation in Hong Kong.",
"## Link to dataset\n\nHong Kong - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce industry in the Asia Pacific, particularly in Hong Kong, has shown significant growth with a rising number of online shoppers. Companies, such as Net-a-Porter, have been thriving in this robust market, offering luxury fashion and accessories through their digital platforms. Hong Kongs high internet penetration and mobile device usage rates provide an excellent environment for Ecommerce businesses. Additionally, its consumers high purchasing power and desire for luxury brands have made this region a vital location for digital retail. The dataset observed encompassed the Ecommerce product-list page (PLP) data of Net-a-Porters operation in Hong Kong.",
"## Link to dataset\n\nHong Kong - Net-a-Porter - Product-level price list dataset"
]
| [
177,
11,
153,
22
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe Ecommerce industry in the Asia Pacific, particularly in Hong Kong, has shown significant growth with a rising number of online shoppers. Companies, such as Net-a-Porter, have been thriving in this robust market, offering luxury fashion and accessories through their digital platforms. Hong Kongs high internet penetration and mobile device usage rates provide an excellent environment for Ecommerce businesses. Additionally, its consumers high purchasing power and desire for luxury brands have made this region a vital location for digital retail. The dataset observed encompassed the Ecommerce product-list page (PLP) data of Net-a-Porters operation in Hong Kong.## Link to dataset\n\nHong Kong - Net-a-Porter - Product-level price list dataset"
]
|
41b3b9c1325647f3622cb81ac071d372ba12f633 | # Gucci web scraped data
## About the website
The **luxury fashion industry** in the **United States** is a rapidly evolving market, with a significant presence of globally renowned brands such as **Gucci**. In the recent years, the industry has channelled its efforts towards online platforms, leading to an increase in **ecommerce** activity. This transition has played a crucial role in boosting the accessibility and convenience of purchasing high-end fashion products. Of particular interest is the **Ecommerce product-list page (PLP) data on Gucci** in the United States. This dataset provides invaluable insights into online fashion consumer behavior, purchase patterns, and product preference trends.
## Link to **dataset**
[United States - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20United%20States/r/rec9JLoIyDa81dIck)
| DBQ/Gucci.Product.prices.United.States | [
"region:us"
]
| 2023-11-18T22:15:31+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2746363, "num_examples": 5764}], "download_size": 807752, "dataset_size": 2746363}} | 2023-11-18T22:15:36+00:00 | []
| []
| TAGS
#region-us
| # Gucci web scraped data
## About the website
The luxury fashion industry in the United States is a rapidly evolving market, with a significant presence of globally renowned brands such as Gucci. In the recent years, the industry has channelled its efforts towards online platforms, leading to an increase in ecommerce activity. This transition has played a crucial role in boosting the accessibility and convenience of purchasing high-end fashion products. Of particular interest is the Ecommerce product-list page (PLP) data on Gucci in the United States. This dataset provides invaluable insights into online fashion consumer behavior, purchase patterns, and product preference trends.
## Link to dataset
United States - Gucci - Product-level price list dataset
| [
"# Gucci web scraped data",
"## About the website\n\nThe luxury fashion industry in the United States is a rapidly evolving market, with a significant presence of globally renowned brands such as Gucci. In the recent years, the industry has channelled its efforts towards online platforms, leading to an increase in ecommerce activity. This transition has played a crucial role in boosting the accessibility and convenience of purchasing high-end fashion products. Of particular interest is the Ecommerce product-list page (PLP) data on Gucci in the United States. This dataset provides invaluable insights into online fashion consumer behavior, purchase patterns, and product preference trends.",
"## Link to dataset\n\nUnited States - Gucci - Product-level price list dataset"
]
| [
"TAGS\n#region-us \n",
"# Gucci web scraped data",
"## About the website\n\nThe luxury fashion industry in the United States is a rapidly evolving market, with a significant presence of globally renowned brands such as Gucci. In the recent years, the industry has channelled its efforts towards online platforms, leading to an increase in ecommerce activity. This transition has played a crucial role in boosting the accessibility and convenience of purchasing high-end fashion products. Of particular interest is the Ecommerce product-list page (PLP) data on Gucci in the United States. This dataset provides invaluable insights into online fashion consumer behavior, purchase patterns, and product preference trends.",
"## Link to dataset\n\nUnited States - Gucci - Product-level price list dataset"
]
| [
6,
7,
140,
18
]
| [
"passage: TAGS\n#region-us \n# Gucci web scraped data## About the website\n\nThe luxury fashion industry in the United States is a rapidly evolving market, with a significant presence of globally renowned brands such as Gucci. In the recent years, the industry has channelled its efforts towards online platforms, leading to an increase in ecommerce activity. This transition has played a crucial role in boosting the accessibility and convenience of purchasing high-end fashion products. Of particular interest is the Ecommerce product-list page (PLP) data on Gucci in the United States. This dataset provides invaluable insights into online fashion consumer behavior, purchase patterns, and product preference trends.## Link to dataset\n\nUnited States - Gucci - Product-level price list dataset"
]
|
61480d947f32f4096b6775b5a634a2cc6f7cf708 |
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-20b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-20b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T22:18:06.041004](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b_public/blob/main/results_2023-11-18T22-18-06.041004.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.2917409259387278,
"acc_stderr": 0.031996943680235174,
"acc_norm": 0.2937476215264222,
"acc_norm_stderr": 0.032809325583186354,
"mc1": 0.23011015911872704,
"mc1_stderr": 0.014734557959807767,
"mc2": 0.37096560638460435,
"mc2_stderr": 0.013667285437196756,
"em": 0.013108221476510067,
"em_stderr": 0.0011647864293203474,
"f1": 0.06517617449664448,
"f1_stderr": 0.0017212538746189806
},
"harness|arc:challenge|25": {
"acc": 0.38139931740614336,
"acc_stderr": 0.014194389086685261,
"acc_norm": 0.4180887372013652,
"acc_norm_stderr": 0.014413988396996074
},
"harness|hellaswag|10": {
"acc": 0.5077673770165305,
"acc_stderr": 0.004989179286677388,
"acc_norm": 0.6875124477195778,
"acc_norm_stderr": 0.00462560091677499
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2222222222222222,
"acc_stderr": 0.0359144408419697,
"acc_norm": 0.2222222222222222,
"acc_norm_stderr": 0.0359144408419697
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.26973684210526316,
"acc_stderr": 0.03611780560284898,
"acc_norm": 0.26973684210526316,
"acc_norm_stderr": 0.03611780560284898
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.32,
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b | [
"region:us"
]
| 2023-11-18T22:20:30+00:00 | {"pretty_name": "Evaluation run of AI-Sweden-Models/gpt-sw3-20b", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-20b](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T22:18:06.041004](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b_public/blob/main/results_2023-11-18T22-18-06.041004.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.2917409259387278,\n \"acc_stderr\": 0.031996943680235174,\n \"acc_norm\": 0.2937476215264222,\n \"acc_norm_stderr\": 0.032809325583186354,\n \"mc1\": 0.23011015911872704,\n \"mc1_stderr\": 0.014734557959807767,\n \"mc2\": 0.37096560638460435,\n \"mc2_stderr\": 0.013667285437196756,\n \"em\": 0.013108221476510067,\n \"em_stderr\": 0.0011647864293203474,\n \"f1\": 0.06517617449664448,\n \"f1_stderr\": 0.0017212538746189806\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.38139931740614336,\n \"acc_stderr\": 0.014194389086685261,\n \"acc_norm\": 0.4180887372013652,\n \"acc_norm_stderr\": 0.014413988396996074\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5077673770165305,\n \"acc_stderr\": 0.004989179286677388,\n \"acc_norm\": 0.6875124477195778,\n \"acc_norm_stderr\": 0.00462560091677499\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2222222222222222,\n \"acc_stderr\": 0.0359144408419697,\n \"acc_norm\": 0.2222222222222222,\n \"acc_norm_stderr\": 0.0359144408419697\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.26973684210526316,\n \"acc_stderr\": 0.03611780560284898,\n \"acc_norm\": 0.26973684210526316,\n \"acc_norm_stderr\": 0.03611780560284898\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2943396226415094,\n \"acc_stderr\": 0.028049186315695245,\n \"acc_norm\": 0.2943396226415094,\n \"acc_norm_stderr\": 0.028049186315695245\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.0446196043338474,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.0446196043338474\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2138728323699422,\n \"acc_stderr\": 0.03126511206173042,\n \"acc_norm\": 0.2138728323699422,\n \"acc_norm_stderr\": 0.03126511206173042\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.18627450980392157,\n \"acc_stderr\": 0.03873958714149351,\n \"acc_norm\": 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["**/details_harness|hendrycksTest-prehistory|5_2023-11-18T22-18-06.041004.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_11_18T22_18_06.041004", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-18-06.041004.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-18-06.041004.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_11_18T22_18_06.041004", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-18-06.041004.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-18-06.041004.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_11_18T22_18_06.041004", "path": 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["**/details_harness|winogrande|5_2023-11-18T22-18-06.041004.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-18T22-18-06.041004.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T22_18_06.041004", "path": ["results_2023-11-18T22-18-06.041004.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T22-18-06.041004.parquet"]}]}]} | 2023-11-18T22:21:24+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-20b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-20b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T22:18:06.041004(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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]
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T22:18:06.041004(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-20b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-20b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T22:18:06.041004(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
f1d5810e9986a05c53f6b0daecf503f29cd8e060 |
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-1.3b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-1.3b-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b-instruct_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T22:24:58.034031](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b-instruct_public/blob/main/results_2023-11-18T22-24-58.034031.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.26508562345540565,
"acc_stderr": 0.031234722319638815,
"acc_norm": 0.26674815683597625,
"acc_norm_stderr": 0.03200950359205627,
"mc1": 0.23990208078335373,
"mc1_stderr": 0.014948812679062133,
"mc2": 0.4031210627490525,
"mc2_stderr": 0.014405145562379922,
"em": 0.0003145973154362416,
"em_stderr": 0.0001816137946883977,
"f1": 0.04640520134228205,
"f1_stderr": 0.001234443866638647
},
"harness|arc:challenge|25": {
"acc": 0.2713310580204778,
"acc_stderr": 0.012993807727545797,
"acc_norm": 0.3097269624573379,
"acc_norm_stderr": 0.01351205841523836
},
"harness|hellaswag|10": {
"acc": 0.40131447918741286,
"acc_stderr": 0.0048916267180972575,
"acc_norm": 0.5142401911969727,
"acc_norm_stderr": 0.004987757314769834
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909282,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909282
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.28888888888888886,
"acc_stderr": 0.03915450630414251,
"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.03915450630414251
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.29605263157894735,
"acc_stderr": 0.037150621549989056,
"acc_norm": 0.29605263157894735,
"acc_norm_stderr": 0.037150621549989056
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.23018867924528302,
"acc_stderr": 0.02590789712240817,
"acc_norm": 0.23018867924528302,
"acc_norm_stderr": 0.02590789712240817
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2847222222222222,
"acc_stderr": 0.03773809990686933,
"acc_norm": 0.2847222222222222,
"acc_norm_stderr": 0.03773809990686933
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.19,
"acc_stderr": 0.03942772444036623,
"acc_norm": 0.19,
"acc_norm_stderr": 0.03942772444036623
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.2774566473988439,
"acc_stderr": 0.034140140070440354,
"acc_norm": 0.2774566473988439,
"acc_norm_stderr": 0.034140140070440354
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2549019607843137,
"acc_stderr": 0.043364327079931785,
"acc_norm": 0.2549019607843137,
"acc_norm_stderr": 0.043364327079931785
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2936170212765957,
"acc_stderr": 0.02977164271249123,
"acc_norm": 0.2936170212765957,
"acc_norm_stderr": 0.02977164271249123
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.19298245614035087,
"acc_stderr": 0.037124548537213684,
"acc_norm": 0.19298245614035087,
"acc_norm_stderr": 0.037124548537213684
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.33793103448275863,
"acc_stderr": 0.039417076320648906,
"acc_norm": 0.33793103448275863,
"acc_norm_stderr": 0.039417076320648906
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2724867724867725,
"acc_stderr": 0.022930973071633345,
"acc_norm": 0.2724867724867725,
"acc_norm_stderr": 0.022930973071633345
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.19047619047619047,
"acc_stderr": 0.03512207412302054,
"acc_norm": 0.19047619047619047,
"acc_norm_stderr": 0.03512207412302054
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.24838709677419354,
"acc_stderr": 0.024580028921481003,
"acc_norm": 0.24838709677419354,
"acc_norm_stderr": 0.024580028921481003
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|gsm8k|5": {
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"acc_stderr": 0.003447819272389002
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b-instruct | [
"region:us"
]
| 2023-11-18T22:27:19+00:00 | {"pretty_name": "Evaluation run of AI-Sweden-Models/gpt-sw3-1.3b-instruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [AI-Sweden-Models/gpt-sw3-1.3b-instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b-instruct) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b-instruct_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T22:24:58.034031](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b-instruct_public/blob/main/results_2023-11-18T22-24-58.034031.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.26508562345540565,\n \"acc_stderr\": 0.031234722319638815,\n \"acc_norm\": 0.26674815683597625,\n \"acc_norm_stderr\": 0.03200950359205627,\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062133,\n \"mc2\": 0.4031210627490525,\n \"mc2_stderr\": 0.014405145562379922,\n \"em\": 0.0003145973154362416,\n \"em_stderr\": 0.0001816137946883977,\n \"f1\": 0.04640520134228205,\n \"f1_stderr\": 0.001234443866638647\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.2713310580204778,\n \"acc_stderr\": 0.012993807727545797,\n \"acc_norm\": 0.3097269624573379,\n \"acc_norm_stderr\": 0.01351205841523836\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.40131447918741286,\n \"acc_stderr\": 0.0048916267180972575,\n \"acc_norm\": 0.5142401911969727,\n \"acc_norm_stderr\": 0.004987757314769834\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.28888888888888886,\n \"acc_stderr\": 0.03915450630414251,\n \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.03915450630414251\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.29605263157894735,\n \"acc_stderr\": 0.037150621549989056,\n \"acc_norm\": 0.29605263157894735,\n \"acc_norm_stderr\": 0.037150621549989056\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.23018867924528302,\n \"acc_stderr\": 0.02590789712240817,\n \"acc_norm\": 0.23018867924528302,\n \"acc_norm_stderr\": 0.02590789712240817\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.2847222222222222,\n \"acc_stderr\": 0.03773809990686933,\n \"acc_norm\": 0.2847222222222222,\n \"acc_norm_stderr\": 0.03773809990686933\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036623,\n \"acc_norm\": 0.19,\n \"acc_norm_stderr\": 0.03942772444036623\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.2774566473988439,\n \"acc_stderr\": 0.034140140070440354,\n \"acc_norm\": 0.2774566473988439,\n \"acc_norm_stderr\": 0.034140140070440354\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.2549019607843137,\n \"acc_stderr\": 0.043364327079931785,\n \"acc_norm\": 0.2549019607843137,\n \"acc_norm_stderr\": 0.043364327079931785\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.2936170212765957,\n \"acc_stderr\": 0.02977164271249123,\n \"acc_norm\": 0.2936170212765957,\n \"acc_norm_stderr\": 0.02977164271249123\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.19298245614035087,\n \"acc_stderr\": 0.037124548537213684,\n \"acc_norm\": 0.19298245614035087,\n \"acc_norm_stderr\": 0.037124548537213684\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.33793103448275863,\n \"acc_stderr\": 0.039417076320648906,\n \"acc_norm\": 0.33793103448275863,\n \"acc_norm_stderr\": 0.039417076320648906\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2724867724867725,\n \"acc_stderr\": 0.022930973071633345,\n \"acc_norm\": 0.2724867724867725,\n \"acc_norm_stderr\": 0.022930973071633345\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n \"acc_stderr\": 0.03512207412302054,\n \"acc_norm\": 0.19047619047619047,\n \"acc_norm_stderr\": 0.03512207412302054\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.24838709677419354,\n \"acc_stderr\": 0.024580028921481003,\n \"acc_norm\": 0.24838709677419354,\n \"acc_norm_stderr\": 0.024580028921481003\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2561576354679803,\n \"acc_stderr\": 0.0307127300709826,\n \"acc_norm\": 0.2561576354679803,\n \"acc_norm_stderr\": 0.0307127300709826\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.03427743175816524,\n \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.03427743175816524\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.20707070707070707,\n \"acc_stderr\": 0.028869778460267035,\n \"acc_norm\": 0.20707070707070707,\n \"acc_norm_stderr\": 0.028869778460267035\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.21243523316062177,\n \"acc_stderr\": 0.029519282616817244,\n \"acc_norm\": 0.21243523316062177,\n \"acc_norm_stderr\": 0.029519282616817244\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.24102564102564103,\n \"acc_stderr\": 0.02168554666533319,\n \"acc_norm\": 0.24102564102564103,\n \"acc_norm_stderr\": 0.02168554666533319\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n 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["**/details_harness|hendrycksTest-prehistory|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_accounting_5", "data_files": [{"split": "2023_11_18T22_24_58.034031", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_11_18T22_24_58.034031", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_11_18T22_24_58.034031", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_11_18T22_24_58.034031", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_11_18T22_24_58.034031", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": 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["**/details_harness|winogrande|5_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-18T22-24-58.034031.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_18T22_24_58.034031", "path": ["results_2023-11-18T22-24-58.034031.parquet"]}, {"split": "latest", "path": ["results_2023-11-18T22-24-58.034031.parquet"]}]}]} | 2023-11-18T22:28:08+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-1.3b-instruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-1.3b-instruct on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T22:24:58.034031(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-1.3b-instruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-1.3b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T22:24:58.034031(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
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"## Dataset Structure",
"### Data Instances",
"### Data Fields",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-1.3b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-18T22:24:58.034031(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of AI-Sweden-Models/gpt-sw3-1.3b-instruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model AI-Sweden-Models/gpt-sw3-1.3b-instruct on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T22:24:58.034031(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
|
e6831a028bc41758cebe66afbfb81ea5a2f9c984 | # Net-a-Porter web scraped data
## About the website
Net-a-Porter participates in the growing **ecommerce industry** in the **EMEA** region, particularly in **Spain**. The **fashion ecommerce industry** of Spain is expanding rapidly as digital technology becomes increasingly integrated in daily life. Many consumers are turning to online shopping for ease and convenience, and businesses are capitalizing on this trend. The provided dataset includes **Ecommerce product-list page (PLP) data** on Net-a-Porter in Spain, offering valuable insights to guide strategies within the online retail sector. In particular, it can help understand customer browsing behavior, preferences and purchasing patterns, crucial for content and product portfolio planning.
## Link to **dataset**
[Spain - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Spain/r/recdTadGg6KS4lFMV)
| DBQ/Net.a.Porter.Product.prices.Spain | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-18T22:28:37+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Spain - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"]} | 2023-11-18T22:28:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
Net-a-Porter participates in the growing ecommerce industry in the EMEA region, particularly in Spain. The fashion ecommerce industry of Spain is expanding rapidly as digital technology becomes increasingly integrated in daily life. Many consumers are turning to online shopping for ease and convenience, and businesses are capitalizing on this trend. The provided dataset includes Ecommerce product-list page (PLP) data on Net-a-Porter in Spain, offering valuable insights to guide strategies within the online retail sector. In particular, it can help understand customer browsing behavior, preferences and purchasing patterns, crucial for content and product portfolio planning.
## Link to dataset
Spain - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nNet-a-Porter participates in the growing ecommerce industry in the EMEA region, particularly in Spain. The fashion ecommerce industry of Spain is expanding rapidly as digital technology becomes increasingly integrated in daily life. Many consumers are turning to online shopping for ease and convenience, and businesses are capitalizing on this trend. The provided dataset includes Ecommerce product-list page (PLP) data on Net-a-Porter in Spain, offering valuable insights to guide strategies within the online retail sector. In particular, it can help understand customer browsing behavior, preferences and purchasing patterns, crucial for content and product portfolio planning.",
"## Link to dataset\n\nSpain - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nNet-a-Porter participates in the growing ecommerce industry in the EMEA region, particularly in Spain. The fashion ecommerce industry of Spain is expanding rapidly as digital technology becomes increasingly integrated in daily life. Many consumers are turning to online shopping for ease and convenience, and businesses are capitalizing on this trend. The provided dataset includes Ecommerce product-list page (PLP) data on Net-a-Porter in Spain, offering valuable insights to guide strategies within the online retail sector. In particular, it can help understand customer browsing behavior, preferences and purchasing patterns, crucial for content and product portfolio planning.",
"## Link to dataset\n\nSpain - Net-a-Porter - Product-level price list dataset"
]
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nNet-a-Porter participates in the growing ecommerce industry in the EMEA region, particularly in Spain. The fashion ecommerce industry of Spain is expanding rapidly as digital technology becomes increasingly integrated in daily life. Many consumers are turning to online shopping for ease and convenience, and businesses are capitalizing on this trend. The provided dataset includes Ecommerce product-list page (PLP) data on Net-a-Porter in Spain, offering valuable insights to guide strategies within the online retail sector. In particular, it can help understand customer browsing behavior, preferences and purchasing patterns, crucial for content and product portfolio planning.## Link to dataset\n\nSpain - Net-a-Porter - Product-level price list dataset"
]
|
de07b2f403ef0531fe12982f3aea2e24985d1ae3 |
# WIS database
This database contains a question answer list about text
This database was built using my this workflow:
1- load a raw text file
2- split into paragraphs
3- split paragraphs into sentences
4- for each word, ask question about its position and answer with the position, then ask about the word length and answer with the actual length of the word
5- ask a question about the number of words in the sentence and answer it
6- build a json database using this.
To do this, I kindly got the concent of Keith Curtis to use his website content as fuel to this algorithm.
The website can be found here:
https://keithcu.com/wordpress/?page_id=599
Best regards. | ParisNeo/Word_in_Sentence_Database | [
"task_categories:table-question-answering",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-11-18T22:31:02+00:00 | {"language": ["en"], "license": "apache-2.0", "task_categories": ["table-question-answering"], "pretty_name": "Word in Sentence database"} | 2023-11-19T00:13:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-table-question-answering #language-English #license-apache-2.0 #region-us
|
# WIS database
This database contains a question answer list about text
This database was built using my this workflow:
1- load a raw text file
2- split into paragraphs
3- split paragraphs into sentences
4- for each word, ask question about its position and answer with the position, then ask about the word length and answer with the actual length of the word
5- ask a question about the number of words in the sentence and answer it
6- build a json database using this.
To do this, I kindly got the concent of Keith Curtis to use his website content as fuel to this algorithm.
The website can be found here:
URL
Best regards. | [
"# WIS database\nThis database contains a question answer list about text\n\n\nThis database was built using my this workflow:\n\n1- load a raw text file\n2- split into paragraphs\n3- split paragraphs into sentences\n4- for each word, ask question about its position and answer with the position, then ask about the word length and answer with the actual length of the word\n5- ask a question about the number of words in the sentence and answer it\n6- build a json database using this.\n\n\nTo do this, I kindly got the concent of Keith Curtis to use his website content as fuel to this algorithm.\nThe website can be found here:\nURL\n\nBest regards."
]
| [
"TAGS\n#task_categories-table-question-answering #language-English #license-apache-2.0 #region-us \n",
"# WIS database\nThis database contains a question answer list about text\n\n\nThis database was built using my this workflow:\n\n1- load a raw text file\n2- split into paragraphs\n3- split paragraphs into sentences\n4- for each word, ask question about its position and answer with the position, then ask about the word length and answer with the actual length of the word\n5- ask a question about the number of words in the sentence and answer it\n6- build a json database using this.\n\n\nTo do this, I kindly got the concent of Keith Curtis to use his website content as fuel to this algorithm.\nThe website can be found here:\nURL\n\nBest regards."
]
| [
32,
135
]
| [
"passage: TAGS\n#task_categories-table-question-answering #language-English #license-apache-2.0 #region-us \n# WIS database\nThis database contains a question answer list about text\n\n\nThis database was built using my this workflow:\n\n1- load a raw text file\n2- split into paragraphs\n3- split paragraphs into sentences\n4- for each word, ask question about its position and answer with the position, then ask about the word length and answer with the actual length of the word\n5- ask a question about the number of words in the sentence and answer it\n6- build a json database using this.\n\n\nTo do this, I kindly got the concent of Keith Curtis to use his website content as fuel to this algorithm.\nThe website can be found here:\nURL\n\nBest regards."
]
|
75133b7a1f737edd0323cdddd4db04598fd77ede |
# Dataset Card for Evaluation run of maywell/Synatra-7B-v0.3-RP
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/Synatra-7B-v0.3-RP
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [maywell/Synatra-7B-v0.3-RP](https://huggingface.co/maywell/Synatra-7B-v0.3-RP) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-RP_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-18T23:02:29.150817](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-RP_public/blob/main/results_2023-11-18T23-02-29.150817.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6042478184701645,
"acc_stderr": 0.03267991789724199,
"acc_norm": 0.6118798356357696,
"acc_norm_stderr": 0.03337492967666177,
"mc1": 0.37209302325581395,
"mc1_stderr": 0.016921090118814035,
"mc2": 0.5263791321103062,
"mc2_stderr": 0.015312628675104242,
"em": 0.3953439597315436,
"em_stderr": 0.005007043944789993,
"f1": 0.46059983221476697,
"f1_stderr": 0.00481810685968407
},
"harness|arc:challenge|25": {
"acc": 0.5930034129692833,
"acc_stderr": 0.014356399418009117,
"acc_norm": 0.6220136518771331,
"acc_norm_stderr": 0.014169664520303098
},
"harness|hellaswag|10": {
"acc": 0.6338378809002191,
"acc_stderr": 0.004807699539973411,
"acc_norm": 0.8229436367257519,
"acc_norm_stderr": 0.003809362761248109
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.04284958639753401,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.04284958639753401
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316092,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6415094339622641,
"acc_stderr": 0.029514703583981765,
"acc_norm": 0.6415094339622641,
"acc_norm_stderr": 0.029514703583981765
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6597222222222222,
"acc_stderr": 0.039621355734862175,
"acc_norm": 0.6597222222222222,
"acc_norm_stderr": 0.039621355734862175
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5664739884393064,
"acc_stderr": 0.03778621079092056,
"acc_norm": 0.5664739884393064,
"acc_norm_stderr": 0.03778621079092056
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.24509803921568626,
"acc_stderr": 0.042801058373643966,
"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.042801058373643966
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.77,
"acc_stderr": 0.04229525846816507,
"acc_norm": 0.77,
"acc_norm_stderr": 0.04229525846816507
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5106382978723404,
"acc_stderr": 0.03267862331014063,
"acc_norm": 0.5106382978723404,
"acc_norm_stderr": 0.03267862331014063
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.0416180850350153,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.0416180850350153
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4497354497354497,
"acc_stderr": 0.02562085704293665,
"acc_norm": 0.4497354497354497,
"acc_norm_stderr": 0.02562085704293665
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.044444444444444495,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.044444444444444495
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7354838709677419,
"acc_stderr": 0.02509189237885928,
"acc_norm": 0.7354838709677419,
"acc_norm_stderr": 0.02509189237885928
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4630541871921182,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.4630541871921182,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009181,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009181
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8497409326424871,
"acc_stderr": 0.02578772318072387,
"acc_norm": 0.8497409326424871,
"acc_norm_stderr": 0.02578772318072387
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6,
"acc_stderr": 0.02483881198803316,
"acc_norm": 0.6,
"acc_norm_stderr": 0.02483881198803316
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2740740740740741,
"acc_stderr": 0.027195934804085626,
"acc_norm": 0.2740740740740741,
"acc_norm_stderr": 0.027195934804085626
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.634453781512605,
"acc_stderr": 0.031282177063684614,
"acc_norm": 0.634453781512605,
"acc_norm_stderr": 0.031282177063684614
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3841059602649007,
"acc_stderr": 0.03971301814719197,
"acc_norm": 0.3841059602649007,
"acc_norm_stderr": 0.03971301814719197
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7944954128440367,
"acc_stderr": 0.017324352325016022,
"acc_norm": 0.7944954128440367,
"acc_norm_stderr": 0.017324352325016022
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8088235294117647,
"acc_stderr": 0.02759917430064077,
"acc_norm": 0.8088235294117647,
"acc_norm_stderr": 0.02759917430064077
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7679324894514767,
"acc_stderr": 0.02747974455080851,
"acc_norm": 0.7679324894514767,
"acc_norm_stderr": 0.02747974455080851
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7099236641221374,
"acc_stderr": 0.03980066246467766,
"acc_norm": 0.7099236641221374,
"acc_norm_stderr": 0.03980066246467766
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252627,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252627
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7300613496932515,
"acc_stderr": 0.034878251684978906,
"acc_norm": 0.7300613496932515,
"acc_norm_stderr": 0.034878251684978906
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.041858325989283136,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.041858325989283136
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8461538461538461,
"acc_stderr": 0.02363687331748928,
"acc_norm": 0.8461538461538461,
"acc_norm_stderr": 0.02363687331748928
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8173690932311622,
"acc_stderr": 0.013816335389973145,
"acc_norm": 0.8173690932311622,
"acc_norm_stderr": 0.013816335389973145
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.02541600377316554,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.02541600377316554
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2346368715083799,
"acc_stderr": 0.014173044098303654,
"acc_norm": 0.2346368715083799,
"acc_norm_stderr": 0.014173044098303654
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.026716118380156847,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.026716118380156847
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
"acc_stderr": 0.026236965881153273,
"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153273
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7129629629629629,
"acc_stderr": 0.02517104191530968,
"acc_norm": 0.7129629629629629,
"acc_norm_stderr": 0.02517104191530968
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4645390070921986,
"acc_stderr": 0.029752389657427047,
"acc_norm": 0.4645390070921986,
"acc_norm_stderr": 0.029752389657427047
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.455019556714472,
"acc_stderr": 0.012718456618701768,
"acc_norm": 0.455019556714472,
"acc_norm_stderr": 0.012718456618701768
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6213235294117647,
"acc_stderr": 0.02946513363977613,
"acc_norm": 0.6213235294117647,
"acc_norm_stderr": 0.02946513363977613
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6339869281045751,
"acc_stderr": 0.019488025745529675,
"acc_norm": 0.6339869281045751,
"acc_norm_stderr": 0.019488025745529675
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6636363636363637,
"acc_stderr": 0.04525393596302506,
"acc_norm": 0.6636363636363637,
"acc_norm_stderr": 0.04525393596302506
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6775510204081633,
"acc_stderr": 0.029923100563683903,
"acc_norm": 0.6775510204081633,
"acc_norm_stderr": 0.029923100563683903
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8258706467661692,
"acc_stderr": 0.026814951200421603,
"acc_norm": 0.8258706467661692,
"acc_norm_stderr": 0.026814951200421603
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.0377525168068637,
"acc_norm": 0.83,
"acc_norm_stderr": 0.0377525168068637
},
"harness|hendrycksTest-virology|5": {
"acc": 0.46987951807228917,
"acc_stderr": 0.03885425420866766,
"acc_norm": 0.46987951807228917,
"acc_norm_stderr": 0.03885425420866766
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8070175438596491,
"acc_stderr": 0.030267457554898458,
"acc_norm": 0.8070175438596491,
"acc_norm_stderr": 0.030267457554898458
},
"harness|truthfulqa:mc|0": {
"mc1": 0.37209302325581395,
"mc1_stderr": 0.016921090118814035,
"mc2": 0.5263791321103062,
"mc2_stderr": 0.015312628675104242
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.01192000816365087
},
"harness|drop|3": {
"em": 0.3953439597315436,
"em_stderr": 0.005007043944789993,
"f1": 0.46059983221476697,
"f1_stderr": 0.00481810685968407
},
"harness|gsm8k|5": {
"acc": 0.21152388172858225,
"acc_stderr": 0.01124906096863505
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-RP | [
"region:us"
]
| 2023-11-18T23:05:30+00:00 | {"pretty_name": "Evaluation run of maywell/Synatra-7B-v0.3-RP", "dataset_summary": "Dataset automatically created during the evaluation run of model [maywell/Synatra-7B-v0.3-RP](https://huggingface.co/maywell/Synatra-7B-v0.3-RP) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-RP_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-18T23:02:29.150817](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-RP_public/blob/main/results_2023-11-18T23-02-29.150817.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.6042478184701645,\n \"acc_stderr\": 0.03267991789724199,\n \"acc_norm\": 0.6118798356357696,\n \"acc_norm_stderr\": 0.03337492967666177,\n \"mc1\": 0.37209302325581395,\n \"mc1_stderr\": 0.016921090118814035,\n \"mc2\": 0.5263791321103062,\n \"mc2_stderr\": 0.015312628675104242,\n \"em\": 0.3953439597315436,\n \"em_stderr\": 0.005007043944789993,\n \"f1\": 0.46059983221476697,\n \"f1_stderr\": 0.00481810685968407\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5930034129692833,\n \"acc_stderr\": 0.014356399418009117,\n \"acc_norm\": 0.6220136518771331,\n \"acc_norm_stderr\": 0.014169664520303098\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6338378809002191,\n \"acc_stderr\": 0.004807699539973411,\n \"acc_norm\": 0.8229436367257519,\n \"acc_norm_stderr\": 0.003809362761248109\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 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| TAGS
#region-us
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# Dataset Card for Evaluation run of maywell/Synatra-7B-v0.3-RP
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model maywell/Synatra-7B-v0.3-RP on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-18T23:02:29.150817(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of maywell/Synatra-7B-v0.3-RP## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model maywell/Synatra-7B-v0.3-RP on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-18T23:02:29.150817(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
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|
7232e25fb9f4ca1860cf90c958f7c7b66c4dde50 | # Dataset Card for "mlp_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | PaulLoisel/mlp_dataset | [
"region:us"
]
| 2023-11-18T23:47:05+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "purchased_products", "dtype": "float64"}, {"name": "review_time_spent", "dtype": "float64"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "Beauty Products", "dtype": "float64"}, {"name": "Furniture", "dtype": "float64"}, {"name": "Toys", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 636.6, "num_examples": 3}, {"name": "test", "num_bytes": 212.2, "num_examples": 1}, {"name": "val", "num_bytes": 212.2, "num_examples": 1}], "download_size": 15397, "dataset_size": 1061.0}} | 2023-11-18T23:47:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mlp_dataset"
More Information needed | [
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|
1edaada0f85ba039885f7903e0db1b219aa58d2d | # Dataset Card for "mlp_no_cat_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | PaulLoisel/mlp_no_cat_dataset | [
"region:us"
]
| 2023-11-18T23:59:34+00:00 | {"dataset_info": {"features": [{"name": "purchased_products", "dtype": "int64"}, {"name": "review_time_spent", "dtype": "int64"}, {"name": "product_category", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 601.2, "num_examples": 3}, {"name": "test", "num_bytes": 200.4, "num_examples": 1}, {"name": "val", "num_bytes": 200.4, "num_examples": 1}], "download_size": 12247, "dataset_size": 1002.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}]} | 2023-11-19T06:55:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mlp_no_cat_dataset"
More Information needed | [
"# Dataset Card for \"mlp_no_cat_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mlp_no_cat_dataset\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"mlp_no_cat_dataset\"\n\nMore Information needed"
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|
557ceafc2f2270ed80617a3c65da1b7ad10ec78d | # Farfetch web scraped data
## About the website
Farfetch operates within the **Ecommerce** industry in the EMEA region, specifically in the **United Arab Emirates**. This industry has grown exponentially in recent years, driven by an increasing number of internet users, accessibility to high-speed internet, and consumer convenience. In the UAE, the Ecommerce industry is especially dynamic and forward-thinking, with continuous technological advancements and an investment-friendly environment propelling its growth. The dataset observed includes **Ecommerce product-list page (PLP) data on Farfetch** in the UAE, providing valuable insights into the industry trends and consumer behaviour in this specific region.
## Link to **dataset**
[United Arab Emirates - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20United%20Arab%20Emirates/r/recltOpHnVuiZZmML)
| DBQ/Farfetch.Product.prices.United.Arab.Emirates | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
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"multilinguality:monolingual",
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"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T00:16:15+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "United Arab Emirates - Farfetch - Product-level price list", "tags": ["webscraping", "ecommerce", "Farfetch", "fashion", "fashion product", "image", "fashion image"]} | 2023-11-19T00:16:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us
| # Farfetch web scraped data
## About the website
Farfetch operates within the Ecommerce industry in the EMEA region, specifically in the United Arab Emirates. This industry has grown exponentially in recent years, driven by an increasing number of internet users, accessibility to high-speed internet, and consumer convenience. In the UAE, the Ecommerce industry is especially dynamic and forward-thinking, with continuous technological advancements and an investment-friendly environment propelling its growth. The dataset observed includes Ecommerce product-list page (PLP) data on Farfetch in the UAE, providing valuable insights into the industry trends and consumer behaviour in this specific region.
## Link to dataset
United Arab Emirates - Farfetch - Product-level price list dataset
| [
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"## About the website\n\nFarfetch operates within the Ecommerce industry in the EMEA region, specifically in the United Arab Emirates. This industry has grown exponentially in recent years, driven by an increasing number of internet users, accessibility to high-speed internet, and consumer convenience. In the UAE, the Ecommerce industry is especially dynamic and forward-thinking, with continuous technological advancements and an investment-friendly environment propelling its growth. The dataset observed includes Ecommerce product-list page (PLP) data on Farfetch in the UAE, providing valuable insights into the industry trends and consumer behaviour in this specific region.",
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]
|
9fc48e7a84c696ae2c9dbddf8bc67c8ffa4e3ea3 | # YuzuMarker.FontDetection
- Code: https://github.com/JeffersonQin/YuzuMarker.FontDetection
- Space: https://huggingface.co/spaces/gyrojeff/YuzuMarker.FontDetection
- Dataset: https://huggingface.co/datasets/gyrojeff/YuzuMarker.FontDetection/tree/master
The generated dataset is now available. Data are on the `master` branch due to initial commit error.
To use the data, each `.tar` package contains a `train`, `val`, `test` split. Move them to `./dataset`, then untar them.
To train the model, provide the path(s) of the untarred folders to the training script. For detail, please check the code repository.
| gyrojeff/YuzuMarker.FontDetection | [
"license:mit",
"region:us"
]
| 2023-11-19T00:52:04+00:00 | {"license": "mit"} | 2023-11-19T02:51:10+00:00 | []
| []
| TAGS
#license-mit #region-us
| # YuzuMarker.FontDetection
- Code: URL
- Space: URL
- Dataset: URL
The generated dataset is now available. Data are on the 'master' branch due to initial commit error.
To use the data, each '.tar' package contains a 'train', 'val', 'test' split. Move them to './dataset', then untar them.
To train the model, provide the path(s) of the untarred folders to the training script. For detail, please check the code repository.
| [
"# YuzuMarker.FontDetection\n\n- Code: URL\n- Space: URL\n- Dataset: URL\n\nThe generated dataset is now available. Data are on the 'master' branch due to initial commit error.\n\nTo use the data, each '.tar' package contains a 'train', 'val', 'test' split. Move them to './dataset', then untar them.\n\nTo train the model, provide the path(s) of the untarred folders to the training script. For detail, please check the code repository."
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| [
"TAGS\n#license-mit #region-us \n",
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| [
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| [
"passage: TAGS\n#license-mit #region-us \n# YuzuMarker.FontDetection\n\n- Code: URL\n- Space: URL\n- Dataset: URL\n\nThe generated dataset is now available. Data are on the 'master' branch due to initial commit error.\n\nTo use the data, each '.tar' package contains a 'train', 'val', 'test' split. Move them to './dataset', then untar them.\n\nTo train the model, provide the path(s) of the untarred folders to the training script. For detail, please check the code repository."
]
|
6bdaa83cce115420709b3d7a729d7a623995c5f5 | # Dataset Card for "ark-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | keylazy/ark-raw | [
"region:us"
]
| 2023-11-19T01:09:28+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text1", "dtype": "string"}, {"name": "text2", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 274489671, "num_examples": 1000000}, {"name": "test", "num_bytes": 27481428, "num_examples": 100000}], "download_size": 189424610, "dataset_size": 301971099}} | 2023-11-19T04:12:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ark-raw"
More Information needed | [
"# Dataset Card for \"ark-raw\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ark-raw\"\n\nMore Information needed"
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| [
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13
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ark-raw\"\n\nMore Information needed"
]
|
a0ff250c15d778096706e9951b371ade49457013 | # Dataset Card for "colorful_s_1024"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | recoilme/colorful_s_1024 | [
"region:us"
]
| 2023-11-19T01:12:55+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4734762072.4, "num_examples": 9050}], "download_size": 4863621553, "dataset_size": 4734762072.4}} | 2024-01-07T02:29:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "colorful_s_1024"
More Information needed | [
"# Dataset Card for \"colorful_s_1024\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"colorful_s_1024\"\n\nMore Information needed"
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| [
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"passage: TAGS\n#region-us \n# Dataset Card for \"colorful_s_1024\"\n\nMore Information needed"
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|
522d73f245689c494ca7284ce6186f7ae9fa45bb | 根据 https://github.com/ml-distribution/chinese-corpus/tree/master/emotion-dic/zhiwang 整理的 词表（一个文件），但似乎用在分词方面效果不太好。
分词代码可以参考：https://cloud.tencent.com/developer/article/2003172?pos=comment&pos=comment | ewwerpm/Chinese_word | [
"region:us"
]
| 2023-11-19T02:51:13+00:00 | {} | 2023-11-19T02:53:14+00:00 | []
| []
| TAGS
#region-us
| 根据 URL 整理的 词表（一个文件），但似乎用在分词方面效果不太好。
分词代码可以参考：URL | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
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|
2f2fa3b37662c7517599d319eb6e2cf38aef2e01 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [https://raw.githubusercontent.com/Goutami-Sooda/ITRL-Project/main/Dataset(JSON).json](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | AshArya/ITRLDataset | [
"region:us"
]
| 2023-11-19T05:08:43+00:00 | {} | 2023-11-19T05:10:07+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using URL
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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"## Glossary [optional]",
"## More Information [optional]",
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"## Dataset Card Contact"
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|
0f1ff25d625a8759d4d2895a2c8115e4a9b786dc |
# Dataset Card for Black-box Prompt Optimization (BPO)
## Dataset Description
- **Repository:** https://github.com/thu-coai/BPO
- **Paper:** https://arxiv.org/abs/2311.04155
- **Model:**
### Data Summary
To advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's `gpt-3.5-turbo` engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.
Please refer to our [paper](https://arxiv.org/abs/2311.04155) for further details.
### Supported Tasks
The BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.
### Language
The data in BPO are in English.
## Data Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"prompt": "Minimize the following data set\n[3, 4, 6, 7, 8, 10]",
"optimized_prompt": "Provide a step-by-step solution to minimize the following data set: [3, 4, 6, 7, 8, 10].",
"good_res": "To minimize the values in the given data set, which is a list of numbers, you could sort them in ascending order as follows: [3, 4, 6, 7, 8, 10]. The minimized (least) value in this data set would be 3.",
"bad_res": "[3, 4, 6, 7, 8, 10]"
}
```
### Data Fields
The data fields are as follows:
* `prompt`: the original user input
* `optimized_prompt`: the optimized prompt based on feedback
* `good_res`: human-chosen (preferred) response
* `bad_res`: human-rejected response
### Data Splits
| | train | valid | test |
|---------------|------:|------:|------:|
| BPO | 13895 | 300 | 200 |
## Additional Information
### Source Data
Our feedback data come from the following open-sourced datasets:
- [OASST1](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Alpaca-GPT4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/blob/main/data/comparison_data_v2.json)
- [Chatbot Arena Conversation](https://huggingface.co/datasets/lmsys/chatbot_arena_conversations)
### Other Known Limitations
- Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.
- Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.
- Optimized Prompts: The optimized prompts are auto-generated by `gpt-3.5-turbo` based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct.
### Citation Information
```
@article{cheng2023black,
title={Black-Box Prompt Optimization: Aligning Large Language Models without Model Training},
author={Cheng, Jiale and Liu, Xiao and Zheng, Kehan and Ke, Pei and Wang, Hongning and Dong, Yuxiao and Tang, Jie and Huang, Minlie},
journal={arXiv preprint arXiv:2311.04155},
year={2023}
}
``` | CCCCCC/BPO | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"human_feedback",
"arxiv:2311.04155",
"region:us"
]
| 2023-11-19T06:20:37+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "BPO", "tags": ["human_feedback"]} | 2023-11-20T05:42:13+00:00 | [
"2311.04155"
]
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #human_feedback #arxiv-2311.04155 #region-us
| Dataset Card for Black-box Prompt Optimization (BPO)
====================================================
Dataset Description
-------------------
* Repository: URL
* Paper: URL
* Model:
### Data Summary
To advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.
Please refer to our paper for further details.
### Supported Tasks
The BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.
### Language
The data in BPO are in English.
Data Structure
--------------
### Data Instances
An example of "train" looks as follows:
### Data Fields
The data fields are as follows:
* 'prompt': the original user input
* 'optimized\_prompt': the optimized prompt based on feedback
* 'good\_res': human-chosen (preferred) response
* 'bad\_res': human-rejected response
### Data Splits
Additional Information
----------------------
### Source Data
Our feedback data come from the following open-sourced datasets:
* OASST1
* hh-rlhf
* Alpaca-GPT4
* Chatbot Arena Conversation
### Other Known Limitations
* Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.
* Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.
* Optimized Prompts: The optimized prompts are auto-generated by 'gpt-3.5-turbo' based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct.
| [
"### Data Summary\n\n\nTo advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.\n\n\nPlease refer to our paper for further details.",
"### Supported Tasks\n\n\nThe BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.",
"### Language\n\n\nThe data in BPO are in English.\n\n\nData Structure\n--------------",
"### Data Instances\n\n\nAn example of \"train\" looks as follows:",
"### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'prompt': the original user input\n* 'optimized\\_prompt': the optimized prompt based on feedback\n* 'good\\_res': human-chosen (preferred) response\n* 'bad\\_res': human-rejected response",
"### Data Splits\n\n\n\nAdditional Information\n----------------------",
"### Source Data\n\n\nOur feedback data come from the following open-sourced datasets:\n\n\n* OASST1\n* hh-rlhf\n* Alpaca-GPT4\n* Chatbot Arena Conversation",
"### Other Known Limitations\n\n\n* Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.\n* Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.\n* Optimized Prompts: The optimized prompts are auto-generated by 'gpt-3.5-turbo' based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #human_feedback #arxiv-2311.04155 #region-us \n",
"### Data Summary\n\n\nTo advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.\n\n\nPlease refer to our paper for further details.",
"### Supported Tasks\n\n\nThe BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.",
"### Language\n\n\nThe data in BPO are in English.\n\n\nData Structure\n--------------",
"### Data Instances\n\n\nAn example of \"train\" looks as follows:",
"### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'prompt': the original user input\n* 'optimized\\_prompt': the optimized prompt based on feedback\n* 'good\\_res': human-chosen (preferred) response\n* 'bad\\_res': human-rejected response",
"### Data Splits\n\n\n\nAdditional Information\n----------------------",
"### Source Data\n\n\nOur feedback data come from the following open-sourced datasets:\n\n\n* OASST1\n* hh-rlhf\n* Alpaca-GPT4\n* Chatbot Arena Conversation",
"### Other Known Limitations\n\n\n* Feedback Data Quality: Due to our use of open-source feedback data, some human preferences included may not be entirely accurate.\n* Task Diversity: Despite our efforts to filter and achieve a diverse dataset, these open-source datasets are clearly not sufficient to cover the wide variety of user queries.\n* Optimized Prompts: The optimized prompts are auto-generated by 'gpt-3.5-turbo' based on feedback data. Even though we have manually reviewed and modified the dataset, we cannot guarantee that all prompt optimizations are correct."
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| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #human_feedback #arxiv-2311.04155 #region-us \n### Data Summary\n\n\nTo advance the development of alignment in language models, we introduce a black-box alignment method. BPO enhances the alignment of various Large Language Models (LLMs) with human preferences using only a plug-and-play model. To further promote alignment work from the prompting perspective, we are releasing the BPO Dataset. This dataset comprises 14,395 entries of prompt optimization pairs, constructed using open-source feedback data with OpenAI's 'gpt-3.5-turbo' engine. We have thoroughly ensured the quality and diversity of the data through careful filtering and correction.\n\n\nPlease refer to our paper for further details.### Supported Tasks\n\n\nThe BPO Dataset is designed for Prompt Optimization / Engineering task. As we have carefully flitered and curated the dataset, it is also a good choice for SFT or RLHF.### Language\n\n\nThe data in BPO are in English.\n\n\nData Structure\n--------------### Data Instances\n\n\nAn example of \"train\" looks as follows:### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'prompt': the original user input\n* 'optimized\\_prompt': the optimized prompt based on feedback\n* 'good\\_res': human-chosen (preferred) response\n* 'bad\\_res': human-rejected response### Data Splits\n\n\n\nAdditional Information\n----------------------### Source Data\n\n\nOur feedback data come from the following open-sourced datasets:\n\n\n* OASST1\n* hh-rlhf\n* Alpaca-GPT4\n* Chatbot Arena Conversation"
]
|
bf0c54d036ec7fa518dfd414cf2d33d68b3e1c62 | # Dataset Card for "mlp_splitted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | PaulLoisel/mlp_splitted | [
"region:us"
]
| 2023-11-19T06:59:33+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "val", "path": "data/val-*"}]}], "dataset_info": {"features": [{"name": "purchased_products", "dtype": "float64"}, {"name": "review_time_spent", "dtype": "float64"}, {"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}, {"name": "Beauty Products", "dtype": "float64"}, {"name": "Furniture", "dtype": "float64"}, {"name": "Toys", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 636.6, "num_examples": 3}, {"name": "test", "num_bytes": 212.2, "num_examples": 1}, {"name": "val", "num_bytes": 212.2, "num_examples": 1}], "download_size": 15368, "dataset_size": 1061.0}} | 2023-11-20T08:36:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mlp_splitted"
More Information needed | [
"# Dataset Card for \"mlp_splitted\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mlp_splitted\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"mlp_splitted\"\n\nMore Information needed"
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|
f293ed5b67192fe26d46e3823965d2a86e3d47a7 |
# Dataset Card for German financial_phrasebank
## Dataset Description
### Dataset Summary
This datset is a German translation of the financial phrasebank of [Malo et al. (2013)](https://arxiv.org/abs/1307.5336) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with [Deepl](https://www.deepl.com/translator).
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
German
## Dataset Structure
### Data Instances
```
{ "sentence": "Die finnische nationale Fluggesellschaft gab an, dass der Nettoverlust in den Monaten April bis Juni 26 Millionen Euro betrug, verglichen mit einem Nettogewinn von 13 Millionen Euro im Vorjahr..",
"label": "negative"
}
```
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
### Data Splits
The data is splitted in a train, test and validation set using stratified sampling:
- train (2763 observations)
- validation (344 observations)
- test (346 observations)
## Further Information
For further information regarding the source data or the annotation process, please look at the original [paper](https://arxiv.org/abs/1307.5336) or the original [dataset](https://huggingface.co/datasets/financial_phrasebank).
## Licensing Information
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/.
In particular, this license permits the free use of the data for non-commercial purposes.
If you are interested in commercial use of the data, please contact the authors of the original datset for an appropriate license:
- [Pekka Malo](mailto:[email protected])
- [Ankur Sinha](mailto:[email protected])
| scherrmann/financial_phrasebank_75agree_german | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"language:de",
"license:cc-by-nc-sa-3.0",
"finance",
"arxiv:1307.5336",
"region:us"
]
| 2023-11-19T07:34:09+00:00 | {"language": ["de"], "license": ["cc-by-nc-sa-3.0"], "multilinguality": ["monolingual"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification", "sentiment-classification"], "pretty_name": "FinancialPhrasebankGerman", "tags": ["finance"], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "neutral", "2": "positive"}}}}], "splits": [{"name": "train", "num_bytes": 422345, "num_examples": 2763}, {"name": "validation", "num_bytes": 51710, "num_examples": 344}, {"name": "test", "num_bytes": 55109, "num_examples": 346}], "download_size": 318382, "dataset_size": 529164}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-11-19T08:24:02+00:00 | [
"1307.5336"
]
| [
"de"
]
| TAGS
#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #multilinguality-monolingual #language-German #license-cc-by-nc-sa-3.0 #finance #arxiv-1307.5336 #region-us
|
# Dataset Card for German financial_phrasebank
## Dataset Description
### Dataset Summary
This datset is a German translation of the financial phrasebank of Malo et al. (2013) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with Deepl.
### Supported Tasks and Leaderboards
Sentiment Classification
### Languages
German
## Dataset Structure
### Data Instances
### Data Fields
- sentence: a tokenized line from the dataset
- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'
### Data Splits
The data is splitted in a train, test and validation set using stratified sampling:
- train (2763 observations)
- validation (344 observations)
- test (346 observations)
## Further Information
For further information regarding the source data or the annotation process, please look at the original paper or the original dataset.
## Licensing Information
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit URL
In particular, this license permits the free use of the data for non-commercial purposes.
If you are interested in commercial use of the data, please contact the authors of the original datset for an appropriate license:
- Pekka Malo
- Ankur Sinha
| [
"# Dataset Card for German financial_phrasebank",
"## Dataset Description",
"### Dataset Summary\n\nThis datset is a German translation of the financial phrasebank of Malo et al. (2013) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with Deepl.",
"### Supported Tasks and Leaderboards\n\nSentiment Classification",
"### Languages\n\nGerman",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'",
"### Data Splits\nThe data is splitted in a train, test and validation set using stratified sampling:\n- train (2763 observations)\n- validation (344 observations)\n- test (346 observations)",
"## Further Information\n\nFor further information regarding the source data or the annotation process, please look at the original paper or the original dataset.",
"## Licensing Information\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit URL\nIn particular, this license permits the free use of the data for non-commercial purposes.\n\nIf you are interested in commercial use of the data, please contact the authors of the original datset for an appropriate license:\n- Pekka Malo\n- Ankur Sinha"
]
| [
"TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #multilinguality-monolingual #language-German #license-cc-by-nc-sa-3.0 #finance #arxiv-1307.5336 #region-us \n",
"# Dataset Card for German financial_phrasebank",
"## Dataset Description",
"### Dataset Summary\n\nThis datset is a German translation of the financial phrasebank of Malo et al. (2013) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with Deepl.",
"### Supported Tasks and Leaderboards\n\nSentiment Classification",
"### Languages\n\nGerman",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'",
"### Data Splits\nThe data is splitted in a train, test and validation set using stratified sampling:\n- train (2763 observations)\n- validation (344 observations)\n- test (346 observations)",
"## Further Information\n\nFor further information regarding the source data or the annotation process, please look at the original paper or the original dataset.",
"## Licensing Information\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit URL\nIn particular, this license permits the free use of the data for non-commercial purposes.\n\nIf you are interested in commercial use of the data, please contact the authors of the original datset for an appropriate license:\n- Pekka Malo\n- Ankur Sinha"
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"passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #task_ids-sentiment-classification #multilinguality-monolingual #language-German #license-cc-by-nc-sa-3.0 #finance #arxiv-1307.5336 #region-us \n# Dataset Card for German financial_phrasebank## Dataset Description### Dataset Summary\n\nThis datset is a German translation of the financial phrasebank of Malo et al. (2013) with a minimum agreement rate between annotators of 75% (3453 observations in total). The translation was mechanically accomplished with Deepl.### Supported Tasks and Leaderboards\n\nSentiment Classification### Languages\n\nGerman## Dataset Structure### Data Instances### Data Fields\n\n- sentence: a tokenized line from the dataset\n- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral'### Data Splits\nThe data is splitted in a train, test and validation set using stratified sampling:\n- train (2763 observations)\n- validation (344 observations)\n- test (346 observations)## Further Information\n\nFor further information regarding the source data or the annotation process, please look at the original paper or the original dataset.## Licensing Information\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit URL\nIn particular, this license permits the free use of the data for non-commercial purposes.\n\nIf you are interested in commercial use of the data, please contact the authors of the original datset for an appropriate license:\n- Pekka Malo\n- Ankur Sinha"
]
|
ec9160a9dc0618f37fac9d07fc885747ca86f1f7 | # Dataset Card for "ark-atk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | keylazy/ark-atk | [
"region:us"
]
| 2023-11-19T07:57:31+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text1", "dtype": "string"}, {"name": "text2", "dtype": "string"}, {"name": "tgt_input_ids", "sequence": "int64"}, {"name": "tgt_attention_mask", "sequence": "int64"}, {"name": "lm_input_ids", "sequence": "int64"}, {"name": "lm_attention_mask", "sequence": "int64"}, {"name": "atk_input_ids", "sequence": "int64"}, {"name": "atk_attention_mask", "sequence": "int64"}, {"name": "atk_token_type_ids", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7470489671, "num_examples": 1000000}], "download_size": 557393595, "dataset_size": 7470489671}} | 2023-11-19T08:10:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ark-atk"
More Information needed | [
"# Dataset Card for \"ark-atk\"\n\nMore Information needed"
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|
7b6c5696a0b0a5739337f900a4d47e1ba7d8f023 | # Dataset Card for "diffusion.7.control_net"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ayan1988/diffusion.7.control_net | [
"region:us"
]
| 2023-11-19T08:25:21+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 453988831.0, "num_examples": 50000}], "download_size": 324957581, "dataset_size": 453988831.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-19T15:44:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "diffusion.7.control_net"
More Information needed | [
"# Dataset Card for \"diffusion.7.control_net\"\n\nMore Information needed"
]
| [
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|
a099c8ae351225671de6b16d0cd8827924126af9 | * Malaysia textbook for primary and secondary school
* Primary school textbook: [KSSR](https://www.ipendidikan.my/buku-teks-digital-kssr-tahun-1-hingga-6.html)
* Secondary school textbook: [KSSM](https://www.ipendidikan.my/koleksi-buku-teks-digital-asas-kssm.html)
* Link to dataset on [Huggingface](https://huggingface.co/datasets/haizad/malaysia-textbook/) | haizad/malaysia-textbook | [
"language:ar",
"language:ms",
"language:zh",
"language:en",
"language:ta",
"region:us"
]
| 2023-11-19T08:27:42+00:00 | {"language": ["ar", "ms", "zh", "en", "ta"]} | 2023-11-19T08:36:06+00:00 | []
| [
"ar",
"ms",
"zh",
"en",
"ta"
]
| TAGS
#language-Arabic #language-Malay (macrolanguage) #language-Chinese #language-English #language-Tamil #region-us
| * Malaysia textbook for primary and secondary school
* Primary school textbook: KSSR
* Secondary school textbook: KSSM
* Link to dataset on Huggingface | []
| [
"TAGS\n#language-Arabic #language-Malay (macrolanguage) #language-Chinese #language-English #language-Tamil #region-us \n"
]
| [
34
]
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|
c24a4746b4cbfdf1ca87a47e7c93db00cfb87010 | # Bottega Veneta web scraped data
## About the website
**Bottega Veneta** operates in the **luxury fashion industry** in Asia Pacific, where numerous high-end brands engage in fierce competition to capture the attention and spending capabilities of affluent consumers. In particular, South Korea stands as a notable hub for luxury fashion, underpinned by growing economic affluence, sophisticated consumers, and the cultural wave commonly referred to as the "Hallyu Wave". The market is heavily influenced by digital platforms, with the **ecommerce sector** playing a substantial role. The dataset on hand provides detailed insights into the **Ecommerce product-list page (PLP) data** of Bottega Veneta in South Korea, offering valuable cues about their market positioning and performance in this dynamic landscape.
## Link to **dataset**
[South Korea - Bottega Veneta - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Bottega%20Veneta%20Product-prices%20South%20Korea/r/rec7XB9ZpvrruDvJ6)
| DBQ/Bottega.Veneta.Product.prices.South.Korea | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Bottega Veneta",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:31:53+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "South Korea - Bottega Veneta - Product-level price list", "tags": ["webscraping", "ecommerce", "Bottega Veneta", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2034097, "num_examples": 4463}], "download_size": 591026, "dataset_size": 2034097}} | 2023-11-19T08:31:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Bottega Veneta #fashion #fashion product #image #fashion image #region-us
| # Bottega Veneta web scraped data
## About the website
Bottega Veneta operates in the luxury fashion industry in Asia Pacific, where numerous high-end brands engage in fierce competition to capture the attention and spending capabilities of affluent consumers. In particular, South Korea stands as a notable hub for luxury fashion, underpinned by growing economic affluence, sophisticated consumers, and the cultural wave commonly referred to as the "Hallyu Wave". The market is heavily influenced by digital platforms, with the ecommerce sector playing a substantial role. The dataset on hand provides detailed insights into the Ecommerce product-list page (PLP) data of Bottega Veneta in South Korea, offering valuable cues about their market positioning and performance in this dynamic landscape.
## Link to dataset
South Korea - Bottega Veneta - Product-level price list dataset
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"## About the website\n\nBottega Veneta operates in the luxury fashion industry in Asia Pacific, where numerous high-end brands engage in fierce competition to capture the attention and spending capabilities of affluent consumers. In particular, South Korea stands as a notable hub for luxury fashion, underpinned by growing economic affluence, sophisticated consumers, and the cultural wave commonly referred to as the \"Hallyu Wave\". The market is heavily influenced by digital platforms, with the ecommerce sector playing a substantial role. The dataset on hand provides detailed insights into the Ecommerce product-list page (PLP) data of Bottega Veneta in South Korea, offering valuable cues about their market positioning and performance in this dynamic landscape.",
"## Link to dataset\n\nSouth Korea - Bottega Veneta - Product-level price list dataset"
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"# Bottega Veneta web scraped data",
"## About the website\n\nBottega Veneta operates in the luxury fashion industry in Asia Pacific, where numerous high-end brands engage in fierce competition to capture the attention and spending capabilities of affluent consumers. In particular, South Korea stands as a notable hub for luxury fashion, underpinned by growing economic affluence, sophisticated consumers, and the cultural wave commonly referred to as the \"Hallyu Wave\". The market is heavily influenced by digital platforms, with the ecommerce sector playing a substantial role. The dataset on hand provides detailed insights into the Ecommerce product-list page (PLP) data of Bottega Veneta in South Korea, offering valuable cues about their market positioning and performance in this dynamic landscape.",
"## Link to dataset\n\nSouth Korea - Bottega Veneta - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Bottega Veneta #fashion #fashion product #image #fashion image #region-us \n# Bottega Veneta web scraped data## About the website\n\nBottega Veneta operates in the luxury fashion industry in Asia Pacific, where numerous high-end brands engage in fierce competition to capture the attention and spending capabilities of affluent consumers. In particular, South Korea stands as a notable hub for luxury fashion, underpinned by growing economic affluence, sophisticated consumers, and the cultural wave commonly referred to as the \"Hallyu Wave\". The market is heavily influenced by digital platforms, with the ecommerce sector playing a substantial role. The dataset on hand provides detailed insights into the Ecommerce product-list page (PLP) data of Bottega Veneta in South Korea, offering valuable cues about their market positioning and performance in this dynamic landscape.## Link to dataset\n\nSouth Korea - Bottega Veneta - Product-level price list dataset"
]
|
1b56bfdf41a0528c8c820adff1a1cf0d730c7b68 | # Prada web scraped data
## About the website
Prada operates in the luxury fashion industry, a sector which embodies high-end clothing, footwear, and accessories. This industry has seen robust growth across the EMEA region, with Spain playing a significant role in this expansion. The competitive landscape is marked by both local Spanish designers and major international brands like **Prada**. The ever-increasing digital presence is notable, with a wealth of **Ecommerce product-list page (PLP) data** available. This data reveals exciting insights into consumer preferences, sales trends, and buying behaviors. Ecommerce is key to widening access to luxury fashion, making **Prada in Spain** even more noteworthy in this advancing digital age.
## Link to **dataset**
[Spain - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Spain/r/recq1QlhDyI2F1rap)
| DBQ/Prada.Product.prices.Spain | [
"task_categories:text-classification",
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| 2023-11-19T08:33:00+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Spain - Prada - Product-level price list", "tags": ["webscraping", "ecommerce", "Prada", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1271627, "num_examples": 2528}], "download_size": 382577, "dataset_size": 1271627}} | 2023-11-19T08:33:06+00:00 | []
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| TAGS
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| # Prada web scraped data
## About the website
Prada operates in the luxury fashion industry, a sector which embodies high-end clothing, footwear, and accessories. This industry has seen robust growth across the EMEA region, with Spain playing a significant role in this expansion. The competitive landscape is marked by both local Spanish designers and major international brands like Prada. The ever-increasing digital presence is notable, with a wealth of Ecommerce product-list page (PLP) data available. This data reveals exciting insights into consumer preferences, sales trends, and buying behaviors. Ecommerce is key to widening access to luxury fashion, making Prada in Spain even more noteworthy in this advancing digital age.
## Link to dataset
Spain - Prada - Product-level price list dataset
| [
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"## About the website\n\nPrada operates in the luxury fashion industry, a sector which embodies high-end clothing, footwear, and accessories. This industry has seen robust growth across the EMEA region, with Spain playing a significant role in this expansion. The competitive landscape is marked by both local Spanish designers and major international brands like Prada. The ever-increasing digital presence is notable, with a wealth of Ecommerce product-list page (PLP) data available. This data reveals exciting insights into consumer preferences, sales trends, and buying behaviors. Ecommerce is key to widening access to luxury fashion, making Prada in Spain even more noteworthy in this advancing digital age.",
"## Link to dataset\n\nSpain - Prada - Product-level price list dataset"
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"# Prada web scraped data",
"## About the website\n\nPrada operates in the luxury fashion industry, a sector which embodies high-end clothing, footwear, and accessories. This industry has seen robust growth across the EMEA region, with Spain playing a significant role in this expansion. The competitive landscape is marked by both local Spanish designers and major international brands like Prada. The ever-increasing digital presence is notable, with a wealth of Ecommerce product-list page (PLP) data available. This data reveals exciting insights into consumer preferences, sales trends, and buying behaviors. Ecommerce is key to widening access to luxury fashion, making Prada in Spain even more noteworthy in this advancing digital age.",
"## Link to dataset\n\nSpain - Prada - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us \n# Prada web scraped data## About the website\n\nPrada operates in the luxury fashion industry, a sector which embodies high-end clothing, footwear, and accessories. This industry has seen robust growth across the EMEA region, with Spain playing a significant role in this expansion. The competitive landscape is marked by both local Spanish designers and major international brands like Prada. The ever-increasing digital presence is notable, with a wealth of Ecommerce product-list page (PLP) data available. This data reveals exciting insights into consumer preferences, sales trends, and buying behaviors. Ecommerce is key to widening access to luxury fashion, making Prada in Spain even more noteworthy in this advancing digital age.## Link to dataset\n\nSpain - Prada - Product-level price list dataset"
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|
1fdab579e9acacf7a0f2d96b9d88a1ca4684516b | # Prada web scraped data
## About the website
The **Luxury Fashion Industry** in the EMEA region, particularly in **Sweden**, is a thriving market with high demand for exclusive and high-end products. **Prada**, a renowned player in this industry, holds a significant presence. The industry is currently experiencing a significant shift towards **digitalization** and **online retail**, also known as **Ecommerce**, fueled by changing consumer behaviors and advancements in technology. A concrete example is the analysis of **Ecommerce product-list page (PLP) data on Prada** in Sweden, which provides valuable insights into consumer preferences, purchasing power, and market trends, ultimately supporting decision-making processes. The dataset observed has shown the intricate correlation between consumer behavior and online retail success for luxury brands.
## Link to **dataset**
[Sweden - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Sweden/r/recyFcsPATKx39Zip)
| DBQ/Prada.Product.prices.Sweden | [
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| 2023-11-19T08:33:21+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Sweden - Prada - Product-level price list", "tags": ["webscraping", "ecommerce", "Prada", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1284630, "num_examples": 2548}], "download_size": 389265, "dataset_size": 1284630}} | 2023-11-19T08:33:25+00:00 | []
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| TAGS
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| # Prada web scraped data
## About the website
The Luxury Fashion Industry in the EMEA region, particularly in Sweden, is a thriving market with high demand for exclusive and high-end products. Prada, a renowned player in this industry, holds a significant presence. The industry is currently experiencing a significant shift towards digitalization and online retail, also known as Ecommerce, fueled by changing consumer behaviors and advancements in technology. A concrete example is the analysis of Ecommerce product-list page (PLP) data on Prada in Sweden, which provides valuable insights into consumer preferences, purchasing power, and market trends, ultimately supporting decision-making processes. The dataset observed has shown the intricate correlation between consumer behavior and online retail success for luxury brands.
## Link to dataset
Sweden - Prada - Product-level price list dataset
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"## About the website\n\nThe Luxury Fashion Industry in the EMEA region, particularly in Sweden, is a thriving market with high demand for exclusive and high-end products. Prada, a renowned player in this industry, holds a significant presence. The industry is currently experiencing a significant shift towards digitalization and online retail, also known as Ecommerce, fueled by changing consumer behaviors and advancements in technology. A concrete example is the analysis of Ecommerce product-list page (PLP) data on Prada in Sweden, which provides valuable insights into consumer preferences, purchasing power, and market trends, ultimately supporting decision-making processes. The dataset observed has shown the intricate correlation between consumer behavior and online retail success for luxury brands.",
"## Link to dataset\n\nSweden - Prada - Product-level price list dataset"
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"## About the website\n\nThe Luxury Fashion Industry in the EMEA region, particularly in Sweden, is a thriving market with high demand for exclusive and high-end products. Prada, a renowned player in this industry, holds a significant presence. The industry is currently experiencing a significant shift towards digitalization and online retail, also known as Ecommerce, fueled by changing consumer behaviors and advancements in technology. A concrete example is the analysis of Ecommerce product-list page (PLP) data on Prada in Sweden, which provides valuable insights into consumer preferences, purchasing power, and market trends, ultimately supporting decision-making processes. The dataset observed has shown the intricate correlation between consumer behavior and online retail success for luxury brands.",
"## Link to dataset\n\nSweden - Prada - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us \n# Prada web scraped data## About the website\n\nThe Luxury Fashion Industry in the EMEA region, particularly in Sweden, is a thriving market with high demand for exclusive and high-end products. Prada, a renowned player in this industry, holds a significant presence. The industry is currently experiencing a significant shift towards digitalization and online retail, also known as Ecommerce, fueled by changing consumer behaviors and advancements in technology. A concrete example is the analysis of Ecommerce product-list page (PLP) data on Prada in Sweden, which provides valuable insights into consumer preferences, purchasing power, and market trends, ultimately supporting decision-making processes. The dataset observed has shown the intricate correlation between consumer behavior and online retail success for luxury brands.## Link to dataset\n\nSweden - Prada - Product-level price list dataset"
]
|
4c1625f19b0caaf6f294e2ee2da21f94c22b1941 | # Prada web scraped data
## About the website
The **fashion industry in EMEA**, particularly in **Italy**, is long-standing and globally respected, with prestigious fashion houses and excellent craftsmanship. One of the leading fashion brands in Italy is **Prada**, an iconic name synonymous with luxury and style. Prada operates in a competitive space characterized by innovative design, high-quality materials, and cultivating desirability through brand prestige. Lately, the fashion industry, including Prada, has been increasingly moving towards the digital space. With the surge in online shopping trends, **Ecommerce** has become increasingly relevant. The dataset observed provides insights from **Ecommerce product-list page (PLP) data** specific to the Prada brand in the Italian market.
## Link to **dataset**
[Italy - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Italy/r/recUq5K9dC8eYLDss)
| DBQ/Prada.Product.prices.Italy | [
"task_categories:text-classification",
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"task_categories:object-detection",
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| 2023-11-19T08:33:33+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Italy - Prada - Product-level price list", "tags": ["webscraping", "ecommerce", "Prada", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1274261, "num_examples": 2533}], "download_size": 364017, "dataset_size": 1274261}} | 2023-11-19T08:33:38+00:00 | []
| [
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| TAGS
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| # Prada web scraped data
## About the website
The fashion industry in EMEA, particularly in Italy, is long-standing and globally respected, with prestigious fashion houses and excellent craftsmanship. One of the leading fashion brands in Italy is Prada, an iconic name synonymous with luxury and style. Prada operates in a competitive space characterized by innovative design, high-quality materials, and cultivating desirability through brand prestige. Lately, the fashion industry, including Prada, has been increasingly moving towards the digital space. With the surge in online shopping trends, Ecommerce has become increasingly relevant. The dataset observed provides insights from Ecommerce product-list page (PLP) data specific to the Prada brand in the Italian market.
## Link to dataset
Italy - Prada - Product-level price list dataset
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"## About the website\n\nThe fashion industry in EMEA, particularly in Italy, is long-standing and globally respected, with prestigious fashion houses and excellent craftsmanship. One of the leading fashion brands in Italy is Prada, an iconic name synonymous with luxury and style. Prada operates in a competitive space characterized by innovative design, high-quality materials, and cultivating desirability through brand prestige. Lately, the fashion industry, including Prada, has been increasingly moving towards the digital space. With the surge in online shopping trends, Ecommerce has become increasingly relevant. The dataset observed provides insights from Ecommerce product-list page (PLP) data specific to the Prada brand in the Italian market.",
"## Link to dataset\n\nItaly - Prada - Product-level price list dataset"
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"## Link to dataset\n\nItaly - Prada - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us \n# Prada web scraped data## About the website\n\nThe fashion industry in EMEA, particularly in Italy, is long-standing and globally respected, with prestigious fashion houses and excellent craftsmanship. One of the leading fashion brands in Italy is Prada, an iconic name synonymous with luxury and style. Prada operates in a competitive space characterized by innovative design, high-quality materials, and cultivating desirability through brand prestige. Lately, the fashion industry, including Prada, has been increasingly moving towards the digital space. With the surge in online shopping trends, Ecommerce has become increasingly relevant. The dataset observed provides insights from Ecommerce product-list page (PLP) data specific to the Prada brand in the Italian market.## Link to dataset\n\nItaly - Prada - Product-level price list dataset"
]
|
b2eddf98de3c2824505e519d8c247981744dfc73 | # Net-a-Porter web scraped data
## About the website
The **Ecommerce fashion industry** in the **Asia Pacific** region, particularly in **Singapore**, is rapidly evolving with numerous avenues opening up for luxury fashion retailers such as **Net-a-Porter**. The city-state presents a huge potential market with its affluent consumer base and high internet penetration rate. It is considered a fashion-literate market with a strong inclination towards high-end labels. The increase in digital transformation and uptake of online shopping trends have given an additional push to the luxury ecommerce sector here. The dataset in focus carries **Ecommerce product-list page (PLP) data** on **Net-a-Porter** operations within Singapore, providing valuable insights into customer preferences and buying behaviors in this market.
## Link to **dataset**
[Singapore - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Singapore/r/recJO3YUjaf8BAyJ4)
| DBQ/Net.a.Porter.Product.prices.Singapore | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
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"multilinguality:monolingual",
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"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:33:58+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Singapore - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20913147, "num_examples": 51236}], "download_size": 6382332, "dataset_size": 20913147}} | 2023-11-19T08:34:06+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The Ecommerce fashion industry in the Asia Pacific region, particularly in Singapore, is rapidly evolving with numerous avenues opening up for luxury fashion retailers such as Net-a-Porter. The city-state presents a huge potential market with its affluent consumer base and high internet penetration rate. It is considered a fashion-literate market with a strong inclination towards high-end labels. The increase in digital transformation and uptake of online shopping trends have given an additional push to the luxury ecommerce sector here. The dataset in focus carries Ecommerce product-list page (PLP) data on Net-a-Porter operations within Singapore, providing valuable insights into customer preferences and buying behaviors in this market.
## Link to dataset
Singapore - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce fashion industry in the Asia Pacific region, particularly in Singapore, is rapidly evolving with numerous avenues opening up for luxury fashion retailers such as Net-a-Porter. The city-state presents a huge potential market with its affluent consumer base and high internet penetration rate. It is considered a fashion-literate market with a strong inclination towards high-end labels. The increase in digital transformation and uptake of online shopping trends have given an additional push to the luxury ecommerce sector here. The dataset in focus carries Ecommerce product-list page (PLP) data on Net-a-Porter operations within Singapore, providing valuable insights into customer preferences and buying behaviors in this market.",
"## Link to dataset\n\nSingapore - Net-a-Porter - Product-level price list dataset"
]
| [
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"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce fashion industry in the Asia Pacific region, particularly in Singapore, is rapidly evolving with numerous avenues opening up for luxury fashion retailers such as Net-a-Porter. The city-state presents a huge potential market with its affluent consumer base and high internet penetration rate. It is considered a fashion-literate market with a strong inclination towards high-end labels. The increase in digital transformation and uptake of online shopping trends have given an additional push to the luxury ecommerce sector here. The dataset in focus carries Ecommerce product-list page (PLP) data on Net-a-Porter operations within Singapore, providing valuable insights into customer preferences and buying behaviors in this market.",
"## Link to dataset\n\nSingapore - Net-a-Porter - Product-level price list dataset"
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe Ecommerce fashion industry in the Asia Pacific region, particularly in Singapore, is rapidly evolving with numerous avenues opening up for luxury fashion retailers such as Net-a-Porter. The city-state presents a huge potential market with its affluent consumer base and high internet penetration rate. It is considered a fashion-literate market with a strong inclination towards high-end labels. The increase in digital transformation and uptake of online shopping trends have given an additional push to the luxury ecommerce sector here. The dataset in focus carries Ecommerce product-list page (PLP) data on Net-a-Porter operations within Singapore, providing valuable insights into customer preferences and buying behaviors in this market.## Link to dataset\n\nSingapore - Net-a-Porter - Product-level price list dataset"
]
|
40453ddc353106e4cdbf028d8f4697c1035dc717 | # Dior web scraped data
## About the website
The **luxury fashion industry** is a prominent sector in the **Asia Pacific region**, particularly in **Australia**. This industry features numerous global brands, with **Dior** being one of the most popular. Over the years, Australia has seen drastic growth in high-end fashion demand, driven by increasingly affluent consumers desiring international luxury labels. Ecommerce has significantly facilitated this growth. The dataset we observed includes **Ecommerce product-list page (PLP) data** on Dior in Australia. This data provides insights into Diors online product offerings, consumer interests, and purchasing habits in the Australian market.
## Link to **dataset**
[Australia - Dior - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Dior%20Product-prices%20Australia/r/recw4dCGHCI9eSFD8)
| DBQ/Dior.Product.prices.Australia | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
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"webscraping",
"ecommerce",
"Dior",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:34:24+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Australia - Dior - Product-level price list", "tags": ["webscraping", "ecommerce", "Dior", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1434453, "num_examples": 3644}], "download_size": 414510, "dataset_size": 1434453}} | 2023-11-19T08:34:29+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Dior #fashion #fashion product #image #fashion image #region-us
| # Dior web scraped data
## About the website
The luxury fashion industry is a prominent sector in the Asia Pacific region, particularly in Australia. This industry features numerous global brands, with Dior being one of the most popular. Over the years, Australia has seen drastic growth in high-end fashion demand, driven by increasingly affluent consumers desiring international luxury labels. Ecommerce has significantly facilitated this growth. The dataset we observed includes Ecommerce product-list page (PLP) data on Dior in Australia. This data provides insights into Diors online product offerings, consumer interests, and purchasing habits in the Australian market.
## Link to dataset
Australia - Dior - Product-level price list dataset
| [
"# Dior web scraped data",
"## About the website\n\nThe luxury fashion industry is a prominent sector in the Asia Pacific region, particularly in Australia. This industry features numerous global brands, with Dior being one of the most popular. Over the years, Australia has seen drastic growth in high-end fashion demand, driven by increasingly affluent consumers desiring international luxury labels. Ecommerce has significantly facilitated this growth. The dataset we observed includes Ecommerce product-list page (PLP) data on Dior in Australia. This data provides insights into Diors online product offerings, consumer interests, and purchasing habits in the Australian market.",
"## Link to dataset\n\nAustralia - Dior - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Dior #fashion #fashion product #image #fashion image #region-us \n",
"# Dior web scraped data",
"## About the website\n\nThe luxury fashion industry is a prominent sector in the Asia Pacific region, particularly in Australia. This industry features numerous global brands, with Dior being one of the most popular. Over the years, Australia has seen drastic growth in high-end fashion demand, driven by increasingly affluent consumers desiring international luxury labels. Ecommerce has significantly facilitated this growth. The dataset we observed includes Ecommerce product-list page (PLP) data on Dior in Australia. This data provides insights into Diors online product offerings, consumer interests, and purchasing habits in the Australian market.",
"## Link to dataset\n\nAustralia - Dior - Product-level price list dataset"
]
| [
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Dior #fashion #fashion product #image #fashion image #region-us \n# Dior web scraped data## About the website\n\nThe luxury fashion industry is a prominent sector in the Asia Pacific region, particularly in Australia. This industry features numerous global brands, with Dior being one of the most popular. Over the years, Australia has seen drastic growth in high-end fashion demand, driven by increasingly affluent consumers desiring international luxury labels. Ecommerce has significantly facilitated this growth. The dataset we observed includes Ecommerce product-list page (PLP) data on Dior in Australia. This data provides insights into Diors online product offerings, consumer interests, and purchasing habits in the Australian market.## Link to dataset\n\nAustralia - Dior - Product-level price list dataset"
]
|
bde4a3100b69ab6e140fce278c8f9a0fb79bcacd | # Gucci web scraped data
## About the website
In the EMEA region, particularly in **Qatar**, the luxury fashion industry is thriving and demonstrating significant growth. The Italian luxury fashion brand, **Gucci**, is a major player in this industry. A crucial component of its business model and regional success is the **e-commerce sector**, which is rapidly gaining popularity among consumers. E-commerce platforms provide an important avenue for global brands to reach local consumers seamlessly and efficiently, thus driving their sales. The dataset under consideration offers valuable insights into the **Ecommerce product-list page (PLP) data on Gucci** in this wealthy Middle Eastern nation. It reveals the brands online performance, product catalogue, pricing strategy, and more.
## Link to **dataset**
[Qatar - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Qatar/r/recI7K2eHwzEhEyNk)
| DBQ/Gucci.Product.prices.Qatar | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
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"multilinguality:monolingual",
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"language:en",
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"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:34:37+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Qatar - Gucci - Product-level price list", "tags": ["webscraping", "ecommerce", "Gucci", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2414601, "num_examples": 5067}], "download_size": 719886, "dataset_size": 2414601}} | 2023-11-19T08:34:43+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us
| # Gucci web scraped data
## About the website
In the EMEA region, particularly in Qatar, the luxury fashion industry is thriving and demonstrating significant growth. The Italian luxury fashion brand, Gucci, is a major player in this industry. A crucial component of its business model and regional success is the e-commerce sector, which is rapidly gaining popularity among consumers. E-commerce platforms provide an important avenue for global brands to reach local consumers seamlessly and efficiently, thus driving their sales. The dataset under consideration offers valuable insights into the Ecommerce product-list page (PLP) data on Gucci in this wealthy Middle Eastern nation. It reveals the brands online performance, product catalogue, pricing strategy, and more.
## Link to dataset
Qatar - Gucci - Product-level price list dataset
| [
"# Gucci web scraped data",
"## About the website\n\nIn the EMEA region, particularly in Qatar, the luxury fashion industry is thriving and demonstrating significant growth. The Italian luxury fashion brand, Gucci, is a major player in this industry. A crucial component of its business model and regional success is the e-commerce sector, which is rapidly gaining popularity among consumers. E-commerce platforms provide an important avenue for global brands to reach local consumers seamlessly and efficiently, thus driving their sales. The dataset under consideration offers valuable insights into the Ecommerce product-list page (PLP) data on Gucci in this wealthy Middle Eastern nation. It reveals the brands online performance, product catalogue, pricing strategy, and more.",
"## Link to dataset\n\nQatar - Gucci - Product-level price list dataset"
]
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"# Gucci web scraped data",
"## About the website\n\nIn the EMEA region, particularly in Qatar, the luxury fashion industry is thriving and demonstrating significant growth. The Italian luxury fashion brand, Gucci, is a major player in this industry. A crucial component of its business model and regional success is the e-commerce sector, which is rapidly gaining popularity among consumers. E-commerce platforms provide an important avenue for global brands to reach local consumers seamlessly and efficiently, thus driving their sales. The dataset under consideration offers valuable insights into the Ecommerce product-list page (PLP) data on Gucci in this wealthy Middle Eastern nation. It reveals the brands online performance, product catalogue, pricing strategy, and more.",
"## Link to dataset\n\nQatar - Gucci - Product-level price list dataset"
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"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us \n# Gucci web scraped data## About the website\n\nIn the EMEA region, particularly in Qatar, the luxury fashion industry is thriving and demonstrating significant growth. The Italian luxury fashion brand, Gucci, is a major player in this industry. A crucial component of its business model and regional success is the e-commerce sector, which is rapidly gaining popularity among consumers. E-commerce platforms provide an important avenue for global brands to reach local consumers seamlessly and efficiently, thus driving their sales. The dataset under consideration offers valuable insights into the Ecommerce product-list page (PLP) data on Gucci in this wealthy Middle Eastern nation. It reveals the brands online performance, product catalogue, pricing strategy, and more.## Link to dataset\n\nQatar - Gucci - Product-level price list dataset"
]
|
4978549c870ea70d5fdc0e655ad877e84fdee939 | # Gucci web scraped data
## About the website
**Gucci** operates within the high-end **luxury fashion industry** in the **EMEA** region, particularly in **Bulgaria**. Bulgarias luxury market shows constant growth, driven by an increase in affluent tourists and a growing upper class with a taste for high-end fashion. With Guccis presence, the industry displays a substantial potential for further expansion. The dataset under consideration provides **Ecommerce product-list page (PLP) data on Gucci in Bulgaria**, offering valuable insights into product preferences, customer behavior, and market trends. This data is crucial for understanding the dynamic luxury fashion industry in Bulgaria and forming effective business strategies.
## Link to **dataset**
[Bulgaria - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Bulgaria/r/recAVa3OpTL3hE2Ow)
| DBQ/Gucci.Product.prices.Bulgaria | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
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]
| 2023-11-19T08:34:51+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Bulgaria - Gucci - Product-level price list", "tags": ["webscraping", "ecommerce", "Gucci", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2466564, "num_examples": 5170}], "download_size": 723057, "dataset_size": 2466564}} | 2023-11-19T08:34:56+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us
| # Gucci web scraped data
## About the website
Gucci operates within the high-end luxury fashion industry in the EMEA region, particularly in Bulgaria. Bulgarias luxury market shows constant growth, driven by an increase in affluent tourists and a growing upper class with a taste for high-end fashion. With Guccis presence, the industry displays a substantial potential for further expansion. The dataset under consideration provides Ecommerce product-list page (PLP) data on Gucci in Bulgaria, offering valuable insights into product preferences, customer behavior, and market trends. This data is crucial for understanding the dynamic luxury fashion industry in Bulgaria and forming effective business strategies.
## Link to dataset
Bulgaria - Gucci - Product-level price list dataset
| [
"# Gucci web scraped data",
"## About the website\n\nGucci operates within the high-end luxury fashion industry in the EMEA region, particularly in Bulgaria. Bulgarias luxury market shows constant growth, driven by an increase in affluent tourists and a growing upper class with a taste for high-end fashion. With Guccis presence, the industry displays a substantial potential for further expansion. The dataset under consideration provides Ecommerce product-list page (PLP) data on Gucci in Bulgaria, offering valuable insights into product preferences, customer behavior, and market trends. This data is crucial for understanding the dynamic luxury fashion industry in Bulgaria and forming effective business strategies.",
"## Link to dataset\n\nBulgaria - Gucci - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us \n",
"# Gucci web scraped data",
"## About the website\n\nGucci operates within the high-end luxury fashion industry in the EMEA region, particularly in Bulgaria. Bulgarias luxury market shows constant growth, driven by an increase in affluent tourists and a growing upper class with a taste for high-end fashion. With Guccis presence, the industry displays a substantial potential for further expansion. The dataset under consideration provides Ecommerce product-list page (PLP) data on Gucci in Bulgaria, offering valuable insights into product preferences, customer behavior, and market trends. This data is crucial for understanding the dynamic luxury fashion industry in Bulgaria and forming effective business strategies.",
"## Link to dataset\n\nBulgaria - Gucci - Product-level price list dataset"
]
| [
178,
7,
142,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us \n# Gucci web scraped data## About the website\n\nGucci operates within the high-end luxury fashion industry in the EMEA region, particularly in Bulgaria. Bulgarias luxury market shows constant growth, driven by an increase in affluent tourists and a growing upper class with a taste for high-end fashion. With Guccis presence, the industry displays a substantial potential for further expansion. The dataset under consideration provides Ecommerce product-list page (PLP) data on Gucci in Bulgaria, offering valuable insights into product preferences, customer behavior, and market trends. This data is crucial for understanding the dynamic luxury fashion industry in Bulgaria and forming effective business strategies.## Link to dataset\n\nBulgaria - Gucci - Product-level price list dataset"
]
|
5bca90dfb895c63a76bb8b79af772e4d3d8d6557 | # Net-a-Porter web scraped data
## About the website
The **e-commerce industry** in the **Asia Pacific** region is flourishing, with a distinctive leap in **Japan**. **Net-a-Porter**, a pilot retailer in the luxury fashion online retail industry, has made an intriguing impression. The **Japanese market**, known for its pioneering technology and advancement, is a hotbed for luxury fashion e-commerce platforms. Rapid digital transformation and high consumer acceptance of online shopping have only fast-tracked e-commerce growth in Japan. It has been observed that the dataset encompasses **Ecommerce product-list page (PLP) data** on **Net-a-Porter** in **Japan**, offering fruitful insights into the market trends, consumer preferences, and business strategies.
## Link to **dataset**
[Japan - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Japan/r/recfF82XrwpERvmrv)
| DBQ/Net.a.Porter.Product.prices.Japan | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
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"multilinguality:monolingual",
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"fashion",
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"image",
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"region:us"
]
| 2023-11-19T08:35:10+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Japan - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 20926211, "num_examples": 51262}], "download_size": 6397759, "dataset_size": 20926211}} | 2023-11-19T08:35:18+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The e-commerce industry in the Asia Pacific region is flourishing, with a distinctive leap in Japan. Net-a-Porter, a pilot retailer in the luxury fashion online retail industry, has made an intriguing impression. The Japanese market, known for its pioneering technology and advancement, is a hotbed for luxury fashion e-commerce platforms. Rapid digital transformation and high consumer acceptance of online shopping have only fast-tracked e-commerce growth in Japan. It has been observed that the dataset encompasses Ecommerce product-list page (PLP) data on Net-a-Porter in Japan, offering fruitful insights into the market trends, consumer preferences, and business strategies.
## Link to dataset
Japan - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe e-commerce industry in the Asia Pacific region is flourishing, with a distinctive leap in Japan. Net-a-Porter, a pilot retailer in the luxury fashion online retail industry, has made an intriguing impression. The Japanese market, known for its pioneering technology and advancement, is a hotbed for luxury fashion e-commerce platforms. Rapid digital transformation and high consumer acceptance of online shopping have only fast-tracked e-commerce growth in Japan. It has been observed that the dataset encompasses Ecommerce product-list page (PLP) data on Net-a-Porter in Japan, offering fruitful insights into the market trends, consumer preferences, and business strategies.",
"## Link to dataset\n\nJapan - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nThe e-commerce industry in the Asia Pacific region is flourishing, with a distinctive leap in Japan. Net-a-Porter, a pilot retailer in the luxury fashion online retail industry, has made an intriguing impression. The Japanese market, known for its pioneering technology and advancement, is a hotbed for luxury fashion e-commerce platforms. Rapid digital transformation and high consumer acceptance of online shopping have only fast-tracked e-commerce growth in Japan. It has been observed that the dataset encompasses Ecommerce product-list page (PLP) data on Net-a-Porter in Japan, offering fruitful insights into the market trends, consumer preferences, and business strategies.",
"## Link to dataset\n\nJapan - Net-a-Porter - Product-level price list dataset"
]
| [
177,
11,
163,
21
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe e-commerce industry in the Asia Pacific region is flourishing, with a distinctive leap in Japan. Net-a-Porter, a pilot retailer in the luxury fashion online retail industry, has made an intriguing impression. The Japanese market, known for its pioneering technology and advancement, is a hotbed for luxury fashion e-commerce platforms. Rapid digital transformation and high consumer acceptance of online shopping have only fast-tracked e-commerce growth in Japan. It has been observed that the dataset encompasses Ecommerce product-list page (PLP) data on Net-a-Porter in Japan, offering fruitful insights into the market trends, consumer preferences, and business strategies.## Link to dataset\n\nJapan - Net-a-Porter - Product-level price list dataset"
]
|
e8afd2af9c4261cbc6aacba4e50f0b6a447a8072 | # Prada web scraped data
## About the website
**Prada** operates in the high-end fashion industry in the **Asia Pacific** region, particularly in **Hong Kong**. This industry in Asia Pacific is notable for its rapid growth and great potential due to increasing income level and changing consumption behavior. Hong Kong is a prominent hub for luxury retail markets, which gives Prada a strategic advantage. With the rise of advanced technology and increasing use of digital platforms in this era, Prada has also adopted **Ecommerce** as one its business operations. The dataset observed has detailed **Ecommerce product-list page (PLP) data** on Pradas products and sales in Hong Kong, reflecting the brands performance and market trends.
## Link to **dataset**
[Hong Kong - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Hong%20Kong/r/recTtpWP1VKVUmHVp)
| DBQ/Prada.Product.prices.Hong.Kong | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
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"webscraping",
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"Prada",
"fashion",
"fashion product",
"image",
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"region:us"
]
| 2023-11-19T08:35:34+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hong Kong - Prada - Product-level price list", "tags": ["webscraping", "ecommerce", "Prada", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 838913, "num_examples": 1692}], "download_size": 234539, "dataset_size": 838913}} | 2023-11-19T08:35:39+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us
| # Prada web scraped data
## About the website
Prada operates in the high-end fashion industry in the Asia Pacific region, particularly in Hong Kong. This industry in Asia Pacific is notable for its rapid growth and great potential due to increasing income level and changing consumption behavior. Hong Kong is a prominent hub for luxury retail markets, which gives Prada a strategic advantage. With the rise of advanced technology and increasing use of digital platforms in this era, Prada has also adopted Ecommerce as one its business operations. The dataset observed has detailed Ecommerce product-list page (PLP) data on Pradas products and sales in Hong Kong, reflecting the brands performance and market trends.
## Link to dataset
Hong Kong - Prada - Product-level price list dataset
| [
"# Prada web scraped data",
"## About the website\n\nPrada operates in the high-end fashion industry in the Asia Pacific region, particularly in Hong Kong. This industry in Asia Pacific is notable for its rapid growth and great potential due to increasing income level and changing consumption behavior. Hong Kong is a prominent hub for luxury retail markets, which gives Prada a strategic advantage. With the rise of advanced technology and increasing use of digital platforms in this era, Prada has also adopted Ecommerce as one its business operations. The dataset observed has detailed Ecommerce product-list page (PLP) data on Pradas products and sales in Hong Kong, reflecting the brands performance and market trends.",
"## Link to dataset\n\nHong Kong - Prada - Product-level price list dataset"
]
| [
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"# Prada web scraped data",
"## About the website\n\nPrada operates in the high-end fashion industry in the Asia Pacific region, particularly in Hong Kong. This industry in Asia Pacific is notable for its rapid growth and great potential due to increasing income level and changing consumption behavior. Hong Kong is a prominent hub for luxury retail markets, which gives Prada a strategic advantage. With the rise of advanced technology and increasing use of digital platforms in this era, Prada has also adopted Ecommerce as one its business operations. The dataset observed has detailed Ecommerce product-list page (PLP) data on Pradas products and sales in Hong Kong, reflecting the brands performance and market trends.",
"## Link to dataset\n\nHong Kong - Prada - Product-level price list dataset"
]
| [
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145,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us \n# Prada web scraped data## About the website\n\nPrada operates in the high-end fashion industry in the Asia Pacific region, particularly in Hong Kong. This industry in Asia Pacific is notable for its rapid growth and great potential due to increasing income level and changing consumption behavior. Hong Kong is a prominent hub for luxury retail markets, which gives Prada a strategic advantage. With the rise of advanced technology and increasing use of digital platforms in this era, Prada has also adopted Ecommerce as one its business operations. The dataset observed has detailed Ecommerce product-list page (PLP) data on Pradas products and sales in Hong Kong, reflecting the brands performance and market trends.## Link to dataset\n\nHong Kong - Prada - Product-level price list dataset"
]
|
569ea4a002fd1ea52265d1524535dccba5657d53 | # Louis Vuitton web scraped data
## About the website
The **Luxury Fashion Industry** is a flourishing industry in the **Asia Pacific region**, particularly in countries like **Singapore**, known for its affluent consumer base. This industry observes leading fashion houses, such as **Louis Vuitton**, creating a significant footprint through a combination of physical boutiques and a robust **Ecommerce** presence. The competitive dynamics of this sphere are driven by high-end fashion trends, exclusivity, and premium pricing. The **Ecommerce product-list page (PLP) data** provide valuable insights about consumer preferences and behavior. These insights further influence the marketing strategies, thus shaping the overall growth of premium brands like Louis Vuitton in Singapores Luxury Fashion Industry.
## Link to **dataset**
[Singapore - Louis Vuitton - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Louis%20Vuitton%20Product-prices%20Singapore/r/rectQmgk2z4KcCwrz)
| DBQ/Louis.Vuitton.Product.prices.Singapore | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
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"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
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"ecommerce",
"Louis Vuitton",
"fashion",
"fashion product",
"image",
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"region:us"
]
| 2023-11-19T08:35:54+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Singapore - Louis Vuitton - Product-level price list", "tags": ["webscraping", "ecommerce", "Louis Vuitton", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2796943, "num_examples": 6576}], "download_size": 748428, "dataset_size": 2796943}} | 2023-11-19T08:35:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Louis Vuitton #fashion #fashion product #image #fashion image #region-us
| # Louis Vuitton web scraped data
## About the website
The Luxury Fashion Industry is a flourishing industry in the Asia Pacific region, particularly in countries like Singapore, known for its affluent consumer base. This industry observes leading fashion houses, such as Louis Vuitton, creating a significant footprint through a combination of physical boutiques and a robust Ecommerce presence. The competitive dynamics of this sphere are driven by high-end fashion trends, exclusivity, and premium pricing. The Ecommerce product-list page (PLP) data provide valuable insights about consumer preferences and behavior. These insights further influence the marketing strategies, thus shaping the overall growth of premium brands like Louis Vuitton in Singapores Luxury Fashion Industry.
## Link to dataset
Singapore - Louis Vuitton - Product-level price list dataset
| [
"# Louis Vuitton web scraped data",
"## About the website\n\nThe Luxury Fashion Industry is a flourishing industry in the Asia Pacific region, particularly in countries like Singapore, known for its affluent consumer base. This industry observes leading fashion houses, such as Louis Vuitton, creating a significant footprint through a combination of physical boutiques and a robust Ecommerce presence. The competitive dynamics of this sphere are driven by high-end fashion trends, exclusivity, and premium pricing. The Ecommerce product-list page (PLP) data provide valuable insights about consumer preferences and behavior. These insights further influence the marketing strategies, thus shaping the overall growth of premium brands like Louis Vuitton in Singapores Luxury Fashion Industry.",
"## Link to dataset\n\nSingapore - Louis Vuitton - Product-level price list dataset"
]
| [
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"# Louis Vuitton web scraped data",
"## About the website\n\nThe Luxury Fashion Industry is a flourishing industry in the Asia Pacific region, particularly in countries like Singapore, known for its affluent consumer base. This industry observes leading fashion houses, such as Louis Vuitton, creating a significant footprint through a combination of physical boutiques and a robust Ecommerce presence. The competitive dynamics of this sphere are driven by high-end fashion trends, exclusivity, and premium pricing. The Ecommerce product-list page (PLP) data provide valuable insights about consumer preferences and behavior. These insights further influence the marketing strategies, thus shaping the overall growth of premium brands like Louis Vuitton in Singapores Luxury Fashion Industry.",
"## Link to dataset\n\nSingapore - Louis Vuitton - Product-level price list dataset"
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178,
7,
148,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Louis Vuitton #fashion #fashion product #image #fashion image #region-us \n# Louis Vuitton web scraped data## About the website\n\nThe Luxury Fashion Industry is a flourishing industry in the Asia Pacific region, particularly in countries like Singapore, known for its affluent consumer base. This industry observes leading fashion houses, such as Louis Vuitton, creating a significant footprint through a combination of physical boutiques and a robust Ecommerce presence. The competitive dynamics of this sphere are driven by high-end fashion trends, exclusivity, and premium pricing. The Ecommerce product-list page (PLP) data provide valuable insights about consumer preferences and behavior. These insights further influence the marketing strategies, thus shaping the overall growth of premium brands like Louis Vuitton in Singapores Luxury Fashion Industry.## Link to dataset\n\nSingapore - Louis Vuitton - Product-level price list dataset"
]
|
23949e321fe38ad043e166db2a99b1de18cd1f9f | # Balenciaga web scraped data
## About the website
Balenciaga operates within the **luxury fashion industry** in the **Asia Pacific region**, specifically in **Hong Kong**, which is known for its strong demand for high-end fashion. As part of the global trend, the industry has shifted towards **Ecommerce**, which has been growing significantly in the past few years. The dataset observed includes **Ecommerce product-list page (PLP) data on Balenciaga in Hong Kong**, providing valuable insights into the market. **Product listings**, pricing, and availability data are examples of the information included. Other geographic details or types of data can be found on the [Balenciaga main page](https://www.databoutique.com/buy-data-list-subset/Balenciaga web scraped data/r/rec0EGCU96DEBdTOE).
## Link to **dataset**
[Hong Kong - Balenciaga - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Balenciaga%20Product-prices%20Hong%20Kong/r/recR8PANJMgN5obaw)
| DBQ/Balenciaga.Product.prices.Hong.Kong | [
"task_categories:text-classification",
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"task_categories:image-to-image",
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"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Balenciaga",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:36:13+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hong Kong - Balenciaga - Product-level price list", "tags": ["webscraping", "ecommerce", "Balenciaga", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 858709, "num_examples": 2307}], "download_size": 274910, "dataset_size": 858709}} | 2023-11-19T08:36:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Balenciaga #fashion #fashion product #image #fashion image #region-us
| # Balenciaga web scraped data
## About the website
Balenciaga operates within the luxury fashion industry in the Asia Pacific region, specifically in Hong Kong, which is known for its strong demand for high-end fashion. As part of the global trend, the industry has shifted towards Ecommerce, which has been growing significantly in the past few years. The dataset observed includes Ecommerce product-list page (PLP) data on Balenciaga in Hong Kong, providing valuable insights into the market. Product listings, pricing, and availability data are examples of the information included. Other geographic details or types of data can be found on the Balenciaga main page.
## Link to dataset
Hong Kong - Balenciaga - Product-level price list dataset
| [
"# Balenciaga web scraped data",
"## About the website\n\nBalenciaga operates within the luxury fashion industry in the Asia Pacific region, specifically in Hong Kong, which is known for its strong demand for high-end fashion. As part of the global trend, the industry has shifted towards Ecommerce, which has been growing significantly in the past few years. The dataset observed includes Ecommerce product-list page (PLP) data on Balenciaga in Hong Kong, providing valuable insights into the market. Product listings, pricing, and availability data are examples of the information included. Other geographic details or types of data can be found on the Balenciaga main page.",
"## Link to dataset\n\nHong Kong - Balenciaga - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Balenciaga #fashion #fashion product #image #fashion image #region-us \n",
"# Balenciaga web scraped data",
"## About the website\n\nBalenciaga operates within the luxury fashion industry in the Asia Pacific region, specifically in Hong Kong, which is known for its strong demand for high-end fashion. As part of the global trend, the industry has shifted towards Ecommerce, which has been growing significantly in the past few years. The dataset observed includes Ecommerce product-list page (PLP) data on Balenciaga in Hong Kong, providing valuable insights into the market. Product listings, pricing, and availability data are examples of the information included. Other geographic details or types of data can be found on the Balenciaga main page.",
"## Link to dataset\n\nHong Kong - Balenciaga - Product-level price list dataset"
]
| [
179,
8,
138,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Balenciaga #fashion #fashion product #image #fashion image #region-us \n# Balenciaga web scraped data## About the website\n\nBalenciaga operates within the luxury fashion industry in the Asia Pacific region, specifically in Hong Kong, which is known for its strong demand for high-end fashion. As part of the global trend, the industry has shifted towards Ecommerce, which has been growing significantly in the past few years. The dataset observed includes Ecommerce product-list page (PLP) data on Balenciaga in Hong Kong, providing valuable insights into the market. Product listings, pricing, and availability data are examples of the information included. Other geographic details or types of data can be found on the Balenciaga main page.## Link to dataset\n\nHong Kong - Balenciaga - Product-level price list dataset"
]
|
3b7af9c2d924997fc5e75d62533af1b86a6321c7 | # My Theresa web scraped data
## About the website
Observing the dataset, we gather detailed insights into the **Ecommerce** industry of the **EMEA** region, with a primary focus on **France**. Specifically, the Ecommerce domain in this country entails online transactional activities geared towards buying or selling goods and services. The industry has noted considerable growth owing to the increased digitization and emerging tech trends directing consumer interaction. **My Theresa**, a prominent player in this sector, operates with prominence in the high-end fashion retail aspect of Ecommerce. The dataset contains comprehensive **Ecommerce product-list page (PLP) data** on this player, delineating its operational metrics and strategic profile in France.
## Link to **dataset**
[France - My Theresa - Product-level price list dataset](https://www.databoutique.com/buy-data-page/My%20Theresa%20Product-prices%20France/r/recFmCsM3UDH5dtZT)
| DBQ/My.Theresa.Product.prices.France | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"My Theresa",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:36:36+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "France - My Theresa - Product-level price list", "tags": ["webscraping", "ecommerce", "My Theresa", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 33767177, "num_examples": 96985}], "download_size": 9819978, "dataset_size": 33767177}} | 2023-11-19T08:36:47+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #My Theresa #fashion #fashion product #image #fashion image #region-us
| # My Theresa web scraped data
## About the website
Observing the dataset, we gather detailed insights into the Ecommerce industry of the EMEA region, with a primary focus on France. Specifically, the Ecommerce domain in this country entails online transactional activities geared towards buying or selling goods and services. The industry has noted considerable growth owing to the increased digitization and emerging tech trends directing consumer interaction. My Theresa, a prominent player in this sector, operates with prominence in the high-end fashion retail aspect of Ecommerce. The dataset contains comprehensive Ecommerce product-list page (PLP) data on this player, delineating its operational metrics and strategic profile in France.
## Link to dataset
France - My Theresa - Product-level price list dataset
| [
"# My Theresa web scraped data",
"## About the website\n\nObserving the dataset, we gather detailed insights into the Ecommerce industry of the EMEA region, with a primary focus on France. Specifically, the Ecommerce domain in this country entails online transactional activities geared towards buying or selling goods and services. The industry has noted considerable growth owing to the increased digitization and emerging tech trends directing consumer interaction. My Theresa, a prominent player in this sector, operates with prominence in the high-end fashion retail aspect of Ecommerce. The dataset contains comprehensive Ecommerce product-list page (PLP) data on this player, delineating its operational metrics and strategic profile in France.",
"## Link to dataset\n\nFrance - My Theresa - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #My Theresa #fashion #fashion product #image #fashion image #region-us \n",
"# My Theresa web scraped data",
"## About the website\n\nObserving the dataset, we gather detailed insights into the Ecommerce industry of the EMEA region, with a primary focus on France. Specifically, the Ecommerce domain in this country entails online transactional activities geared towards buying or selling goods and services. The industry has noted considerable growth owing to the increased digitization and emerging tech trends directing consumer interaction. My Theresa, a prominent player in this sector, operates with prominence in the high-end fashion retail aspect of Ecommerce. The dataset contains comprehensive Ecommerce product-list page (PLP) data on this player, delineating its operational metrics and strategic profile in France.",
"## Link to dataset\n\nFrance - My Theresa - Product-level price list dataset"
]
| [
178,
7,
151,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #My Theresa #fashion #fashion product #image #fashion image #region-us \n# My Theresa web scraped data## About the website\n\nObserving the dataset, we gather detailed insights into the Ecommerce industry of the EMEA region, with a primary focus on France. Specifically, the Ecommerce domain in this country entails online transactional activities geared towards buying or selling goods and services. The industry has noted considerable growth owing to the increased digitization and emerging tech trends directing consumer interaction. My Theresa, a prominent player in this sector, operates with prominence in the high-end fashion retail aspect of Ecommerce. The dataset contains comprehensive Ecommerce product-list page (PLP) data on this player, delineating its operational metrics and strategic profile in France.## Link to dataset\n\nFrance - My Theresa - Product-level price list dataset"
]
|
a8b5c6f1cbb5bc895d2e960fb52112c935444de1 | # Net-a-Porter web scraped data
## About the website
The **Ecommerce industry** in the EMEA region, particularly in **Bulgaria**, is a rapidly growing sector due to increased internet penetration and adoption of digital shopping habits. **Net-a-Porter**, a notable player in this sector, operates in the luxury fashion ecommerce market. The company has a strong presence in Bulgaria, catering to a consumer base with a preference for signature and luxury fashion brands. The dataset observed includes **Ecommerce product-list page (PLP) data** on Net-a-Porter within Bulgaria. This data offers valuable insights into customer shopping behavior, purchase patterns, and preferences, which are crucial for strategizing and optimizing ecommerce operations in the region.
## Link to **dataset**
[Bulgaria - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Bulgaria/r/recgHEJtS0jL44luU)
| DBQ/Net.a.Porter.Product.prices.Bulgaria | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:37:04+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Bulgaria - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17315272, "num_examples": 42495}], "download_size": 5416362, "dataset_size": 17315272}} | 2023-11-19T08:37:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The Ecommerce industry in the EMEA region, particularly in Bulgaria, is a rapidly growing sector due to increased internet penetration and adoption of digital shopping habits. Net-a-Porter, a notable player in this sector, operates in the luxury fashion ecommerce market. The company has a strong presence in Bulgaria, catering to a consumer base with a preference for signature and luxury fashion brands. The dataset observed includes Ecommerce product-list page (PLP) data on Net-a-Porter within Bulgaria. This data offers valuable insights into customer shopping behavior, purchase patterns, and preferences, which are crucial for strategizing and optimizing ecommerce operations in the region.
## Link to dataset
Bulgaria - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Bulgaria, is a rapidly growing sector due to increased internet penetration and adoption of digital shopping habits. Net-a-Porter, a notable player in this sector, operates in the luxury fashion ecommerce market. The company has a strong presence in Bulgaria, catering to a consumer base with a preference for signature and luxury fashion brands. The dataset observed includes Ecommerce product-list page (PLP) data on Net-a-Porter within Bulgaria. This data offers valuable insights into customer shopping behavior, purchase patterns, and preferences, which are crucial for strategizing and optimizing ecommerce operations in the region.",
"## Link to dataset\n\nBulgaria - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Bulgaria, is a rapidly growing sector due to increased internet penetration and adoption of digital shopping habits. Net-a-Porter, a notable player in this sector, operates in the luxury fashion ecommerce market. The company has a strong presence in Bulgaria, catering to a consumer base with a preference for signature and luxury fashion brands. The dataset observed includes Ecommerce product-list page (PLP) data on Net-a-Porter within Bulgaria. This data offers valuable insights into customer shopping behavior, purchase patterns, and preferences, which are crucial for strategizing and optimizing ecommerce operations in the region.",
"## Link to dataset\n\nBulgaria - Net-a-Porter - Product-level price list dataset"
]
| [
177,
11,
153,
21
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Bulgaria, is a rapidly growing sector due to increased internet penetration and adoption of digital shopping habits. Net-a-Porter, a notable player in this sector, operates in the luxury fashion ecommerce market. The company has a strong presence in Bulgaria, catering to a consumer base with a preference for signature and luxury fashion brands. The dataset observed includes Ecommerce product-list page (PLP) data on Net-a-Porter within Bulgaria. This data offers valuable insights into customer shopping behavior, purchase patterns, and preferences, which are crucial for strategizing and optimizing ecommerce operations in the region.## Link to dataset\n\nBulgaria - Net-a-Porter - Product-level price list dataset"
]
|
907869ff68513a04987553b7bdafee365120d12c | # Saint Laurent web scraped data
## About the website
Saint Laurent operates within the **luxury fashion** industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the **United Kingdom**. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards **Ecommerce** sales, owing to customers’ increasing preference for online shopping. Within this dataset, we have obtained **Ecommerce product-list page (PLP) data** specifically for **Saint Laurent in the United Kingdom**, offering detailed insights into the brands online performance in this specific market.
## Link to **dataset**
[United Kingdom - Saint Laurent - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Product-prices%20United%20Kingdom/r/recNRYRcXAYJCmE6x)
| DBQ/Saint.Laurent.Product.prices.United.Kingdom | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Saint Laurent",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:37:29+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "United Kingdom - Saint Laurent - Product-level price list", "tags": ["webscraping", "ecommerce", "Saint Laurent", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1235575, "num_examples": 3063}], "download_size": 376994, "dataset_size": 1235575}} | 2023-11-19T08:37:34+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Saint Laurent #fashion #fashion product #image #fashion image #region-us
| # Saint Laurent web scraped data
## About the website
Saint Laurent operates within the luxury fashion industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the United Kingdom. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards Ecommerce sales, owing to customers’ increasing preference for online shopping. Within this dataset, we have obtained Ecommerce product-list page (PLP) data specifically for Saint Laurent in the United Kingdom, offering detailed insights into the brands online performance in this specific market.
## Link to dataset
United Kingdom - Saint Laurent - Product-level price list dataset
| [
"# Saint Laurent web scraped data",
"## About the website\n\nSaint Laurent operates within the luxury fashion industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the United Kingdom. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards Ecommerce sales, owing to customers’ increasing preference for online shopping. Within this dataset, we have obtained Ecommerce product-list page (PLP) data specifically for Saint Laurent in the United Kingdom, offering detailed insights into the brands online performance in this specific market.",
"## Link to dataset\n\nUnited Kingdom - Saint Laurent - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Saint Laurent #fashion #fashion product #image #fashion image #region-us \n",
"# Saint Laurent web scraped data",
"## About the website\n\nSaint Laurent operates within the luxury fashion industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the United Kingdom. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards Ecommerce sales, owing to customers’ increasing preference for online shopping. Within this dataset, we have obtained Ecommerce product-list page (PLP) data specifically for Saint Laurent in the United Kingdom, offering detailed insights into the brands online performance in this specific market.",
"## Link to dataset\n\nUnited Kingdom - Saint Laurent - Product-level price list dataset"
]
| [
178,
7,
134,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Saint Laurent #fashion #fashion product #image #fashion image #region-us \n# Saint Laurent web scraped data## About the website\n\nSaint Laurent operates within the luxury fashion industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the United Kingdom. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards Ecommerce sales, owing to customers’ increasing preference for online shopping. Within this dataset, we have obtained Ecommerce product-list page (PLP) data specifically for Saint Laurent in the United Kingdom, offering detailed insights into the brands online performance in this specific market.## Link to dataset\n\nUnited Kingdom - Saint Laurent - Product-level price list dataset"
]
|
8336a65d70de8e92d7279196b1d5a045ba7dc28b | # Balenciaga web scraped data
## About the website
The **fashion industry** in the **Asia Pacific** region, particularly in **China**, is a hotbed of activity. It is one of the most lucrative markets in the world, spurred by a fast-growing middle class with an increased appetite for luxury products. The Chinese market, especially, plays host to many high-end, luxury fashion brands like **Balenciaga**. A significant transition has been noted in the mode of shopping, with a sharp turn towards **Ecommerce**. The dataset represents **Ecommerce product-list page (PLP) data** specific to Balenciagas online marketplace in China, highlighting the extensive variety of products offered by this luxury fashion house in the booming Chinese digital market.
## Link to **dataset**
[China - Balenciaga - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Balenciaga%20Product-prices%20China/r/recUPih9uOFY6nzNC)
| DBQ/Balenciaga.Product.prices.China | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Balenciaga",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:37:48+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "China - Balenciaga - Product-level price list", "tags": ["webscraping", "ecommerce", "Balenciaga", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 619495, "num_examples": 1944}], "download_size": 176304, "dataset_size": 619495}} | 2023-11-19T08:37:52+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Balenciaga #fashion #fashion product #image #fashion image #region-us
| # Balenciaga web scraped data
## About the website
The fashion industry in the Asia Pacific region, particularly in China, is a hotbed of activity. It is one of the most lucrative markets in the world, spurred by a fast-growing middle class with an increased appetite for luxury products. The Chinese market, especially, plays host to many high-end, luxury fashion brands like Balenciaga. A significant transition has been noted in the mode of shopping, with a sharp turn towards Ecommerce. The dataset represents Ecommerce product-list page (PLP) data specific to Balenciagas online marketplace in China, highlighting the extensive variety of products offered by this luxury fashion house in the booming Chinese digital market.
## Link to dataset
China - Balenciaga - Product-level price list dataset
| [
"# Balenciaga web scraped data",
"## About the website\n\nThe fashion industry in the Asia Pacific region, particularly in China, is a hotbed of activity. It is one of the most lucrative markets in the world, spurred by a fast-growing middle class with an increased appetite for luxury products. The Chinese market, especially, plays host to many high-end, luxury fashion brands like Balenciaga. A significant transition has been noted in the mode of shopping, with a sharp turn towards Ecommerce. The dataset represents Ecommerce product-list page (PLP) data specific to Balenciagas online marketplace in China, highlighting the extensive variety of products offered by this luxury fashion house in the booming Chinese digital market.",
"## Link to dataset\n\nChina - Balenciaga - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Balenciaga #fashion #fashion product #image #fashion image #region-us \n",
"# Balenciaga web scraped data",
"## About the website\n\nThe fashion industry in the Asia Pacific region, particularly in China, is a hotbed of activity. It is one of the most lucrative markets in the world, spurred by a fast-growing middle class with an increased appetite for luxury products. The Chinese market, especially, plays host to many high-end, luxury fashion brands like Balenciaga. A significant transition has been noted in the mode of shopping, with a sharp turn towards Ecommerce. The dataset represents Ecommerce product-list page (PLP) data specific to Balenciagas online marketplace in China, highlighting the extensive variety of products offered by this luxury fashion house in the booming Chinese digital market.",
"## Link to dataset\n\nChina - Balenciaga - Product-level price list dataset"
]
| [
179,
8,
155,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Balenciaga #fashion #fashion product #image #fashion image #region-us \n# Balenciaga web scraped data## About the website\n\nThe fashion industry in the Asia Pacific region, particularly in China, is a hotbed of activity. It is one of the most lucrative markets in the world, spurred by a fast-growing middle class with an increased appetite for luxury products. The Chinese market, especially, plays host to many high-end, luxury fashion brands like Balenciaga. A significant transition has been noted in the mode of shopping, with a sharp turn towards Ecommerce. The dataset represents Ecommerce product-list page (PLP) data specific to Balenciagas online marketplace in China, highlighting the extensive variety of products offered by this luxury fashion house in the booming Chinese digital market.## Link to dataset\n\nChina - Balenciaga - Product-level price list dataset"
]
|
4a557d6ae264b728b1e43096d444302496f40056 | # Louis Vuitton web scraped data
## About the website
The **luxury fashion industry** in the **EMEA** region, particularly in **Russia**, is characterized by a growing demand for high-end products from renowned brands. **Louis Vuitton**, a global leader in this industry, caters to this escalating demand through their extensive range of luxury clothing, accessories, and luggage. The brand has significantly increased its presence in Russia by leveraging the power of **Ecommerce**, effectively reaching out to a wider targeted audience. Within this dataset, **Ecommerce product-list page (PLP)** data has been specifically examined for Louis Vuittons operations in the Russian market, reflecting the companys online strategy and consumer appeal in the region.
## Link to **dataset**
[Russia - Louis Vuitton - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Louis%20Vuitton%20Product-prices%20Russia/r/recdaAlMIm9kxriKT)
| DBQ/Louis.Vuitton.Product.prices.Russia | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Louis Vuitton",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:38:01+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Russia - Louis Vuitton - Product-level price list", "tags": ["webscraping", "ecommerce", "Louis Vuitton", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3013022, "num_examples": 6543}], "download_size": 817757, "dataset_size": 3013022}} | 2023-11-19T08:38:07+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Louis Vuitton #fashion #fashion product #image #fashion image #region-us
| # Louis Vuitton web scraped data
## About the website
The luxury fashion industry in the EMEA region, particularly in Russia, is characterized by a growing demand for high-end products from renowned brands. Louis Vuitton, a global leader in this industry, caters to this escalating demand through their extensive range of luxury clothing, accessories, and luggage. The brand has significantly increased its presence in Russia by leveraging the power of Ecommerce, effectively reaching out to a wider targeted audience. Within this dataset, Ecommerce product-list page (PLP) data has been specifically examined for Louis Vuittons operations in the Russian market, reflecting the companys online strategy and consumer appeal in the region.
## Link to dataset
Russia - Louis Vuitton - Product-level price list dataset
| [
"# Louis Vuitton web scraped data",
"## About the website\n\nThe luxury fashion industry in the EMEA region, particularly in Russia, is characterized by a growing demand for high-end products from renowned brands. Louis Vuitton, a global leader in this industry, caters to this escalating demand through their extensive range of luxury clothing, accessories, and luggage. The brand has significantly increased its presence in Russia by leveraging the power of Ecommerce, effectively reaching out to a wider targeted audience. Within this dataset, Ecommerce product-list page (PLP) data has been specifically examined for Louis Vuittons operations in the Russian market, reflecting the companys online strategy and consumer appeal in the region.",
"## Link to dataset\n\nRussia - Louis Vuitton - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Louis Vuitton #fashion #fashion product #image #fashion image #region-us \n",
"# Louis Vuitton web scraped data",
"## About the website\n\nThe luxury fashion industry in the EMEA region, particularly in Russia, is characterized by a growing demand for high-end products from renowned brands. Louis Vuitton, a global leader in this industry, caters to this escalating demand through their extensive range of luxury clothing, accessories, and luggage. The brand has significantly increased its presence in Russia by leveraging the power of Ecommerce, effectively reaching out to a wider targeted audience. Within this dataset, Ecommerce product-list page (PLP) data has been specifically examined for Louis Vuittons operations in the Russian market, reflecting the companys online strategy and consumer appeal in the region.",
"## Link to dataset\n\nRussia - Louis Vuitton - Product-level price list dataset"
]
| [
178,
7,
149,
17
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Louis Vuitton #fashion #fashion product #image #fashion image #region-us \n# Louis Vuitton web scraped data## About the website\n\nThe luxury fashion industry in the EMEA region, particularly in Russia, is characterized by a growing demand for high-end products from renowned brands. Louis Vuitton, a global leader in this industry, caters to this escalating demand through their extensive range of luxury clothing, accessories, and luggage. The brand has significantly increased its presence in Russia by leveraging the power of Ecommerce, effectively reaching out to a wider targeted audience. Within this dataset, Ecommerce product-list page (PLP) data has been specifically examined for Louis Vuittons operations in the Russian market, reflecting the companys online strategy and consumer appeal in the region.## Link to dataset\n\nRussia - Louis Vuitton - Product-level price list dataset"
]
|
3889f4961a3d681cd0cf4534f6d4cf5c96f2ed21 | # Mr Porter web scraped data
## About the website
The **fashion retail industry** in EMEA, specifically in the **Netherlands**, is a well-established and diverse marketplace encompassing a broad spectrum of brands and products. The ever-evolving landscape has witnessed a shift from traditional high-street stores to online platforms like **Mr Porter**, a trend accelerated by the pandemic. A vital player in the **menswear luxury online retailer** sector, Mr Porter has paved the way for breakthroughs in the **e-commerce** space. The dataset observed provides **Ecommerce product-list page (PLP)** data on **Mr Porter in Netherlands**, offering valuable insights into consumer behavior, purchasing habits, and popular product categories in the region.
## Link to **dataset**
[Netherlands - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Netherlands/r/recsmLp2flO8XRue5)
| DBQ/Mr.Porter.Product.prices.Netherlands | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:38:17+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Netherlands - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9095427, "num_examples": 27680}], "download_size": 2072546, "dataset_size": 9095427}} | 2023-11-19T08:38:23+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us
| # Mr Porter web scraped data
## About the website
The fashion retail industry in EMEA, specifically in the Netherlands, is a well-established and diverse marketplace encompassing a broad spectrum of brands and products. The ever-evolving landscape has witnessed a shift from traditional high-street stores to online platforms like Mr Porter, a trend accelerated by the pandemic. A vital player in the menswear luxury online retailer sector, Mr Porter has paved the way for breakthroughs in the e-commerce space. The dataset observed provides Ecommerce product-list page (PLP) data on Mr Porter in Netherlands, offering valuable insights into consumer behavior, purchasing habits, and popular product categories in the region.
## Link to dataset
Netherlands - Mr Porter - Product-level price list dataset
| [
"# Mr Porter web scraped data",
"## About the website\n\nThe fashion retail industry in EMEA, specifically in the Netherlands, is a well-established and diverse marketplace encompassing a broad spectrum of brands and products. The ever-evolving landscape has witnessed a shift from traditional high-street stores to online platforms like Mr Porter, a trend accelerated by the pandemic. A vital player in the menswear luxury online retailer sector, Mr Porter has paved the way for breakthroughs in the e-commerce space. The dataset observed provides Ecommerce product-list page (PLP) data on Mr Porter in Netherlands, offering valuable insights into consumer behavior, purchasing habits, and popular product categories in the region.",
"## Link to dataset\n\nNetherlands - Mr Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n",
"# Mr Porter web scraped data",
"## About the website\n\nThe fashion retail industry in EMEA, specifically in the Netherlands, is a well-established and diverse marketplace encompassing a broad spectrum of brands and products. The ever-evolving landscape has witnessed a shift from traditional high-street stores to online platforms like Mr Porter, a trend accelerated by the pandemic. A vital player in the menswear luxury online retailer sector, Mr Porter has paved the way for breakthroughs in the e-commerce space. The dataset observed provides Ecommerce product-list page (PLP) data on Mr Porter in Netherlands, offering valuable insights into consumer behavior, purchasing habits, and popular product categories in the region.",
"## Link to dataset\n\nNetherlands - Mr Porter - Product-level price list dataset"
]
| [
180,
8,
158,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n# Mr Porter web scraped data## About the website\n\nThe fashion retail industry in EMEA, specifically in the Netherlands, is a well-established and diverse marketplace encompassing a broad spectrum of brands and products. The ever-evolving landscape has witnessed a shift from traditional high-street stores to online platforms like Mr Porter, a trend accelerated by the pandemic. A vital player in the menswear luxury online retailer sector, Mr Porter has paved the way for breakthroughs in the e-commerce space. The dataset observed provides Ecommerce product-list page (PLP) data on Mr Porter in Netherlands, offering valuable insights into consumer behavior, purchasing habits, and popular product categories in the region.## Link to dataset\n\nNetherlands - Mr Porter - Product-level price list dataset"
]
|
ef0c609567860c0d4a1da8fea20931c42f7f82a9 | # Farfetch web scraped data
## About the website
Farfetch operates in the dynamic and rapidly evolving **E-commerce industry** in the **EMEA**, particularly in the **United Kingdom**. This sector is marked by intense digital transformation with a growing shift towards online shopping. Notably, the fashion and lifestyle segment of e-commerce is witnessing massive growth. The **UK E-commerce sector** is marked by high internet penetration rates, favourable consumer attitudes, and advances in technology. This has resulted in a significant increase in online transactions, specifically within the fashion industry. The dataset under review contains **Ecommerce product-list page (PLP) data** on **Farfetch** in the United Kingdom, indicating a comprehensive overview of the company’s digital profile in the UK market.
## Link to **dataset**
[United Kingdom - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20United%20Kingdom/r/rec4fnXBKT4UpoaXk)
| DBQ/Farfetch.Product.prices.United.Kingdom | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:38:56+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "United Kingdom - Farfetch - Product-level price list", "tags": ["webscraping", "ecommerce", "Farfetch", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 229625994, "num_examples": 613571}], "download_size": 80532862, "dataset_size": 229625994}} | 2023-11-19T08:39:53+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us
| # Farfetch web scraped data
## About the website
Farfetch operates in the dynamic and rapidly evolving E-commerce industry in the EMEA, particularly in the United Kingdom. This sector is marked by intense digital transformation with a growing shift towards online shopping. Notably, the fashion and lifestyle segment of e-commerce is witnessing massive growth. The UK E-commerce sector is marked by high internet penetration rates, favourable consumer attitudes, and advances in technology. This has resulted in a significant increase in online transactions, specifically within the fashion industry. The dataset under review contains Ecommerce product-list page (PLP) data on Farfetch in the United Kingdom, indicating a comprehensive overview of the company’s digital profile in the UK market.
## Link to dataset
United Kingdom - Farfetch - Product-level price list dataset
| [
"# Farfetch web scraped data",
"## About the website\n\nFarfetch operates in the dynamic and rapidly evolving E-commerce industry in the EMEA, particularly in the United Kingdom. This sector is marked by intense digital transformation with a growing shift towards online shopping. Notably, the fashion and lifestyle segment of e-commerce is witnessing massive growth. The UK E-commerce sector is marked by high internet penetration rates, favourable consumer attitudes, and advances in technology. This has resulted in a significant increase in online transactions, specifically within the fashion industry. The dataset under review contains Ecommerce product-list page (PLP) data on Farfetch in the United Kingdom, indicating a comprehensive overview of the company’s digital profile in the UK market.",
"## Link to dataset\n\nUnited Kingdom - Farfetch - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us \n",
"# Farfetch web scraped data",
"## About the website\n\nFarfetch operates in the dynamic and rapidly evolving E-commerce industry in the EMEA, particularly in the United Kingdom. This sector is marked by intense digital transformation with a growing shift towards online shopping. Notably, the fashion and lifestyle segment of e-commerce is witnessing massive growth. The UK E-commerce sector is marked by high internet penetration rates, favourable consumer attitudes, and advances in technology. This has resulted in a significant increase in online transactions, specifically within the fashion industry. The dataset under review contains Ecommerce product-list page (PLP) data on Farfetch in the United Kingdom, indicating a comprehensive overview of the company’s digital profile in the UK market.",
"## Link to dataset\n\nUnited Kingdom - Farfetch - Product-level price list dataset"
]
| [
179,
8,
159,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us \n# Farfetch web scraped data## About the website\n\nFarfetch operates in the dynamic and rapidly evolving E-commerce industry in the EMEA, particularly in the United Kingdom. This sector is marked by intense digital transformation with a growing shift towards online shopping. Notably, the fashion and lifestyle segment of e-commerce is witnessing massive growth. The UK E-commerce sector is marked by high internet penetration rates, favourable consumer attitudes, and advances in technology. This has resulted in a significant increase in online transactions, specifically within the fashion industry. The dataset under review contains Ecommerce product-list page (PLP) data on Farfetch in the United Kingdom, indicating a comprehensive overview of the company’s digital profile in the UK market.## Link to dataset\n\nUnited Kingdom - Farfetch - Product-level price list dataset"
]
|
4d760d306f6c99342f10e372d1e84c3f0546e411 | # Net-a-Porter web scraped data
## About the website
The **EMEA industry**, particularly in **Poland**, where **Net-a-Porter** operates, is represented by the **online fashion retail sector**. This industry has faced significant growth in the last few years due to increased internet penetration and consumer preference for online shopping. Polish e-commerce market has drastically expanded, with a growing number of consumers becoming more comfortable in purchasing a variety of products online. The **dataset** observed pertains to the **Ecommerce product-list page (PLP) data on Net-a-Porter in Poland**. This data provides valuable insights into shopping trends, customer preferences, and overall performance of products on the online platform.
## Link to **dataset**
[Poland - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Poland/r/recreBSBJ60wTY54C)
| DBQ/Net.a.Porter.Product.prices.Poland | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
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"webscraping",
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"Net",
"fashion",
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"image",
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]
| 2023-11-19T08:40:13+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Poland - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17622494, "num_examples": 43226}], "download_size": 5517346, "dataset_size": 17622494}} | 2023-11-19T08:40:20+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The EMEA industry, particularly in Poland, where Net-a-Porter operates, is represented by the online fashion retail sector. This industry has faced significant growth in the last few years due to increased internet penetration and consumer preference for online shopping. Polish e-commerce market has drastically expanded, with a growing number of consumers becoming more comfortable in purchasing a variety of products online. The dataset observed pertains to the Ecommerce product-list page (PLP) data on Net-a-Porter in Poland. This data provides valuable insights into shopping trends, customer preferences, and overall performance of products on the online platform.
## Link to dataset
Poland - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe EMEA industry, particularly in Poland, where Net-a-Porter operates, is represented by the online fashion retail sector. This industry has faced significant growth in the last few years due to increased internet penetration and consumer preference for online shopping. Polish e-commerce market has drastically expanded, with a growing number of consumers becoming more comfortable in purchasing a variety of products online. The dataset observed pertains to the Ecommerce product-list page (PLP) data on Net-a-Porter in Poland. This data provides valuable insights into shopping trends, customer preferences, and overall performance of products on the online platform.",
"## Link to dataset\n\nPoland - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nThe EMEA industry, particularly in Poland, where Net-a-Porter operates, is represented by the online fashion retail sector. This industry has faced significant growth in the last few years due to increased internet penetration and consumer preference for online shopping. Polish e-commerce market has drastically expanded, with a growing number of consumers becoming more comfortable in purchasing a variety of products online. The dataset observed pertains to the Ecommerce product-list page (PLP) data on Net-a-Porter in Poland. This data provides valuable insights into shopping trends, customer preferences, and overall performance of products on the online platform.",
"## Link to dataset\n\nPoland - Net-a-Porter - Product-level price list dataset"
]
| [
177,
11,
146,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe EMEA industry, particularly in Poland, where Net-a-Porter operates, is represented by the online fashion retail sector. This industry has faced significant growth in the last few years due to increased internet penetration and consumer preference for online shopping. Polish e-commerce market has drastically expanded, with a growing number of consumers becoming more comfortable in purchasing a variety of products online. The dataset observed pertains to the Ecommerce product-list page (PLP) data on Net-a-Porter in Poland. This data provides valuable insights into shopping trends, customer preferences, and overall performance of products on the online platform.## Link to dataset\n\nPoland - Net-a-Porter - Product-level price list dataset"
]
|
b4bc1ad9885ca6b9b5788b3490fec53d6cd6cb89 | # Gucci web scraped data
## About the website
The **fashion and luxury goods industry** is a rapidly growing sector in the **Asia Pacific** region, particularly in **Hong Kong**. A focal point of high-end shopping, Hong Kong attracts both Asian and international luxury brands such as **Gucci**. The growth of **online shopping** has a significant impact on the market, with brands leveraging **e-commerce platforms** to reach a broader audience. The dataset available encompasses **Ecommerce product-list page (PLP) data** on Gucci in this thriving city. For more insights on Gucci in other regions or data types, please visit the main page [here](https://www.databoutique.com/buy-data-list-subset/Gucci web scraped data/r/recr1rIJ15FC7ckn4).
## Link to **dataset**
[Hong Kong - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Hong%20Kong/r/recQfC0w2NW4NhnCX)
| DBQ/Gucci.Product.prices.Hong.Kong | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
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"multilinguality:monolingual",
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"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:40:32+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hong Kong - Gucci - Product-level price list", "tags": ["webscraping", "ecommerce", "Gucci", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2080541, "num_examples": 4398}], "download_size": 643102, "dataset_size": 2080541}} | 2023-11-19T08:40:37+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us
| # Gucci web scraped data
## About the website
The fashion and luxury goods industry is a rapidly growing sector in the Asia Pacific region, particularly in Hong Kong. A focal point of high-end shopping, Hong Kong attracts both Asian and international luxury brands such as Gucci. The growth of online shopping has a significant impact on the market, with brands leveraging e-commerce platforms to reach a broader audience. The dataset available encompasses Ecommerce product-list page (PLP) data on Gucci in this thriving city. For more insights on Gucci in other regions or data types, please visit the main page here.
## Link to dataset
Hong Kong - Gucci - Product-level price list dataset
| [
"# Gucci web scraped data",
"## About the website\n\nThe fashion and luxury goods industry is a rapidly growing sector in the Asia Pacific region, particularly in Hong Kong. A focal point of high-end shopping, Hong Kong attracts both Asian and international luxury brands such as Gucci. The growth of online shopping has a significant impact on the market, with brands leveraging e-commerce platforms to reach a broader audience. The dataset available encompasses Ecommerce product-list page (PLP) data on Gucci in this thriving city. For more insights on Gucci in other regions or data types, please visit the main page here.",
"## Link to dataset\n\nHong Kong - Gucci - Product-level price list dataset"
]
| [
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"# Gucci web scraped data",
"## About the website\n\nThe fashion and luxury goods industry is a rapidly growing sector in the Asia Pacific region, particularly in Hong Kong. A focal point of high-end shopping, Hong Kong attracts both Asian and international luxury brands such as Gucci. The growth of online shopping has a significant impact on the market, with brands leveraging e-commerce platforms to reach a broader audience. The dataset available encompasses Ecommerce product-list page (PLP) data on Gucci in this thriving city. For more insights on Gucci in other regions or data types, please visit the main page here.",
"## Link to dataset\n\nHong Kong - Gucci - Product-level price list dataset"
]
| [
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7,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us \n# Gucci web scraped data## About the website\n\nThe fashion and luxury goods industry is a rapidly growing sector in the Asia Pacific region, particularly in Hong Kong. A focal point of high-end shopping, Hong Kong attracts both Asian and international luxury brands such as Gucci. The growth of online shopping has a significant impact on the market, with brands leveraging e-commerce platforms to reach a broader audience. The dataset available encompasses Ecommerce product-list page (PLP) data on Gucci in this thriving city. For more insights on Gucci in other regions or data types, please visit the main page here.## Link to dataset\n\nHong Kong - Gucci - Product-level price list dataset"
]
|
6b9cf61c323cb2abfe35ee4b41471b6ac9b4e603 | # Prada web scraped data
## About the website
The fashion industry, particularly the luxury fashion segment, exhibits a vast and dynamic scope in the Europe, Middle East, and Africa (EMEA) region, with Austria playing a crucial role in its positive trajectory. **Prada**, a prominent luxury fashion icon, continues to thrive in Austrias competitive market. **The industry** is significantly propelled by advancements in technology, leading to a surge in **Ecommerce** platforms. A recent dataset provides insights into the **Ecommerce product-list page (PLP) data** of Prada in Austria. Such data is critical in understanding consumer preferences, purchasing patterns and overall market trends, subsequently influencing strategic business decisions and marketing campaigns.
## Link to **dataset**
[Austria - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Austria/r/recYuVyhn9tiSVuIh)
| DBQ/Prada.Product.prices.Austria | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
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"multilinguality:monolingual",
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"webscraping",
"ecommerce",
"Prada",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:40:53+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Austria - Prada - Product-level price list", "tags": ["webscraping", "ecommerce", "Prada", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1280254, "num_examples": 2545}], "download_size": 387267, "dataset_size": 1280254}} | 2023-11-19T08:40:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us
| # Prada web scraped data
## About the website
The fashion industry, particularly the luxury fashion segment, exhibits a vast and dynamic scope in the Europe, Middle East, and Africa (EMEA) region, with Austria playing a crucial role in its positive trajectory. Prada, a prominent luxury fashion icon, continues to thrive in Austrias competitive market. The industry is significantly propelled by advancements in technology, leading to a surge in Ecommerce platforms. A recent dataset provides insights into the Ecommerce product-list page (PLP) data of Prada in Austria. Such data is critical in understanding consumer preferences, purchasing patterns and overall market trends, subsequently influencing strategic business decisions and marketing campaigns.
## Link to dataset
Austria - Prada - Product-level price list dataset
| [
"# Prada web scraped data",
"## About the website\n\nThe fashion industry, particularly the luxury fashion segment, exhibits a vast and dynamic scope in the Europe, Middle East, and Africa (EMEA) region, with Austria playing a crucial role in its positive trajectory. Prada, a prominent luxury fashion icon, continues to thrive in Austrias competitive market. The industry is significantly propelled by advancements in technology, leading to a surge in Ecommerce platforms. A recent dataset provides insights into the Ecommerce product-list page (PLP) data of Prada in Austria. Such data is critical in understanding consumer preferences, purchasing patterns and overall market trends, subsequently influencing strategic business decisions and marketing campaigns.",
"## Link to dataset\n\nAustria - Prada - Product-level price list dataset"
]
| [
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"# Prada web scraped data",
"## About the website\n\nThe fashion industry, particularly the luxury fashion segment, exhibits a vast and dynamic scope in the Europe, Middle East, and Africa (EMEA) region, with Austria playing a crucial role in its positive trajectory. Prada, a prominent luxury fashion icon, continues to thrive in Austrias competitive market. The industry is significantly propelled by advancements in technology, leading to a surge in Ecommerce platforms. A recent dataset provides insights into the Ecommerce product-list page (PLP) data of Prada in Austria. Such data is critical in understanding consumer preferences, purchasing patterns and overall market trends, subsequently influencing strategic business decisions and marketing campaigns.",
"## Link to dataset\n\nAustria - Prada - Product-level price list dataset"
]
| [
178,
7,
154,
17
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Prada #fashion #fashion product #image #fashion image #region-us \n# Prada web scraped data## About the website\n\nThe fashion industry, particularly the luxury fashion segment, exhibits a vast and dynamic scope in the Europe, Middle East, and Africa (EMEA) region, with Austria playing a crucial role in its positive trajectory. Prada, a prominent luxury fashion icon, continues to thrive in Austrias competitive market. The industry is significantly propelled by advancements in technology, leading to a surge in Ecommerce platforms. A recent dataset provides insights into the Ecommerce product-list page (PLP) data of Prada in Austria. Such data is critical in understanding consumer preferences, purchasing patterns and overall market trends, subsequently influencing strategic business decisions and marketing campaigns.## Link to dataset\n\nAustria - Prada - Product-level price list dataset"
]
|
a1c23f8174adc7c896b9fc06a65e9a3c64cf795c | # Mr Porter web scraped data
## About the website
The **Ecommerce** industry in the EMEA region, particularly in **Slovakia**, has seen significant growth in recent years. **Mr Porter**, a renowned luxury menswear retailer, operates within this sector. Slovakia, characterized by a rising middle class and increased internet penetration, provides fertile ground for Ecommerce growth. Key factors driving this industry growth include advanced online payments and an efficient logistics infrastructure for product delivery. This study utilized a dataset containing **product-list page (PLP)** data from Mr Porter’s operations in Slovakia, thereby providing invaluable insight into this flourishing Ecommerce market.
## Link to **dataset**
[Slovakia - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Slovakia/r/recUwnNbtqOugybPd)
| DBQ/Mr.Porter.Product.prices.Slovakia | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:41:10+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Slovakia - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9143481, "num_examples": 27826}], "download_size": 2072283, "dataset_size": 9143481}} | 2023-11-19T08:41:16+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us
| # Mr Porter web scraped data
## About the website
The Ecommerce industry in the EMEA region, particularly in Slovakia, has seen significant growth in recent years. Mr Porter, a renowned luxury menswear retailer, operates within this sector. Slovakia, characterized by a rising middle class and increased internet penetration, provides fertile ground for Ecommerce growth. Key factors driving this industry growth include advanced online payments and an efficient logistics infrastructure for product delivery. This study utilized a dataset containing product-list page (PLP) data from Mr Porter’s operations in Slovakia, thereby providing invaluable insight into this flourishing Ecommerce market.
## Link to dataset
Slovakia - Mr Porter - Product-level price list dataset
| [
"# Mr Porter web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Slovakia, has seen significant growth in recent years. Mr Porter, a renowned luxury menswear retailer, operates within this sector. Slovakia, characterized by a rising middle class and increased internet penetration, provides fertile ground for Ecommerce growth. Key factors driving this industry growth include advanced online payments and an efficient logistics infrastructure for product delivery. This study utilized a dataset containing product-list page (PLP) data from Mr Porter’s operations in Slovakia, thereby providing invaluable insight into this flourishing Ecommerce market.",
"## Link to dataset\n\nSlovakia - Mr Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n",
"# Mr Porter web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Slovakia, has seen significant growth in recent years. Mr Porter, a renowned luxury menswear retailer, operates within this sector. Slovakia, characterized by a rising middle class and increased internet penetration, provides fertile ground for Ecommerce growth. Key factors driving this industry growth include advanced online payments and an efficient logistics infrastructure for product delivery. This study utilized a dataset containing product-list page (PLP) data from Mr Porter’s operations in Slovakia, thereby providing invaluable insight into this flourishing Ecommerce market.",
"## Link to dataset\n\nSlovakia - Mr Porter - Product-level price list dataset"
]
| [
180,
8,
137,
18
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n# Mr Porter web scraped data## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Slovakia, has seen significant growth in recent years. Mr Porter, a renowned luxury menswear retailer, operates within this sector. Slovakia, characterized by a rising middle class and increased internet penetration, provides fertile ground for Ecommerce growth. Key factors driving this industry growth include advanced online payments and an efficient logistics infrastructure for product delivery. This study utilized a dataset containing product-list page (PLP) data from Mr Porter’s operations in Slovakia, thereby providing invaluable insight into this flourishing Ecommerce market.## Link to dataset\n\nSlovakia - Mr Porter - Product-level price list dataset"
]
|
2d90840b90ebbbf6b4a1ba8b72252fd5bbe51230 | # Burberry web scraped data
## About the website
Burberry operates within the **luxury fashion industry** in the United States, which is a segment of the wider **retail industry**. The American market is highly competitive and renowned for its considerable consumer spending. **E-commerce** has become a vital platform for luxury brands like Burberry to extend their customer reach, especially amid changing shopping habits among consumers. The **online luxury fashion market** in the United States has seen exponential growth, with more consumers turning to the convenience and vast product selection of online shopping. The dataset observed includes **Ecommerce product-list page (PLP) data** on Burberry in the United States.
## Link to **dataset**
[United States - Burberry - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Burberry%20Product-prices%20United%20States/r/rec3QS3ibv3mus8AY)
| DBQ/Burberry.Product.prices.United.States | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
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"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Burberry",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:41:24+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "United States - Burberry - Product-level price list", "tags": ["webscraping", "ecommerce", "Burberry", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 969198, "num_examples": 3038}], "download_size": 287409, "dataset_size": 969198}} | 2023-11-19T08:41:29+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Burberry #fashion #fashion product #image #fashion image #region-us
| # Burberry web scraped data
## About the website
Burberry operates within the luxury fashion industry in the United States, which is a segment of the wider retail industry. The American market is highly competitive and renowned for its considerable consumer spending. E-commerce has become a vital platform for luxury brands like Burberry to extend their customer reach, especially amid changing shopping habits among consumers. The online luxury fashion market in the United States has seen exponential growth, with more consumers turning to the convenience and vast product selection of online shopping. The dataset observed includes Ecommerce product-list page (PLP) data on Burberry in the United States.
## Link to dataset
United States - Burberry - Product-level price list dataset
| [
"# Burberry web scraped data",
"## About the website\n\nBurberry operates within the luxury fashion industry in the United States, which is a segment of the wider retail industry. The American market is highly competitive and renowned for its considerable consumer spending. E-commerce has become a vital platform for luxury brands like Burberry to extend their customer reach, especially amid changing shopping habits among consumers. The online luxury fashion market in the United States has seen exponential growth, with more consumers turning to the convenience and vast product selection of online shopping. The dataset observed includes Ecommerce product-list page (PLP) data on Burberry in the United States.",
"## Link to dataset\n\nUnited States - Burberry - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Burberry #fashion #fashion product #image #fashion image #region-us \n",
"# Burberry web scraped data",
"## About the website\n\nBurberry operates within the luxury fashion industry in the United States, which is a segment of the wider retail industry. The American market is highly competitive and renowned for its considerable consumer spending. E-commerce has become a vital platform for luxury brands like Burberry to extend their customer reach, especially amid changing shopping habits among consumers. The online luxury fashion market in the United States has seen exponential growth, with more consumers turning to the convenience and vast product selection of online shopping. The dataset observed includes Ecommerce product-list page (PLP) data on Burberry in the United States.",
"## Link to dataset\n\nUnited States - Burberry - Product-level price list dataset"
]
| [
178,
7,
135,
18
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Burberry #fashion #fashion product #image #fashion image #region-us \n# Burberry web scraped data## About the website\n\nBurberry operates within the luxury fashion industry in the United States, which is a segment of the wider retail industry. The American market is highly competitive and renowned for its considerable consumer spending. E-commerce has become a vital platform for luxury brands like Burberry to extend their customer reach, especially amid changing shopping habits among consumers. The online luxury fashion market in the United States has seen exponential growth, with more consumers turning to the convenience and vast product selection of online shopping. The dataset observed includes Ecommerce product-list page (PLP) data on Burberry in the United States.## Link to dataset\n\nUnited States - Burberry - Product-level price list dataset"
]
|
59d387d9212452854809026a74d5e88367168d70 | # Hermes web scraped data
## About the website
The **luxury retail industry** in the **Asia Pacific** region, particularly in **South Korea**, has been witnessing significant growth in the recent years. Undeniably fuelled by a rising middle-class population, increasing disposable income, and rapid urbanization, luxury goods demand is booming. French high-fashion label, **Hermes**, is one of the giants operating in this market. The brand has established a remarkable presence in South Korea, catering to the ever-evolving tastes of the affluent consumers. The dataset observed provides **Ecommerce product-list page (PLP) data on Hermes** in South Korea, reflecting the consumer preferences and shopping patterns in the digital sphere of luxury retail.
## Link to **dataset**
[South Korea - Hermes - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Hermes%20Product-prices%20South%20Korea/r/recovPN1p4g399g3n)
| DBQ/Hermes.Product.prices.South.Korea | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
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"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Hermes",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:41:38+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "South Korea - Hermes - Product-level price list", "tags": ["webscraping", "ecommerce", "Hermes", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "int64"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 225974, "num_examples": 607}], "download_size": 56424, "dataset_size": 225974}} | 2023-11-19T08:41:42+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Hermes #fashion #fashion product #image #fashion image #region-us
| # Hermes web scraped data
## About the website
The luxury retail industry in the Asia Pacific region, particularly in South Korea, has been witnessing significant growth in the recent years. Undeniably fuelled by a rising middle-class population, increasing disposable income, and rapid urbanization, luxury goods demand is booming. French high-fashion label, Hermes, is one of the giants operating in this market. The brand has established a remarkable presence in South Korea, catering to the ever-evolving tastes of the affluent consumers. The dataset observed provides Ecommerce product-list page (PLP) data on Hermes in South Korea, reflecting the consumer preferences and shopping patterns in the digital sphere of luxury retail.
## Link to dataset
South Korea - Hermes - Product-level price list dataset
| [
"# Hermes web scraped data",
"## About the website\n\nThe luxury retail industry in the Asia Pacific region, particularly in South Korea, has been witnessing significant growth in the recent years. Undeniably fuelled by a rising middle-class population, increasing disposable income, and rapid urbanization, luxury goods demand is booming. French high-fashion label, Hermes, is one of the giants operating in this market. The brand has established a remarkable presence in South Korea, catering to the ever-evolving tastes of the affluent consumers. The dataset observed provides Ecommerce product-list page (PLP) data on Hermes in South Korea, reflecting the consumer preferences and shopping patterns in the digital sphere of luxury retail.",
"## Link to dataset\n\nSouth Korea - Hermes - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Hermes #fashion #fashion product #image #fashion image #region-us \n",
"# Hermes web scraped data",
"## About the website\n\nThe luxury retail industry in the Asia Pacific region, particularly in South Korea, has been witnessing significant growth in the recent years. Undeniably fuelled by a rising middle-class population, increasing disposable income, and rapid urbanization, luxury goods demand is booming. French high-fashion label, Hermes, is one of the giants operating in this market. The brand has established a remarkable presence in South Korea, catering to the ever-evolving tastes of the affluent consumers. The dataset observed provides Ecommerce product-list page (PLP) data on Hermes in South Korea, reflecting the consumer preferences and shopping patterns in the digital sphere of luxury retail.",
"## Link to dataset\n\nSouth Korea - Hermes - Product-level price list dataset"
]
| [
178,
7,
158,
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]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Hermes #fashion #fashion product #image #fashion image #region-us \n# Hermes web scraped data## About the website\n\nThe luxury retail industry in the Asia Pacific region, particularly in South Korea, has been witnessing significant growth in the recent years. Undeniably fuelled by a rising middle-class population, increasing disposable income, and rapid urbanization, luxury goods demand is booming. French high-fashion label, Hermes, is one of the giants operating in this market. The brand has established a remarkable presence in South Korea, catering to the ever-evolving tastes of the affluent consumers. The dataset observed provides Ecommerce product-list page (PLP) data on Hermes in South Korea, reflecting the consumer preferences and shopping patterns in the digital sphere of luxury retail.## Link to dataset\n\nSouth Korea - Hermes - Product-level price list dataset"
]
|
11fb488c5e8ff228b4e5afc1749d0e75dc9b9527 | # Burberry web scraped data
## About the website
The **Burberry** brand operates within the **luxury fashion industry** in the EMEA region, particularly in **France**. This sector primarily focuses on the creation and retail of upscale clothing and accessories that are well-renowned among high-end consumers. It is characterized by exclusive distribution methods, high-quality materials, and strong branding. France, especially Paris, is recognized as a global center for luxury fashion, underpinned by famous fashion houses, influential designers and high-profile fashion events. The observed dataset comprises of **Ecommerce product-list page (PLP) data** specifically related to Burberry in the French market, reflecting the brands digital presence and sales in the region.
## Link to **dataset**
[France - Burberry - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Burberry%20Product-prices%20France/r/rectNx7vaD6XOvbMI)
| DBQ/Burberry.Product.prices.France | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
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"Burberry",
"fashion",
"fashion product",
"image",
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"region:us"
]
| 2023-11-19T08:41:51+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "France - Burberry - Product-level price list", "tags": ["webscraping", "ecommerce", "Burberry", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1084708, "num_examples": 3298}], "download_size": 320925, "dataset_size": 1084708}} | 2023-11-19T08:41:56+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Burberry #fashion #fashion product #image #fashion image #region-us
| # Burberry web scraped data
## About the website
The Burberry brand operates within the luxury fashion industry in the EMEA region, particularly in France. This sector primarily focuses on the creation and retail of upscale clothing and accessories that are well-renowned among high-end consumers. It is characterized by exclusive distribution methods, high-quality materials, and strong branding. France, especially Paris, is recognized as a global center for luxury fashion, underpinned by famous fashion houses, influential designers and high-profile fashion events. The observed dataset comprises of Ecommerce product-list page (PLP) data specifically related to Burberry in the French market, reflecting the brands digital presence and sales in the region.
## Link to dataset
France - Burberry - Product-level price list dataset
| [
"# Burberry web scraped data",
"## About the website\n\nThe Burberry brand operates within the luxury fashion industry in the EMEA region, particularly in France. This sector primarily focuses on the creation and retail of upscale clothing and accessories that are well-renowned among high-end consumers. It is characterized by exclusive distribution methods, high-quality materials, and strong branding. France, especially Paris, is recognized as a global center for luxury fashion, underpinned by famous fashion houses, influential designers and high-profile fashion events. The observed dataset comprises of Ecommerce product-list page (PLP) data specifically related to Burberry in the French market, reflecting the brands digital presence and sales in the region.",
"## Link to dataset\n\nFrance - Burberry - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Burberry #fashion #fashion product #image #fashion image #region-us \n",
"# Burberry web scraped data",
"## About the website\n\nThe Burberry brand operates within the luxury fashion industry in the EMEA region, particularly in France. This sector primarily focuses on the creation and retail of upscale clothing and accessories that are well-renowned among high-end consumers. It is characterized by exclusive distribution methods, high-quality materials, and strong branding. France, especially Paris, is recognized as a global center for luxury fashion, underpinned by famous fashion houses, influential designers and high-profile fashion events. The observed dataset comprises of Ecommerce product-list page (PLP) data specifically related to Burberry in the French market, reflecting the brands digital presence and sales in the region.",
"## Link to dataset\n\nFrance - Burberry - Product-level price list dataset"
]
| [
178,
7,
157,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Burberry #fashion #fashion product #image #fashion image #region-us \n# Burberry web scraped data## About the website\n\nThe Burberry brand operates within the luxury fashion industry in the EMEA region, particularly in France. This sector primarily focuses on the creation and retail of upscale clothing and accessories that are well-renowned among high-end consumers. It is characterized by exclusive distribution methods, high-quality materials, and strong branding. France, especially Paris, is recognized as a global center for luxury fashion, underpinned by famous fashion houses, influential designers and high-profile fashion events. The observed dataset comprises of Ecommerce product-list page (PLP) data specifically related to Burberry in the French market, reflecting the brands digital presence and sales in the region.## Link to dataset\n\nFrance - Burberry - Product-level price list dataset"
]
|
602eba3ae85b176758e53748edfccf3b4eaeef15 | # Mr Porter web scraped data
## About the website
The **EMEA industry**, and specifically the **Croatian market**, have recently witnessed substantial growth in the **ecommerce sector**, transforming the way customers shop for goods and services. A leading contributor to this growth is **Mr Porter**, a key player in online retail. The dataset examined offers extensive coverage of **Ecommerce product-list page (PLP)** data on Mr Porter in Croatia. This information allows us to track trends, monitor performance, and predict future behavior of customers, offering invaluable insights into a highly competitive and rapidly growing industry. Additionally, it provides comprehensive consumer buying patterns, preferences, and habits, unlocking opportunities in Croatias ecommerce space.
## Link to **dataset**
[Croatia - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Croatia/r/recd0Jmn7INgElyiD)
| DBQ/Mr.Porter.Product.prices.Croatia | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
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"multilinguality:monolingual",
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"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:42:06+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Croatia - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9058352, "num_examples": 27570}], "download_size": 2057404, "dataset_size": 9058352}} | 2023-11-19T08:42:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us
| # Mr Porter web scraped data
## About the website
The EMEA industry, and specifically the Croatian market, have recently witnessed substantial growth in the ecommerce sector, transforming the way customers shop for goods and services. A leading contributor to this growth is Mr Porter, a key player in online retail. The dataset examined offers extensive coverage of Ecommerce product-list page (PLP) data on Mr Porter in Croatia. This information allows us to track trends, monitor performance, and predict future behavior of customers, offering invaluable insights into a highly competitive and rapidly growing industry. Additionally, it provides comprehensive consumer buying patterns, preferences, and habits, unlocking opportunities in Croatias ecommerce space.
## Link to dataset
Croatia - Mr Porter - Product-level price list dataset
| [
"# Mr Porter web scraped data",
"## About the website\n\nThe EMEA industry, and specifically the Croatian market, have recently witnessed substantial growth in the ecommerce sector, transforming the way customers shop for goods and services. A leading contributor to this growth is Mr Porter, a key player in online retail. The dataset examined offers extensive coverage of Ecommerce product-list page (PLP) data on Mr Porter in Croatia. This information allows us to track trends, monitor performance, and predict future behavior of customers, offering invaluable insights into a highly competitive and rapidly growing industry. Additionally, it provides comprehensive consumer buying patterns, preferences, and habits, unlocking opportunities in Croatias ecommerce space.",
"## Link to dataset\n\nCroatia - Mr Porter - Product-level price list dataset"
]
| [
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"# Mr Porter web scraped data",
"## About the website\n\nThe EMEA industry, and specifically the Croatian market, have recently witnessed substantial growth in the ecommerce sector, transforming the way customers shop for goods and services. A leading contributor to this growth is Mr Porter, a key player in online retail. The dataset examined offers extensive coverage of Ecommerce product-list page (PLP) data on Mr Porter in Croatia. This information allows us to track trends, monitor performance, and predict future behavior of customers, offering invaluable insights into a highly competitive and rapidly growing industry. Additionally, it provides comprehensive consumer buying patterns, preferences, and habits, unlocking opportunities in Croatias ecommerce space.",
"## Link to dataset\n\nCroatia - Mr Porter - Product-level price list dataset"
]
| [
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8,
154,
18
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n# Mr Porter web scraped data## About the website\n\nThe EMEA industry, and specifically the Croatian market, have recently witnessed substantial growth in the ecommerce sector, transforming the way customers shop for goods and services. A leading contributor to this growth is Mr Porter, a key player in online retail. The dataset examined offers extensive coverage of Ecommerce product-list page (PLP) data on Mr Porter in Croatia. This information allows us to track trends, monitor performance, and predict future behavior of customers, offering invaluable insights into a highly competitive and rapidly growing industry. Additionally, it provides comprehensive consumer buying patterns, preferences, and habits, unlocking opportunities in Croatias ecommerce space.## Link to dataset\n\nCroatia - Mr Porter - Product-level price list dataset"
]
|
ff4dd310631ab6c194b194929bfc0068bad6d62c | # Net-a-Porter web scraped data
## About the website
The **Net-a-Porter** company operates within the **Ecommerce industry** in the **EMEA region**, particularly in **Turkey**. This expansive sector primarily focuses on the buying and selling of goods and services through the internet, showcasing an array of products from various providers on a global scale. Over the past few years, the Ecommerce sector in Turkey has experienced rapid growth, leading to a highly competitive market. Companies within this frame such as Net-a-Porter have to consistently develop and implement effective strategies to stay ahead. The dataset analyzed consists of **Ecommerce product-list page (PLP) data** on Net-a-Porters online operations in Turkey.
## Link to **dataset**
[Turkey - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Turkey/r/recJ70Nant3WEl1FS)
| DBQ/Net.a.Porter.Product.prices.Turkey | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
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"multilinguality:monolingual",
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"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:42:23+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Turkey - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16787036, "num_examples": 41170}], "download_size": 4937471, "dataset_size": 16787036}} | 2023-11-19T08:42:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The Net-a-Porter company operates within the Ecommerce industry in the EMEA region, particularly in Turkey. This expansive sector primarily focuses on the buying and selling of goods and services through the internet, showcasing an array of products from various providers on a global scale. Over the past few years, the Ecommerce sector in Turkey has experienced rapid growth, leading to a highly competitive market. Companies within this frame such as Net-a-Porter have to consistently develop and implement effective strategies to stay ahead. The dataset analyzed consists of Ecommerce product-list page (PLP) data on Net-a-Porters online operations in Turkey.
## Link to dataset
Turkey - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Net-a-Porter company operates within the Ecommerce industry in the EMEA region, particularly in Turkey. This expansive sector primarily focuses on the buying and selling of goods and services through the internet, showcasing an array of products from various providers on a global scale. Over the past few years, the Ecommerce sector in Turkey has experienced rapid growth, leading to a highly competitive market. Companies within this frame such as Net-a-Porter have to consistently develop and implement effective strategies to stay ahead. The dataset analyzed consists of Ecommerce product-list page (PLP) data on Net-a-Porters online operations in Turkey.",
"## Link to dataset\n\nTurkey - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Net-a-Porter company operates within the Ecommerce industry in the EMEA region, particularly in Turkey. This expansive sector primarily focuses on the buying and selling of goods and services through the internet, showcasing an array of products from various providers on a global scale. Over the past few years, the Ecommerce sector in Turkey has experienced rapid growth, leading to a highly competitive market. Companies within this frame such as Net-a-Porter have to consistently develop and implement effective strategies to stay ahead. The dataset analyzed consists of Ecommerce product-list page (PLP) data on Net-a-Porters online operations in Turkey.",
"## Link to dataset\n\nTurkey - Net-a-Porter - Product-level price list dataset"
]
| [
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154,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe Net-a-Porter company operates within the Ecommerce industry in the EMEA region, particularly in Turkey. This expansive sector primarily focuses on the buying and selling of goods and services through the internet, showcasing an array of products from various providers on a global scale. Over the past few years, the Ecommerce sector in Turkey has experienced rapid growth, leading to a highly competitive market. Companies within this frame such as Net-a-Porter have to consistently develop and implement effective strategies to stay ahead. The dataset analyzed consists of Ecommerce product-list page (PLP) data on Net-a-Porters online operations in Turkey.## Link to dataset\n\nTurkey - Net-a-Porter - Product-level price list dataset"
]
|
ffba2b17cee7f031e047a289805ec59da2d569b6 | # Blickers web scraped data
## About the website
The **Ecommerce industry** in the **EMEA** region, particularly in **Italy**, has exhibited a significant growth due to the digital transformation and increased online shopping trends. The **Blickers company** operates within this thriving sector. As per the examined dataset, which includes **Ecommerce product-list page (PLP) data** about Blickers online store, it is evident that the company is performing robustly in the Italian market. This dataset offers crucial insights about the online behavior of shoppers on Blickers website, helping understand their preferences and patterns. This vital information remains instrumental in shaping digital marketing strategies to ensure better customer engagement and retention.
## Link to **dataset**
[Italy - Blickers - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Blickers%20Product-prices%20Italy/r/recSiB2rUhdVgNeDj)
| DBQ/Blickers.Product.prices.Italy | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Blickers",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:42:40+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Italy - Blickers - Product-level price list", "tags": ["webscraping", "ecommerce", "Blickers", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 11209876, "num_examples": 29548}], "download_size": 5407387, "dataset_size": 11209876}} | 2023-11-19T08:42:48+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Blickers #fashion #fashion product #image #fashion image #region-us
| # Blickers web scraped data
## About the website
The Ecommerce industry in the EMEA region, particularly in Italy, has exhibited a significant growth due to the digital transformation and increased online shopping trends. The Blickers company operates within this thriving sector. As per the examined dataset, which includes Ecommerce product-list page (PLP) data about Blickers online store, it is evident that the company is performing robustly in the Italian market. This dataset offers crucial insights about the online behavior of shoppers on Blickers website, helping understand their preferences and patterns. This vital information remains instrumental in shaping digital marketing strategies to ensure better customer engagement and retention.
## Link to dataset
Italy - Blickers - Product-level price list dataset
| [
"# Blickers web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Italy, has exhibited a significant growth due to the digital transformation and increased online shopping trends. The Blickers company operates within this thriving sector. As per the examined dataset, which includes Ecommerce product-list page (PLP) data about Blickers online store, it is evident that the company is performing robustly in the Italian market. This dataset offers crucial insights about the online behavior of shoppers on Blickers website, helping understand their preferences and patterns. This vital information remains instrumental in shaping digital marketing strategies to ensure better customer engagement and retention.",
"## Link to dataset\n\nItaly - Blickers - Product-level price list dataset"
]
| [
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"# Blickers web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Italy, has exhibited a significant growth due to the digital transformation and increased online shopping trends. The Blickers company operates within this thriving sector. As per the examined dataset, which includes Ecommerce product-list page (PLP) data about Blickers online store, it is evident that the company is performing robustly in the Italian market. This dataset offers crucial insights about the online behavior of shoppers on Blickers website, helping understand their preferences and patterns. This vital information remains instrumental in shaping digital marketing strategies to ensure better customer engagement and retention.",
"## Link to dataset\n\nItaly - Blickers - Product-level price list dataset"
]
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179,
7,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Blickers #fashion #fashion product #image #fashion image #region-us \n# Blickers web scraped data## About the website\n\nThe Ecommerce industry in the EMEA region, particularly in Italy, has exhibited a significant growth due to the digital transformation and increased online shopping trends. The Blickers company operates within this thriving sector. As per the examined dataset, which includes Ecommerce product-list page (PLP) data about Blickers online store, it is evident that the company is performing robustly in the Italian market. This dataset offers crucial insights about the online behavior of shoppers on Blickers website, helping understand their preferences and patterns. This vital information remains instrumental in shaping digital marketing strategies to ensure better customer engagement and retention.## Link to dataset\n\nItaly - Blickers - Product-level price list dataset"
]
|
097d8fad01f70c7516a76e318510569d224b2c7c | # Saint Laurent web scraped data
## About the website
The **fashion industry** in the **Asia Pacific**, particularly in **South Korea**, is thriving and recognized for its dynamic adaptability to global styling trends. Central to this growth is the luxury segment that includes renowned brands such as **Saint Laurent**. Rapid digital transformation has occurred in this regions retail sector, most notably the rise of **Ecommerce**. Today, online retail portals serve as a significant sales channel for fashion brands. The dataset observed involves **Ecommerce product-list page (PLP)** data on Saint Laurent in South Korea which grants valuable insights into the online retail trends and consumer behaviors of the luxury brand in this region.
## Link to **dataset**
[South Korea - Saint Laurent - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Product-prices%20South%20Korea/r/recnrJOE5NE4dFDeY)
| DBQ/Saint.Laurent.Product.prices.South.Korea | [
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| 2023-11-19T08:43:05+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "South Korea - Saint Laurent - Product-level price list", "tags": ["webscraping", "ecommerce", "Saint Laurent", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1548448, "num_examples": 3018}], "download_size": 468846, "dataset_size": 1548448}} | 2023-11-19T08:43:10+00:00 | []
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| TAGS
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| # Saint Laurent web scraped data
## About the website
The fashion industry in the Asia Pacific, particularly in South Korea, is thriving and recognized for its dynamic adaptability to global styling trends. Central to this growth is the luxury segment that includes renowned brands such as Saint Laurent. Rapid digital transformation has occurred in this regions retail sector, most notably the rise of Ecommerce. Today, online retail portals serve as a significant sales channel for fashion brands. The dataset observed involves Ecommerce product-list page (PLP) data on Saint Laurent in South Korea which grants valuable insights into the online retail trends and consumer behaviors of the luxury brand in this region.
## Link to dataset
South Korea - Saint Laurent - Product-level price list dataset
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"## About the website\n\nThe fashion industry in the Asia Pacific, particularly in South Korea, is thriving and recognized for its dynamic adaptability to global styling trends. Central to this growth is the luxury segment that includes renowned brands such as Saint Laurent. Rapid digital transformation has occurred in this regions retail sector, most notably the rise of Ecommerce. Today, online retail portals serve as a significant sales channel for fashion brands. The dataset observed involves Ecommerce product-list page (PLP) data on Saint Laurent in South Korea which grants valuable insights into the online retail trends and consumer behaviors of the luxury brand in this region.",
"## Link to dataset\n\nSouth Korea - Saint Laurent - Product-level price list dataset"
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]
|
762a7dd8d9999e1e5f86998f085aeb6a808d503b | # Saint Laurent web scraped data
## About the website
The **Fashion** and **Luxury** retail industry is a prominent economic sector in the EMEA, particularly in **France**, known globally as the birthplace of Haute Couture. Anchored by prestigious French fashion houses like **Saint Laurent**, France is a cornerstone in this industry. Its influence extends from high-end fashion districts in Paris to worldwide through **Ecommerce**. The observed data set specifically provides **Ecommerce product-list page (PLP) data** on Saint Laurents operations in France. This provides valuable insights into market trends, consumer preferences, and competitive landscape, all essential factors in steering brand strategies and maintaining market relevance in the dynamic world of luxury fashion.
## Link to **dataset**
[France - Saint Laurent - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Product-prices%20France/r/rec6dHiH2JbY9XQx5)
| DBQ/Saint.Laurent.Product.prices.France | [
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| 2023-11-19T08:43:19+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "France - Saint Laurent - Product-level price list", "tags": ["webscraping", "ecommerce", "Saint Laurent", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1240501, "num_examples": 3064}], "download_size": 377349, "dataset_size": 1240501}} | 2023-11-19T08:43:24+00:00 | []
| [
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| TAGS
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| # Saint Laurent web scraped data
## About the website
The Fashion and Luxury retail industry is a prominent economic sector in the EMEA, particularly in France, known globally as the birthplace of Haute Couture. Anchored by prestigious French fashion houses like Saint Laurent, France is a cornerstone in this industry. Its influence extends from high-end fashion districts in Paris to worldwide through Ecommerce. The observed data set specifically provides Ecommerce product-list page (PLP) data on Saint Laurents operations in France. This provides valuable insights into market trends, consumer preferences, and competitive landscape, all essential factors in steering brand strategies and maintaining market relevance in the dynamic world of luxury fashion.
## Link to dataset
France - Saint Laurent - Product-level price list dataset
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"## Link to dataset\n\nFrance - Saint Laurent - Product-level price list dataset"
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]
|
5db7ae2999a0f838aaca8eea0f198a330fbfc1c1 | # Mr Porter web scraped data
## About the website
The **Ecommerce** industry in Americas, particularly in **Mexico**, is a rapidly expanding sector, characterized by burgeoning online retail platforms and evolving shopping habits. Specifically, companies like **Mr. Porter** operate in the luxury online fashion industry where demand is driven by fashion-forward consumers seeking high-end products. With a robust digital infrastructure and growing smartphone penetration, Mexico has emerged as a vibrant market for ecommerce players to explore. The dataset observed comprises **Ecommerce product-list page (PLP) data** on Mr Porter in Mexico, reflecting their extensive product range and their efforts to cater to the evolving sartorial preferences of their Mexican consumer base.
## Link to **dataset**
[Mexico - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Mexico/r/recoekZh2iD3HeBgY)
| DBQ/Mr.Porter.Product.prices.Mexico | [
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| 2023-11-19T08:43:38+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Mexico - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 10096705, "num_examples": 30806}], "download_size": 2355243, "dataset_size": 10096705}} | 2023-11-19T08:43:44+00:00 | []
| [
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| TAGS
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| # Mr Porter web scraped data
## About the website
The Ecommerce industry in Americas, particularly in Mexico, is a rapidly expanding sector, characterized by burgeoning online retail platforms and evolving shopping habits. Specifically, companies like Mr. Porter operate in the luxury online fashion industry where demand is driven by fashion-forward consumers seeking high-end products. With a robust digital infrastructure and growing smartphone penetration, Mexico has emerged as a vibrant market for ecommerce players to explore. The dataset observed comprises Ecommerce product-list page (PLP) data on Mr Porter in Mexico, reflecting their extensive product range and their efforts to cater to the evolving sartorial preferences of their Mexican consumer base.
## Link to dataset
Mexico - Mr Porter - Product-level price list dataset
| [
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|
32c90305e2aff8586bf80442fce602239b7aac06 | # Mr Porter web scraped data
## About the website
Mr Porter operates within the **online luxury fashion retail industry** in the **EMEA** region, particularly focusing on the arena of mens fashion in **Hungary**. This industry has been seeing notable growth owing to increasing internet penetration and the rising consumer interest in high-end fashion. **Ecommerce** has now become a significant component of the luxury fashion business model. The dataset studied particularly consists of **product-list page (PLP) data** for **Mr Porter in Hungary**, which is a valuable source of insight into consumer preferences for luxury mens fashion and the overall performance of the industry in this market.
## Link to **dataset**
[Hungary - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Hungary/r/recevRWtytAuVgwUz)
| DBQ/Mr.Porter.Product.prices.Hungary | [
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| 2023-11-19T08:43:57+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hungary - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9097423, "num_examples": 27686}], "download_size": 2070861, "dataset_size": 9097423}} | 2023-11-19T08:44:03+00:00 | []
| [
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| TAGS
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| # Mr Porter web scraped data
## About the website
Mr Porter operates within the online luxury fashion retail industry in the EMEA region, particularly focusing on the arena of mens fashion in Hungary. This industry has been seeing notable growth owing to increasing internet penetration and the rising consumer interest in high-end fashion. Ecommerce has now become a significant component of the luxury fashion business model. The dataset studied particularly consists of product-list page (PLP) data for Mr Porter in Hungary, which is a valuable source of insight into consumer preferences for luxury mens fashion and the overall performance of the industry in this market.
## Link to dataset
Hungary - Mr Porter - Product-level price list dataset
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"## Link to dataset\n\nHungary - Mr Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n",
"# Mr Porter web scraped data",
"## About the website\n\nMr Porter operates within the online luxury fashion retail industry in the EMEA region, particularly focusing on the arena of mens fashion in Hungary. This industry has been seeing notable growth owing to increasing internet penetration and the rising consumer interest in high-end fashion. Ecommerce has now become a significant component of the luxury fashion business model. The dataset studied particularly consists of product-list page (PLP) data for Mr Porter in Hungary, which is a valuable source of insight into consumer preferences for luxury mens fashion and the overall performance of the industry in this market.",
"## Link to dataset\n\nHungary - Mr Porter - Product-level price list dataset"
]
| [
180,
8,
129,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n# Mr Porter web scraped data## About the website\n\nMr Porter operates within the online luxury fashion retail industry in the EMEA region, particularly focusing on the arena of mens fashion in Hungary. This industry has been seeing notable growth owing to increasing internet penetration and the rising consumer interest in high-end fashion. Ecommerce has now become a significant component of the luxury fashion business model. The dataset studied particularly consists of product-list page (PLP) data for Mr Porter in Hungary, which is a valuable source of insight into consumer preferences for luxury mens fashion and the overall performance of the industry in this market.## Link to dataset\n\nHungary - Mr Porter - Product-level price list dataset"
]
|
f316a93f5192e30684d327abfe9810139357a289 | # Hermes web scraped data
## About the website
The luxury goods industry in the Asia Pacific, particularly in **China**, is experiencing significant growth driven by rising wealth and changing consumer preferences. **Hermes**, a high-end luxury brand, is a notable player within this affluent sector. This industrys success is tied to the robust **ecommerce** landscape in China, characterized by innovative digital platforms with extensive customer reach. The dataset examined contains **Ecommerce product-list page (PLP)** data specifically concerning Hermes operations in China. This provides valuable insights into the brands market position, product range, pricing strategy, and overall ecommerce performance in this vital region. In this dynamic, digital-centric luxury market, such data play an integral role in shaping strategic decisions.
## Link to **dataset**
[China - Hermes - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Hermes%20Product-prices%20China/r/recU7URlmCyJPIUaM)
| DBQ/Hermes.Product.prices.China | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Hermes",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:44:16+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "China - Hermes - Product-level price list", "tags": ["webscraping", "ecommerce", "Hermes", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "int64"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 164463, "num_examples": 468}], "download_size": 41497, "dataset_size": 164463}} | 2023-11-19T08:44:21+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Hermes #fashion #fashion product #image #fashion image #region-us
| # Hermes web scraped data
## About the website
The luxury goods industry in the Asia Pacific, particularly in China, is experiencing significant growth driven by rising wealth and changing consumer preferences. Hermes, a high-end luxury brand, is a notable player within this affluent sector. This industrys success is tied to the robust ecommerce landscape in China, characterized by innovative digital platforms with extensive customer reach. The dataset examined contains Ecommerce product-list page (PLP) data specifically concerning Hermes operations in China. This provides valuable insights into the brands market position, product range, pricing strategy, and overall ecommerce performance in this vital region. In this dynamic, digital-centric luxury market, such data play an integral role in shaping strategic decisions.
## Link to dataset
China - Hermes - Product-level price list dataset
| [
"# Hermes web scraped data",
"## About the website\n\nThe luxury goods industry in the Asia Pacific, particularly in China, is experiencing significant growth driven by rising wealth and changing consumer preferences. Hermes, a high-end luxury brand, is a notable player within this affluent sector. This industrys success is tied to the robust ecommerce landscape in China, characterized by innovative digital platforms with extensive customer reach. The dataset examined contains Ecommerce product-list page (PLP) data specifically concerning Hermes operations in China. This provides valuable insights into the brands market position, product range, pricing strategy, and overall ecommerce performance in this vital region. In this dynamic, digital-centric luxury market, such data play an integral role in shaping strategic decisions.",
"## Link to dataset\n\nChina - Hermes - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Hermes #fashion #fashion product #image #fashion image #region-us \n",
"# Hermes web scraped data",
"## About the website\n\nThe luxury goods industry in the Asia Pacific, particularly in China, is experiencing significant growth driven by rising wealth and changing consumer preferences. Hermes, a high-end luxury brand, is a notable player within this affluent sector. This industrys success is tied to the robust ecommerce landscape in China, characterized by innovative digital platforms with extensive customer reach. The dataset examined contains Ecommerce product-list page (PLP) data specifically concerning Hermes operations in China. This provides valuable insights into the brands market position, product range, pricing strategy, and overall ecommerce performance in this vital region. In this dynamic, digital-centric luxury market, such data play an integral role in shaping strategic decisions.",
"## Link to dataset\n\nChina - Hermes - Product-level price list dataset"
]
| [
178,
7,
168,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Hermes #fashion #fashion product #image #fashion image #region-us \n# Hermes web scraped data## About the website\n\nThe luxury goods industry in the Asia Pacific, particularly in China, is experiencing significant growth driven by rising wealth and changing consumer preferences. Hermes, a high-end luxury brand, is a notable player within this affluent sector. This industrys success is tied to the robust ecommerce landscape in China, characterized by innovative digital platforms with extensive customer reach. The dataset examined contains Ecommerce product-list page (PLP) data specifically concerning Hermes operations in China. This provides valuable insights into the brands market position, product range, pricing strategy, and overall ecommerce performance in this vital region. In this dynamic, digital-centric luxury market, such data play an integral role in shaping strategic decisions.## Link to dataset\n\nChina - Hermes - Product-level price list dataset"
]
|
1c7fe1a74e071d3a37eac64e6dffa3164309cb87 | # Farfetch web scraped data
## About the website
Farfetch operates in the **retail e-commerce industry** in the **EMEA region**, particularly in **France**. The industry involves an online platform where buyers and sellers exchange goods and services. France is characteristically a robust market with a growing number of internet users readily adopting online shopping. In this milieu, Farfetch has successfully positioned itself as a leading player. With a keen focus on luxury fashion, Farfetch has capitalized on the burgeoning appetite for high-end products in the French market. The dataset in question observed **Ecommerce product-list page (PLP)** data specifically on **Farfetch in France**, offering valuable insights into the online retail dynamics at play.
## Link to **dataset**
[France - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20France/r/rec54rX4u2WYwmmzK)
| DBQ/Farfetch.Product.prices.France | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:44:54+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "France - Farfetch - Product-level price list", "tags": ["webscraping", "ecommerce", "Farfetch", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 228976301, "num_examples": 610641}], "download_size": 80985451, "dataset_size": 228976301}} | 2023-11-19T08:45:53+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us
| # Farfetch web scraped data
## About the website
Farfetch operates in the retail e-commerce industry in the EMEA region, particularly in France. The industry involves an online platform where buyers and sellers exchange goods and services. France is characteristically a robust market with a growing number of internet users readily adopting online shopping. In this milieu, Farfetch has successfully positioned itself as a leading player. With a keen focus on luxury fashion, Farfetch has capitalized on the burgeoning appetite for high-end products in the French market. The dataset in question observed Ecommerce product-list page (PLP) data specifically on Farfetch in France, offering valuable insights into the online retail dynamics at play.
## Link to dataset
France - Farfetch - Product-level price list dataset
| [
"# Farfetch web scraped data",
"## About the website\n\nFarfetch operates in the retail e-commerce industry in the EMEA region, particularly in France. The industry involves an online platform where buyers and sellers exchange goods and services. France is characteristically a robust market with a growing number of internet users readily adopting online shopping. In this milieu, Farfetch has successfully positioned itself as a leading player. With a keen focus on luxury fashion, Farfetch has capitalized on the burgeoning appetite for high-end products in the French market. The dataset in question observed Ecommerce product-list page (PLP) data specifically on Farfetch in France, offering valuable insights into the online retail dynamics at play.",
"## Link to dataset\n\nFrance - Farfetch - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us \n",
"# Farfetch web scraped data",
"## About the website\n\nFarfetch operates in the retail e-commerce industry in the EMEA region, particularly in France. The industry involves an online platform where buyers and sellers exchange goods and services. France is characteristically a robust market with a growing number of internet users readily adopting online shopping. In this milieu, Farfetch has successfully positioned itself as a leading player. With a keen focus on luxury fashion, Farfetch has capitalized on the burgeoning appetite for high-end products in the French market. The dataset in question observed Ecommerce product-list page (PLP) data specifically on Farfetch in France, offering valuable insights into the online retail dynamics at play.",
"## Link to dataset\n\nFrance - Farfetch - Product-level price list dataset"
]
| [
179,
8,
157,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us \n# Farfetch web scraped data## About the website\n\nFarfetch operates in the retail e-commerce industry in the EMEA region, particularly in France. The industry involves an online platform where buyers and sellers exchange goods and services. France is characteristically a robust market with a growing number of internet users readily adopting online shopping. In this milieu, Farfetch has successfully positioned itself as a leading player. With a keen focus on luxury fashion, Farfetch has capitalized on the burgeoning appetite for high-end products in the French market. The dataset in question observed Ecommerce product-list page (PLP) data specifically on Farfetch in France, offering valuable insights into the online retail dynamics at play.## Link to dataset\n\nFrance - Farfetch - Product-level price list dataset"
]
|
b34cf5eb025a395326c22396ad235db997b04f85 | # Chanel web scraped data
## About the website
Operating within the highly competitive and exclusive **luxury fashion industry** in EMEA, notably in **Italy**, **Chanel** occupies a premium market position as one of the worlds most recognized fashion brands. This industry is characterized by high-quality, high-priced goods that are primarily targeted at affluent consumers. The industry’s trend towards digitalization has been particularly illustrative in recent years in response to changing consumer habits. The observed dataset contains **Ecommerce product-list page (PLP) data** on Chanels offerings in Italy, showcasing the brands extensive digital presence. Providing insight into Chanels online strategies, the data underscores the critical role of Ecommerce in Italys luxury fashion landscape.
## Link to **dataset**
[Italy - Chanel - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Chanel%20Product-prices%20Italy/r/recwA4XA1XVKUBLa6)
| DBQ/Chanel.Product.prices.Italy | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Chanel",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:46:04+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Italy - Chanel - Product-level price list", "tags": ["webscraping", "ecommerce", "Chanel", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 777179, "num_examples": 1426}], "download_size": 201787, "dataset_size": 777179}} | 2023-11-19T08:46:09+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Chanel #fashion #fashion product #image #fashion image #region-us
| # Chanel web scraped data
## About the website
Operating within the highly competitive and exclusive luxury fashion industry in EMEA, notably in Italy, Chanel occupies a premium market position as one of the worlds most recognized fashion brands. This industry is characterized by high-quality, high-priced goods that are primarily targeted at affluent consumers. The industry’s trend towards digitalization has been particularly illustrative in recent years in response to changing consumer habits. The observed dataset contains Ecommerce product-list page (PLP) data on Chanels offerings in Italy, showcasing the brands extensive digital presence. Providing insight into Chanels online strategies, the data underscores the critical role of Ecommerce in Italys luxury fashion landscape.
## Link to dataset
Italy - Chanel - Product-level price list dataset
| [
"# Chanel web scraped data",
"## About the website\n\nOperating within the highly competitive and exclusive luxury fashion industry in EMEA, notably in Italy, Chanel occupies a premium market position as one of the worlds most recognized fashion brands. This industry is characterized by high-quality, high-priced goods that are primarily targeted at affluent consumers. The industry’s trend towards digitalization has been particularly illustrative in recent years in response to changing consumer habits. The observed dataset contains Ecommerce product-list page (PLP) data on Chanels offerings in Italy, showcasing the brands extensive digital presence. Providing insight into Chanels online strategies, the data underscores the critical role of Ecommerce in Italys luxury fashion landscape.",
"## Link to dataset\n\nItaly - Chanel - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Chanel #fashion #fashion product #image #fashion image #region-us \n",
"# Chanel web scraped data",
"## About the website\n\nOperating within the highly competitive and exclusive luxury fashion industry in EMEA, notably in Italy, Chanel occupies a premium market position as one of the worlds most recognized fashion brands. This industry is characterized by high-quality, high-priced goods that are primarily targeted at affluent consumers. The industry’s trend towards digitalization has been particularly illustrative in recent years in response to changing consumer habits. The observed dataset contains Ecommerce product-list page (PLP) data on Chanels offerings in Italy, showcasing the brands extensive digital presence. Providing insight into Chanels online strategies, the data underscores the critical role of Ecommerce in Italys luxury fashion landscape.",
"## Link to dataset\n\nItaly - Chanel - Product-level price list dataset"
]
| [
178,
6,
165,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Chanel #fashion #fashion product #image #fashion image #region-us \n# Chanel web scraped data## About the website\n\nOperating within the highly competitive and exclusive luxury fashion industry in EMEA, notably in Italy, Chanel occupies a premium market position as one of the worlds most recognized fashion brands. This industry is characterized by high-quality, high-priced goods that are primarily targeted at affluent consumers. The industry’s trend towards digitalization has been particularly illustrative in recent years in response to changing consumer habits. The observed dataset contains Ecommerce product-list page (PLP) data on Chanels offerings in Italy, showcasing the brands extensive digital presence. Providing insight into Chanels online strategies, the data underscores the critical role of Ecommerce in Italys luxury fashion landscape.## Link to dataset\n\nItaly - Chanel - Product-level price list dataset"
]
|
5de0d26f17490e1be13fdac4b737eec50298243c | # Ounass web scraped data
## About the website
Ounass operates within the **e-commerce industry in EMEA**, specifically within the **Luxury Fashion sector**. It is an integral part of the online retail market in the region, offering premium clothing, accessories, and home and beauty products. With a special focus on **Oman**, Ounass has made a significant footprint in the countrys e-commerce landscape, serving high-end customers with an expansive array of products from global luxury brands. The dataset observed contains **Ecommerce product-listing page (PLP) data** with focus on Ounass operations in Oman, offering insights into the companys product offerings, sales, and performance in the region.
## Link to **dataset**
[Oman - Ounass - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Ounass%20Product-prices%20Oman/r/recgVd1MMemHovvyX)
| DBQ/Ounass.Product.prices.Oman | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Ounass",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:46:20+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Oman - Ounass - Product-level price list", "tags": ["webscraping", "ecommerce", "Ounass", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 29313663, "num_examples": 71958}], "download_size": 9088483, "dataset_size": 29313663}} | 2023-11-19T08:46:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Ounass #fashion #fashion product #image #fashion image #region-us
| # Ounass web scraped data
## About the website
Ounass operates within the e-commerce industry in EMEA, specifically within the Luxury Fashion sector. It is an integral part of the online retail market in the region, offering premium clothing, accessories, and home and beauty products. With a special focus on Oman, Ounass has made a significant footprint in the countrys e-commerce landscape, serving high-end customers with an expansive array of products from global luxury brands. The dataset observed contains Ecommerce product-listing page (PLP) data with focus on Ounass operations in Oman, offering insights into the companys product offerings, sales, and performance in the region.
## Link to dataset
Oman - Ounass - Product-level price list dataset
| [
"# Ounass web scraped data",
"## About the website\n\nOunass operates within the e-commerce industry in EMEA, specifically within the Luxury Fashion sector. It is an integral part of the online retail market in the region, offering premium clothing, accessories, and home and beauty products. With a special focus on Oman, Ounass has made a significant footprint in the countrys e-commerce landscape, serving high-end customers with an expansive array of products from global luxury brands. The dataset observed contains Ecommerce product-listing page (PLP) data with focus on Ounass operations in Oman, offering insights into the companys product offerings, sales, and performance in the region.",
"## Link to dataset\n\nOman - Ounass - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Ounass #fashion #fashion product #image #fashion image #region-us \n",
"# Ounass web scraped data",
"## About the website\n\nOunass operates within the e-commerce industry in EMEA, specifically within the Luxury Fashion sector. It is an integral part of the online retail market in the region, offering premium clothing, accessories, and home and beauty products. With a special focus on Oman, Ounass has made a significant footprint in the countrys e-commerce landscape, serving high-end customers with an expansive array of products from global luxury brands. The dataset observed contains Ecommerce product-listing page (PLP) data with focus on Ounass operations in Oman, offering insights into the companys product offerings, sales, and performance in the region.",
"## Link to dataset\n\nOman - Ounass - Product-level price list dataset"
]
| [
180,
8,
151,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Ounass #fashion #fashion product #image #fashion image #region-us \n# Ounass web scraped data## About the website\n\nOunass operates within the e-commerce industry in EMEA, specifically within the Luxury Fashion sector. It is an integral part of the online retail market in the region, offering premium clothing, accessories, and home and beauty products. With a special focus on Oman, Ounass has made a significant footprint in the countrys e-commerce landscape, serving high-end customers with an expansive array of products from global luxury brands. The dataset observed contains Ecommerce product-listing page (PLP) data with focus on Ounass operations in Oman, offering insights into the companys product offerings, sales, and performance in the region.## Link to dataset\n\nOman - Ounass - Product-level price list dataset"
]
|
2d79ef659ae2a60b18d39aaaef8e4664001d37be | # Loro Piana web scraped data
## About the website
_Loro Piana_ operates in the _luxury fashion_ industry in the _United States_, focusing particularly on high-end, quality fabrics and materials. The brand is popular among wealthy Americans who crave its stylish, high-quality goods ranging from clothing to accessories. In this digital era, Loro Piana has also been proactively involved in the _Ecommerce_ space, maximizing their online presence across several platforms. The dataset includes _Ecommerce product-list page (PLP) data on Loro Piana_ in the United States, providing valuable insights into shopper behavior, products, and pricing strategies. The PLP data can significantly influence decisions surrounding merchandising, marketing, and overall digital strategy.
## Link to **dataset**
[United States - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20United%20States/r/receVF2rgpIoU22l6)
| DBQ/Loro.Piana.Product.prices.United.States | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Loro Piana",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:46:45+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "United States - Loro Piana - Product-level price list", "tags": ["webscraping", "ecommerce", "Loro Piana", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 359379, "num_examples": 1042}], "download_size": 125379, "dataset_size": 359379}} | 2023-11-19T08:46:49+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Loro Piana #fashion #fashion product #image #fashion image #region-us
| # Loro Piana web scraped data
## About the website
_Loro Piana_ operates in the _luxury fashion_ industry in the _United States_, focusing particularly on high-end, quality fabrics and materials. The brand is popular among wealthy Americans who crave its stylish, high-quality goods ranging from clothing to accessories. In this digital era, Loro Piana has also been proactively involved in the _Ecommerce_ space, maximizing their online presence across several platforms. The dataset includes _Ecommerce product-list page (PLP) data on Loro Piana_ in the United States, providing valuable insights into shopper behavior, products, and pricing strategies. The PLP data can significantly influence decisions surrounding merchandising, marketing, and overall digital strategy.
## Link to dataset
United States - Loro Piana - Product-level price list dataset
| [
"# Loro Piana web scraped data",
"## About the website\n\n_Loro Piana_ operates in the _luxury fashion_ industry in the _United States_, focusing particularly on high-end, quality fabrics and materials. The brand is popular among wealthy Americans who crave its stylish, high-quality goods ranging from clothing to accessories. In this digital era, Loro Piana has also been proactively involved in the _Ecommerce_ space, maximizing their online presence across several platforms. The dataset includes _Ecommerce product-list page (PLP) data on Loro Piana_ in the United States, providing valuable insights into shopper behavior, products, and pricing strategies. The PLP data can significantly influence decisions surrounding merchandising, marketing, and overall digital strategy.",
"## Link to dataset\n\nUnited States - Loro Piana - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Loro Piana #fashion #fashion product #image #fashion image #region-us \n",
"# Loro Piana web scraped data",
"## About the website\n\n_Loro Piana_ operates in the _luxury fashion_ industry in the _United States_, focusing particularly on high-end, quality fabrics and materials. The brand is popular among wealthy Americans who crave its stylish, high-quality goods ranging from clothing to accessories. In this digital era, Loro Piana has also been proactively involved in the _Ecommerce_ space, maximizing their online presence across several platforms. The dataset includes _Ecommerce product-list page (PLP) data on Loro Piana_ in the United States, providing valuable insights into shopper behavior, products, and pricing strategies. The PLP data can significantly influence decisions surrounding merchandising, marketing, and overall digital strategy.",
"## Link to dataset\n\nUnited States - Loro Piana - Product-level price list dataset"
]
| [
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169,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Loro Piana #fashion #fashion product #image #fashion image #region-us \n# Loro Piana web scraped data## About the website\n\n_Loro Piana_ operates in the _luxury fashion_ industry in the _United States_, focusing particularly on high-end, quality fabrics and materials. The brand is popular among wealthy Americans who crave its stylish, high-quality goods ranging from clothing to accessories. In this digital era, Loro Piana has also been proactively involved in the _Ecommerce_ space, maximizing their online presence across several platforms. The dataset includes _Ecommerce product-list page (PLP) data on Loro Piana_ in the United States, providing valuable insights into shopper behavior, products, and pricing strategies. The PLP data can significantly influence decisions surrounding merchandising, marketing, and overall digital strategy.## Link to dataset\n\nUnited States - Loro Piana - Product-level price list dataset"
]
|
acad2b94292c0b16057c579c118b9dc01182f39b | # Mr Porter web scraped data
## About the website
The industry where **Mr Porter** operates in the EMEA region, especially in **Denmark**, revolves around **Ecommerce and Fashion Retail**. As an upscale online retail destination for men’s style, Mr Porter extensively employs digital marketing tactics to engage its customers. The **Ecommerce industry** in Denmark has shown considerable growth over the years, significantly in the fashion and lifestyle niche that Mr Porter inhabits. **Digital shopping**, influenced by convenience and wide product offerings, is becoming increasingly prevalent. The dataset observed provided valuable insight into Ecommerce product-list page (PLP) data on Mr Porter in Denmark, shedding light on market trends and consumer behavior.
## Link to **dataset**
[Denmark - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Denmark/r/recF33dhCn5AsocBl)
| DBQ/Mr.Porter.Product.prices.Denmark | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:46:58+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Denmark - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 9132560, "num_examples": 27793}], "download_size": 2087145, "dataset_size": 9132560}} | 2023-11-19T08:47:03+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us
| # Mr Porter web scraped data
## About the website
The industry where Mr Porter operates in the EMEA region, especially in Denmark, revolves around Ecommerce and Fashion Retail. As an upscale online retail destination for men’s style, Mr Porter extensively employs digital marketing tactics to engage its customers. The Ecommerce industry in Denmark has shown considerable growth over the years, significantly in the fashion and lifestyle niche that Mr Porter inhabits. Digital shopping, influenced by convenience and wide product offerings, is becoming increasingly prevalent. The dataset observed provided valuable insight into Ecommerce product-list page (PLP) data on Mr Porter in Denmark, shedding light on market trends and consumer behavior.
## Link to dataset
Denmark - Mr Porter - Product-level price list dataset
| [
"# Mr Porter web scraped data",
"## About the website\n\nThe industry where Mr Porter operates in the EMEA region, especially in Denmark, revolves around Ecommerce and Fashion Retail. As an upscale online retail destination for men’s style, Mr Porter extensively employs digital marketing tactics to engage its customers. The Ecommerce industry in Denmark has shown considerable growth over the years, significantly in the fashion and lifestyle niche that Mr Porter inhabits. Digital shopping, influenced by convenience and wide product offerings, is becoming increasingly prevalent. The dataset observed provided valuable insight into Ecommerce product-list page (PLP) data on Mr Porter in Denmark, shedding light on market trends and consumer behavior.",
"## Link to dataset\n\nDenmark - Mr Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n",
"# Mr Porter web scraped data",
"## About the website\n\nThe industry where Mr Porter operates in the EMEA region, especially in Denmark, revolves around Ecommerce and Fashion Retail. As an upscale online retail destination for men’s style, Mr Porter extensively employs digital marketing tactics to engage its customers. The Ecommerce industry in Denmark has shown considerable growth over the years, significantly in the fashion and lifestyle niche that Mr Porter inhabits. Digital shopping, influenced by convenience and wide product offerings, is becoming increasingly prevalent. The dataset observed provided valuable insight into Ecommerce product-list page (PLP) data on Mr Porter in Denmark, shedding light on market trends and consumer behavior.",
"## Link to dataset\n\nDenmark - Mr Porter - Product-level price list dataset"
]
| [
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n# Mr Porter web scraped data## About the website\n\nThe industry where Mr Porter operates in the EMEA region, especially in Denmark, revolves around Ecommerce and Fashion Retail. As an upscale online retail destination for men’s style, Mr Porter extensively employs digital marketing tactics to engage its customers. The Ecommerce industry in Denmark has shown considerable growth over the years, significantly in the fashion and lifestyle niche that Mr Porter inhabits. Digital shopping, influenced by convenience and wide product offerings, is becoming increasingly prevalent. The dataset observed provided valuable insight into Ecommerce product-list page (PLP) data on Mr Porter in Denmark, shedding light on market trends and consumer behavior.## Link to dataset\n\nDenmark - Mr Porter - Product-level price list dataset"
]
|
a60404146af18d7c68cc3a3def425ae22990bca0 | # Fendi web scraped data
## About the website
In the **EMEA** region, particularly in **Italy**, the **luxury fashion industry** has an immense influence and it significantly contributes to Italys economy. Brands such as **Fendi** are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of **Ecommerce**. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development.
## Link to **dataset**
[Italy - Fendi - Fashion Standard Categories dataset](https://www.databoutique.com/buy-data-page/Fendi%20Standard%20Categories%20Italy/r/recrd9vOREnRQ68V1)
| DBQ/Fendi.Standard.Categories.Italy | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Fendi",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:47:11+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Italy - Fendi - Fashion Standard Categories", "tags": ["webscraping", "ecommerce", "Fendi", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "dbq_prd_type", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "website_name", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "tag_field", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 271821, "num_examples": 3055}], "download_size": 56203, "dataset_size": 271821}} | 2023-11-19T08:47:15+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Fendi #fashion #fashion product #image #fashion image #region-us
| # Fendi web scraped data
## About the website
In the EMEA region, particularly in Italy, the luxury fashion industry has an immense influence and it significantly contributes to Italys economy. Brands such as Fendi are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of Ecommerce. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development.
## Link to dataset
Italy - Fendi - Fashion Standard Categories dataset
| [
"# Fendi web scraped data",
"## About the website\n\nIn the EMEA region, particularly in Italy, the luxury fashion industry has an immense influence and it significantly contributes to Italys economy. Brands such as Fendi are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of Ecommerce. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development.",
"## Link to dataset\n\nItaly - Fendi - Fashion Standard Categories dataset"
]
| [
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"# Fendi web scraped data",
"## About the website\n\nIn the EMEA region, particularly in Italy, the luxury fashion industry has an immense influence and it significantly contributes to Italys economy. Brands such as Fendi are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of Ecommerce. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development.",
"## Link to dataset\n\nItaly - Fendi - Fashion Standard Categories dataset"
]
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]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Fendi #fashion #fashion product #image #fashion image #region-us \n# Fendi web scraped data## About the website\n\nIn the EMEA region, particularly in Italy, the luxury fashion industry has an immense influence and it significantly contributes to Italys economy. Brands such as Fendi are prominent players in this sector. Emphasizing on haute couture, ready-to-wear clothing, leather goods, shoes, fragrances, eyewear, timepieces and accessories, the industry has seen significant growth with the adoption of Ecommerce. Our dataset provides an insightful look at Ecommerce product tags data on Fendi products in Italy. The research data aids in understanding the online buying behavior of luxury fashion consumers in Italy, providing great potential for market analysis and strategy development.## Link to dataset\n\nItaly - Fendi - Fashion Standard Categories dataset"
]
|
60b97a0ebdee3430888c3b7e41d3dee7c1328714 | # Net-a-Porter web scraped data
## About the website
The **Ecommerce industry** is a growing sector in the Americas, especially in **Canada**. Businesses in this realm focus on the buying and selling of goods and services over electronic systems, particularly the internet. **Net-a-Porter**, as an online retail brand, operates within this sector by offering a wide range of luxury fashion items to their Canadian clientele. The dataset analyzed presents relevant **Ecommerce product-list page (PLP) data** from Net-a-Porters operations in Canada. This data provides insightful details about product availability, pricing, customer preferences and overall market trends within the Canadian digital retail landscape.
## Link to **dataset**
[Canada - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Canada/r/reciFH7C8uAD2ZQgq)
| DBQ/Net.a.Porter.Product.prices.Canada | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:47:26+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Canada - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17615783, "num_examples": 43168}], "download_size": 5269305, "dataset_size": 17615783}} | 2023-11-19T08:47:34+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The Ecommerce industry is a growing sector in the Americas, especially in Canada. Businesses in this realm focus on the buying and selling of goods and services over electronic systems, particularly the internet. Net-a-Porter, as an online retail brand, operates within this sector by offering a wide range of luxury fashion items to their Canadian clientele. The dataset analyzed presents relevant Ecommerce product-list page (PLP) data from Net-a-Porters operations in Canada. This data provides insightful details about product availability, pricing, customer preferences and overall market trends within the Canadian digital retail landscape.
## Link to dataset
Canada - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce industry is a growing sector in the Americas, especially in Canada. Businesses in this realm focus on the buying and selling of goods and services over electronic systems, particularly the internet. Net-a-Porter, as an online retail brand, operates within this sector by offering a wide range of luxury fashion items to their Canadian clientele. The dataset analyzed presents relevant Ecommerce product-list page (PLP) data from Net-a-Porters operations in Canada. This data provides insightful details about product availability, pricing, customer preferences and overall market trends within the Canadian digital retail landscape.",
"## Link to dataset\n\nCanada - Net-a-Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n",
"# Net-a-Porter web scraped data",
"## About the website\n\nThe Ecommerce industry is a growing sector in the Americas, especially in Canada. Businesses in this realm focus on the buying and selling of goods and services over electronic systems, particularly the internet. Net-a-Porter, as an online retail brand, operates within this sector by offering a wide range of luxury fashion items to their Canadian clientele. The dataset analyzed presents relevant Ecommerce product-list page (PLP) data from Net-a-Porters operations in Canada. This data provides insightful details about product availability, pricing, customer preferences and overall market trends within the Canadian digital retail landscape.",
"## Link to dataset\n\nCanada - Net-a-Porter - Product-level price list dataset"
]
| [
177,
11,
142,
21
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe Ecommerce industry is a growing sector in the Americas, especially in Canada. Businesses in this realm focus on the buying and selling of goods and services over electronic systems, particularly the internet. Net-a-Porter, as an online retail brand, operates within this sector by offering a wide range of luxury fashion items to their Canadian clientele. The dataset analyzed presents relevant Ecommerce product-list page (PLP) data from Net-a-Porters operations in Canada. This data provides insightful details about product availability, pricing, customer preferences and overall market trends within the Canadian digital retail landscape.## Link to dataset\n\nCanada - Net-a-Porter - Product-level price list dataset"
]
|
d7dc7c4b7b6b0955a88eccdce1a6715098d89934 | # Ounass web scraped data
## About the website
Ounass operates in the dynamic and fast-growing **E-commerce industry** within the **EMEA region**, with a strong focus particularly in **Qatar**. The Qatari E-commerce industry is experiencing significant growth, fueled by the rapid digitization, high internet penetration, and a strong inclination towards online shopping among consumers. Particularly, the luxury segment, in which Ounass operates, witnesses a high demand driven by the nations affluent population. The dataset observed includes **Ecommerce product-list page (PLP) data** on Ounass operations in Qatar, offering comprehensive insights into the business strategies, consumer behavior, and emerging trends in this thriving marketplace.
## Link to **dataset**
[Qatar - Ounass - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Ounass%20Product-prices%20Qatar/r/rec6BSAJjNfjYBEUp)
| DBQ/Ounass.Product.prices.Qatar | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Ounass",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:47:47+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Qatar - Ounass - Product-level price list", "tags": ["webscraping", "ecommerce", "Ounass", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 28197555, "num_examples": 69623}], "download_size": 8717370, "dataset_size": 28197555}} | 2023-11-19T08:47:57+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Ounass #fashion #fashion product #image #fashion image #region-us
| # Ounass web scraped data
## About the website
Ounass operates in the dynamic and fast-growing E-commerce industry within the EMEA region, with a strong focus particularly in Qatar. The Qatari E-commerce industry is experiencing significant growth, fueled by the rapid digitization, high internet penetration, and a strong inclination towards online shopping among consumers. Particularly, the luxury segment, in which Ounass operates, witnesses a high demand driven by the nations affluent population. The dataset observed includes Ecommerce product-list page (PLP) data on Ounass operations in Qatar, offering comprehensive insights into the business strategies, consumer behavior, and emerging trends in this thriving marketplace.
## Link to dataset
Qatar - Ounass - Product-level price list dataset
| [
"# Ounass web scraped data",
"## About the website\n\nOunass operates in the dynamic and fast-growing E-commerce industry within the EMEA region, with a strong focus particularly in Qatar. The Qatari E-commerce industry is experiencing significant growth, fueled by the rapid digitization, high internet penetration, and a strong inclination towards online shopping among consumers. Particularly, the luxury segment, in which Ounass operates, witnesses a high demand driven by the nations affluent population. The dataset observed includes Ecommerce product-list page (PLP) data on Ounass operations in Qatar, offering comprehensive insights into the business strategies, consumer behavior, and emerging trends in this thriving marketplace.",
"## Link to dataset\n\nQatar - Ounass - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Ounass #fashion #fashion product #image #fashion image #region-us \n",
"# Ounass web scraped data",
"## About the website\n\nOunass operates in the dynamic and fast-growing E-commerce industry within the EMEA region, with a strong focus particularly in Qatar. The Qatari E-commerce industry is experiencing significant growth, fueled by the rapid digitization, high internet penetration, and a strong inclination towards online shopping among consumers. Particularly, the luxury segment, in which Ounass operates, witnesses a high demand driven by the nations affluent population. The dataset observed includes Ecommerce product-list page (PLP) data on Ounass operations in Qatar, offering comprehensive insights into the business strategies, consumer behavior, and emerging trends in this thriving marketplace.",
"## Link to dataset\n\nQatar - Ounass - Product-level price list dataset"
]
| [
180,
8,
157,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Ounass #fashion #fashion product #image #fashion image #region-us \n# Ounass web scraped data## About the website\n\nOunass operates in the dynamic and fast-growing E-commerce industry within the EMEA region, with a strong focus particularly in Qatar. The Qatari E-commerce industry is experiencing significant growth, fueled by the rapid digitization, high internet penetration, and a strong inclination towards online shopping among consumers. Particularly, the luxury segment, in which Ounass operates, witnesses a high demand driven by the nations affluent population. The dataset observed includes Ecommerce product-list page (PLP) data on Ounass operations in Qatar, offering comprehensive insights into the business strategies, consumer behavior, and emerging trends in this thriving marketplace.## Link to dataset\n\nQatar - Ounass - Product-level price list dataset"
]
|
6d7c02dad4e544cbe057b136b291886de150e045 | # Loro Piana web scraped data
## About the website
In the European, Middle Eastern and African (EMEA) region, specifically France, **Loro Piana** operates within the **luxury fashion industry**. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the "fashion capital of the world". The industry is continuously evolving, incorporating **Ecommerce** and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of **Ecommerce product-list page (PLP) data** on Loro Piana in France.
## Link to **dataset**
[France - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20France/r/recyuT3Im5gEtayt6)
| DBQ/Loro.Piana.Product.prices.France | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Loro Piana",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:48:06+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "France - Loro Piana - Product-level price list", "tags": ["webscraping", "ecommerce", "Loro Piana", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 389433, "num_examples": 1128}], "download_size": 131337, "dataset_size": 389433}} | 2023-11-19T08:48:10+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Loro Piana #fashion #fashion product #image #fashion image #region-us
| # Loro Piana web scraped data
## About the website
In the European, Middle Eastern and African (EMEA) region, specifically France, Loro Piana operates within the luxury fashion industry. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the "fashion capital of the world". The industry is continuously evolving, incorporating Ecommerce and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of Ecommerce product-list page (PLP) data on Loro Piana in France.
## Link to dataset
France - Loro Piana - Product-level price list dataset
| [
"# Loro Piana web scraped data",
"## About the website\n\nIn the European, Middle Eastern and African (EMEA) region, specifically France, Loro Piana operates within the luxury fashion industry. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the \"fashion capital of the world\". The industry is continuously evolving, incorporating Ecommerce and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of Ecommerce product-list page (PLP) data on Loro Piana in France.",
"## Link to dataset\n\nFrance - Loro Piana - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Loro Piana #fashion #fashion product #image #fashion image #region-us \n",
"# Loro Piana web scraped data",
"## About the website\n\nIn the European, Middle Eastern and African (EMEA) region, specifically France, Loro Piana operates within the luxury fashion industry. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the \"fashion capital of the world\". The industry is continuously evolving, incorporating Ecommerce and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of Ecommerce product-list page (PLP) data on Loro Piana in France.",
"## Link to dataset\n\nFrance - Loro Piana - Product-level price list dataset"
]
| [
180,
9,
163,
19
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Loro Piana #fashion #fashion product #image #fashion image #region-us \n# Loro Piana web scraped data## About the website\n\nIn the European, Middle Eastern and African (EMEA) region, specifically France, Loro Piana operates within the luxury fashion industry. This sector is renowned for its fine craftsmanship, superior quality, and the high value attached to its products. France, in particular, has long been a global center of luxury fashion with its capital, Paris, often referred to as the \"fashion capital of the world\". The industry is continuously evolving, incorporating Ecommerce and online platforms into their business models to meet the changing demands and preferences of its upscale clientele. In this context, we observed a dataset consisting of Ecommerce product-list page (PLP) data on Loro Piana in France.## Link to dataset\n\nFrance - Loro Piana - Product-level price list dataset"
]
|
f229c6caa81ec969e7e935b9de3006a823543896 | # Dior web scraped data
## About the website
The **fashion and luxury goods industry** in Asia Pacific, particularly in **Hong Kong**, is experiencing fast growth assisted by wealth creation and the consumers increasing desire for high-end products. **Dior**, one of the luxurious brands, has successfully penetrated this market. Through **ecommerce**, Dior has made strides in capturing more of the Hong Kong market. One dataset observed comprises **Ecommerce product-list page (PLP) data** on Dior in Hong Kong. An in-depth understanding of this data can provide better insights and strategies to further expand Diors reach. To access this information on other geographies or data types, visit the Dior main page [here](https://www.databoutique.com/buy-data-list-subset/Dior web scraped data/r/recmfaShm06mYeLTg).
## Link to **dataset**
[Hong Kong - Dior - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Dior%20Product-prices%20Hong%20Kong/r/recMSnjk1RoCuRXL4)
| DBQ/Dior.Product.prices.Hong.Kong | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Dior",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:48:18+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hong Kong - Dior - Product-level price list", "tags": ["webscraping", "ecommerce", "Dior", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1740690, "num_examples": 4426}], "download_size": 527496, "dataset_size": 1740690}} | 2023-11-19T08:48:23+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Dior #fashion #fashion product #image #fashion image #region-us
| # Dior web scraped data
## About the website
The fashion and luxury goods industry in Asia Pacific, particularly in Hong Kong, is experiencing fast growth assisted by wealth creation and the consumers increasing desire for high-end products. Dior, one of the luxurious brands, has successfully penetrated this market. Through ecommerce, Dior has made strides in capturing more of the Hong Kong market. One dataset observed comprises Ecommerce product-list page (PLP) data on Dior in Hong Kong. An in-depth understanding of this data can provide better insights and strategies to further expand Diors reach. To access this information on other geographies or data types, visit the Dior main page here.
## Link to dataset
Hong Kong - Dior - Product-level price list dataset
| [
"# Dior web scraped data",
"## About the website\n\nThe fashion and luxury goods industry in Asia Pacific, particularly in Hong Kong, is experiencing fast growth assisted by wealth creation and the consumers increasing desire for high-end products. Dior, one of the luxurious brands, has successfully penetrated this market. Through ecommerce, Dior has made strides in capturing more of the Hong Kong market. One dataset observed comprises Ecommerce product-list page (PLP) data on Dior in Hong Kong. An in-depth understanding of this data can provide better insights and strategies to further expand Diors reach. To access this information on other geographies or data types, visit the Dior main page here.",
"## Link to dataset\n\nHong Kong - Dior - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Dior #fashion #fashion product #image #fashion image #region-us \n",
"# Dior web scraped data",
"## About the website\n\nThe fashion and luxury goods industry in Asia Pacific, particularly in Hong Kong, is experiencing fast growth assisted by wealth creation and the consumers increasing desire for high-end products. Dior, one of the luxurious brands, has successfully penetrated this market. Through ecommerce, Dior has made strides in capturing more of the Hong Kong market. One dataset observed comprises Ecommerce product-list page (PLP) data on Dior in Hong Kong. An in-depth understanding of this data can provide better insights and strategies to further expand Diors reach. To access this information on other geographies or data types, visit the Dior main page here.",
"## Link to dataset\n\nHong Kong - Dior - Product-level price list dataset"
]
| [
178,
7,
153,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Dior #fashion #fashion product #image #fashion image #region-us \n# Dior web scraped data## About the website\n\nThe fashion and luxury goods industry in Asia Pacific, particularly in Hong Kong, is experiencing fast growth assisted by wealth creation and the consumers increasing desire for high-end products. Dior, one of the luxurious brands, has successfully penetrated this market. Through ecommerce, Dior has made strides in capturing more of the Hong Kong market. One dataset observed comprises Ecommerce product-list page (PLP) data on Dior in Hong Kong. An in-depth understanding of this data can provide better insights and strategies to further expand Diors reach. To access this information on other geographies or data types, visit the Dior main page here.## Link to dataset\n\nHong Kong - Dior - Product-level price list dataset"
]
|
769b0a485387fdf62fecec51f1352ff8feb47f81 | # Saint Laurent web scraped data
## About the website
The **fashion industry** in the **Asia Pacific** region, particularly in **Singapore**, has seen a considerable upsurge with the onset of luxury brands like **Saint Laurent**. The aged-old perception of Singapore as a fashion follower is being gradually transformed with global luxury brands marking a conspicuous presence. The **luxury retail industry** has been significantly driven by the expansion of **ecommerce**. Specifically, ecommerce product-list page (PLP) data has relayed valuable insights about the brands performance and customer preferences. Ecommerce, coupled with digital media, has reshaped the luxury retail landscape ushering in promising growth trajectories.
## Link to **dataset**
[Singapore - Saint Laurent - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Product-prices%20Singapore/r/recgVrK0QxNGoYRz1)
| DBQ/Saint.Laurent.Product.prices.Singapore | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Saint Laurent",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:48:30+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Singapore - Saint Laurent - Product-level price list", "tags": ["webscraping", "ecommerce", "Saint Laurent", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1082234, "num_examples": 2675}], "download_size": 320252, "dataset_size": 1082234}} | 2023-11-19T08:48:34+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Saint Laurent #fashion #fashion product #image #fashion image #region-us
| # Saint Laurent web scraped data
## About the website
The fashion industry in the Asia Pacific region, particularly in Singapore, has seen a considerable upsurge with the onset of luxury brands like Saint Laurent. The aged-old perception of Singapore as a fashion follower is being gradually transformed with global luxury brands marking a conspicuous presence. The luxury retail industry has been significantly driven by the expansion of ecommerce. Specifically, ecommerce product-list page (PLP) data has relayed valuable insights about the brands performance and customer preferences. Ecommerce, coupled with digital media, has reshaped the luxury retail landscape ushering in promising growth trajectories.
## Link to dataset
Singapore - Saint Laurent - Product-level price list dataset
| [
"# Saint Laurent web scraped data",
"## About the website\n\nThe fashion industry in the Asia Pacific region, particularly in Singapore, has seen a considerable upsurge with the onset of luxury brands like Saint Laurent. The aged-old perception of Singapore as a fashion follower is being gradually transformed with global luxury brands marking a conspicuous presence. The luxury retail industry has been significantly driven by the expansion of ecommerce. Specifically, ecommerce product-list page (PLP) data has relayed valuable insights about the brands performance and customer preferences. Ecommerce, coupled with digital media, has reshaped the luxury retail landscape ushering in promising growth trajectories.",
"## Link to dataset\n\nSingapore - Saint Laurent - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Saint Laurent #fashion #fashion product #image #fashion image #region-us \n",
"# Saint Laurent web scraped data",
"## About the website\n\nThe fashion industry in the Asia Pacific region, particularly in Singapore, has seen a considerable upsurge with the onset of luxury brands like Saint Laurent. The aged-old perception of Singapore as a fashion follower is being gradually transformed with global luxury brands marking a conspicuous presence. The luxury retail industry has been significantly driven by the expansion of ecommerce. Specifically, ecommerce product-list page (PLP) data has relayed valuable insights about the brands performance and customer preferences. Ecommerce, coupled with digital media, has reshaped the luxury retail landscape ushering in promising growth trajectories.",
"## Link to dataset\n\nSingapore - Saint Laurent - Product-level price list dataset"
]
| [
178,
7,
151,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Saint Laurent #fashion #fashion product #image #fashion image #region-us \n# Saint Laurent web scraped data## About the website\n\nThe fashion industry in the Asia Pacific region, particularly in Singapore, has seen a considerable upsurge with the onset of luxury brands like Saint Laurent. The aged-old perception of Singapore as a fashion follower is being gradually transformed with global luxury brands marking a conspicuous presence. The luxury retail industry has been significantly driven by the expansion of ecommerce. Specifically, ecommerce product-list page (PLP) data has relayed valuable insights about the brands performance and customer preferences. Ecommerce, coupled with digital media, has reshaped the luxury retail landscape ushering in promising growth trajectories.## Link to dataset\n\nSingapore - Saint Laurent - Product-level price list dataset"
]
|
e25f1e627e99003f8e1225088340613316223a7f | # Gucci web scraped data
## About the website
The **luxury fashion industry** is a significant sector in the EMEA region, and especially in **Hungary**. The industry has found its footing in the digital world with the advent of **Ecommerce**, resulting in a significant surge in online sales. **Gucci**, an eminent player in this field, has established a strong online presence to capitalize on this trend. The dataset observed pertains to **Ecommerce product-list page (PLP) data** for Gucci in Hungary, where the brand witnesses considerable demand. This data provides valuable insights into consumer preferences and shopping patterns, helping players in this industry shape strategies to foster growth.
## Link to **dataset**
[Hungary - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Hungary/r/rec95J1ypx0pNxrgA)
| DBQ/Gucci.Product.prices.Hungary | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:48:41+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Hungary - Gucci - Product-level price list", "tags": ["webscraping", "ecommerce", "Gucci", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2375242, "num_examples": 4976}], "download_size": 686750, "dataset_size": 2375242}} | 2023-11-19T08:48:47+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us
| # Gucci web scraped data
## About the website
The luxury fashion industry is a significant sector in the EMEA region, and especially in Hungary. The industry has found its footing in the digital world with the advent of Ecommerce, resulting in a significant surge in online sales. Gucci, an eminent player in this field, has established a strong online presence to capitalize on this trend. The dataset observed pertains to Ecommerce product-list page (PLP) data for Gucci in Hungary, where the brand witnesses considerable demand. This data provides valuable insights into consumer preferences and shopping patterns, helping players in this industry shape strategies to foster growth.
## Link to dataset
Hungary - Gucci - Product-level price list dataset
| [
"# Gucci web scraped data",
"## About the website\n\nThe luxury fashion industry is a significant sector in the EMEA region, and especially in Hungary. The industry has found its footing in the digital world with the advent of Ecommerce, resulting in a significant surge in online sales. Gucci, an eminent player in this field, has established a strong online presence to capitalize on this trend. The dataset observed pertains to Ecommerce product-list page (PLP) data for Gucci in Hungary, where the brand witnesses considerable demand. This data provides valuable insights into consumer preferences and shopping patterns, helping players in this industry shape strategies to foster growth.",
"## Link to dataset\n\nHungary - Gucci - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us \n",
"# Gucci web scraped data",
"## About the website\n\nThe luxury fashion industry is a significant sector in the EMEA region, and especially in Hungary. The industry has found its footing in the digital world with the advent of Ecommerce, resulting in a significant surge in online sales. Gucci, an eminent player in this field, has established a strong online presence to capitalize on this trend. The dataset observed pertains to Ecommerce product-list page (PLP) data for Gucci in Hungary, where the brand witnesses considerable demand. This data provides valuable insights into consumer preferences and shopping patterns, helping players in this industry shape strategies to foster growth.",
"## Link to dataset\n\nHungary - Gucci - Product-level price list dataset"
]
| [
178,
7,
137,
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| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Gucci #fashion #fashion product #image #fashion image #region-us \n# Gucci web scraped data## About the website\n\nThe luxury fashion industry is a significant sector in the EMEA region, and especially in Hungary. The industry has found its footing in the digital world with the advent of Ecommerce, resulting in a significant surge in online sales. Gucci, an eminent player in this field, has established a strong online presence to capitalize on this trend. The dataset observed pertains to Ecommerce product-list page (PLP) data for Gucci in Hungary, where the brand witnesses considerable demand. This data provides valuable insights into consumer preferences and shopping patterns, helping players in this industry shape strategies to foster growth.## Link to dataset\n\nHungary - Gucci - Product-level price list dataset"
]
|
42a730aff6253fb8e1806f33d898f35437535faa | # Net-a-Porter web scraped data
## About the website
The **EMEA industry**, particularly in **Austria**, is a dynamic and competitive marketplace. It is characterized by a rapidly growing **e-commerce** sector, driven by technological advancements and changing consumer behavior. A significant player in this space is **Net-a-Porter**, a leading global online luxury fashion retailer. The observed dataset provides detailed **Ecommerce product-list page (PLP)** data on Net-a-Porter in Austria. The **PLP** data can provide valuable insights into customer preferences, shopping behavior, and market trends. This data is crucial for businesses to remain competitive and adapt to the needs of the digital consumer in the modern **Austrian e-commerce landscape**.
## Link to **dataset**
[Austria - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Austria/r/recnisw1IeM92ajoK)
| DBQ/Net.a.Porter.Product.prices.Austria | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:48:56+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Austria - Net-a-Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Net", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 17632573, "num_examples": 43237}], "download_size": 5526380, "dataset_size": 17632573}} | 2023-11-19T08:49:04+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us
| # Net-a-Porter web scraped data
## About the website
The EMEA industry, particularly in Austria, is a dynamic and competitive marketplace. It is characterized by a rapidly growing e-commerce sector, driven by technological advancements and changing consumer behavior. A significant player in this space is Net-a-Porter, a leading global online luxury fashion retailer. The observed dataset provides detailed Ecommerce product-list page (PLP) data on Net-a-Porter in Austria. The PLP data can provide valuable insights into customer preferences, shopping behavior, and market trends. This data is crucial for businesses to remain competitive and adapt to the needs of the digital consumer in the modern Austrian e-commerce landscape.
## Link to dataset
Austria - Net-a-Porter - Product-level price list dataset
| [
"# Net-a-Porter web scraped data",
"## About the website\n\nThe EMEA industry, particularly in Austria, is a dynamic and competitive marketplace. It is characterized by a rapidly growing e-commerce sector, driven by technological advancements and changing consumer behavior. A significant player in this space is Net-a-Porter, a leading global online luxury fashion retailer. The observed dataset provides detailed Ecommerce product-list page (PLP) data on Net-a-Porter in Austria. The PLP data can provide valuable insights into customer preferences, shopping behavior, and market trends. This data is crucial for businesses to remain competitive and adapt to the needs of the digital consumer in the modern Austrian e-commerce landscape.",
"## Link to dataset\n\nAustria - Net-a-Porter - Product-level price list dataset"
]
| [
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"# Net-a-Porter web scraped data",
"## About the website\n\nThe EMEA industry, particularly in Austria, is a dynamic and competitive marketplace. It is characterized by a rapidly growing e-commerce sector, driven by technological advancements and changing consumer behavior. A significant player in this space is Net-a-Porter, a leading global online luxury fashion retailer. The observed dataset provides detailed Ecommerce product-list page (PLP) data on Net-a-Porter in Austria. The PLP data can provide valuable insights into customer preferences, shopping behavior, and market trends. This data is crucial for businesses to remain competitive and adapt to the needs of the digital consumer in the modern Austrian e-commerce landscape.",
"## Link to dataset\n\nAustria - Net-a-Porter - Product-level price list dataset"
]
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148,
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]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Net #fashion #fashion product #image #fashion image #region-us \n# Net-a-Porter web scraped data## About the website\n\nThe EMEA industry, particularly in Austria, is a dynamic and competitive marketplace. It is characterized by a rapidly growing e-commerce sector, driven by technological advancements and changing consumer behavior. A significant player in this space is Net-a-Porter, a leading global online luxury fashion retailer. The observed dataset provides detailed Ecommerce product-list page (PLP) data on Net-a-Porter in Austria. The PLP data can provide valuable insights into customer preferences, shopping behavior, and market trends. This data is crucial for businesses to remain competitive and adapt to the needs of the digital consumer in the modern Austrian e-commerce landscape.## Link to dataset\n\nAustria - Net-a-Porter - Product-level price list dataset"
]
|
fd495b208c93e35dd7fb14468e39cc34434cb9a8 | # Mr Porter web scraped data
## About the website
The **ecommerce industry** in the Asia Pacific region, particularly in **Kazakhstan**, has witnessed substantial growth, fueled by increasing internet penetration and booming digital transformation. Spearheading online luxury fashion retail, **Mr Porter** has established a significant presence in this emerging market, setting a high standard within the industry. The **ecommerce product-list page (PLP) data** demonstrates Mr Porters reach and impact in Kazakhstan, exhibiting an excellent performance in providing a seamless customer experience, showcasing a diverse product range and forging strong relationships with local and international brands. It testament to the growing significance of ecommerce in Kazakhstan and the potential for further growth in this arena.
## Link to **dataset**
[Kazakhstan - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Kazakhstan/r/recoRzrCwnnzeA9Ir)
| DBQ/Mr.Porter.Product.prices.Kazakhstan | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:49:13+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Kazakhstan - Mr Porter - Product-level price list", "tags": ["webscraping", "ecommerce", "Mr Porter", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 8476677, "num_examples": 25806}], "download_size": 1970982, "dataset_size": 8476677}} | 2023-11-19T08:49:18+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us
| # Mr Porter web scraped data
## About the website
The ecommerce industry in the Asia Pacific region, particularly in Kazakhstan, has witnessed substantial growth, fueled by increasing internet penetration and booming digital transformation. Spearheading online luxury fashion retail, Mr Porter has established a significant presence in this emerging market, setting a high standard within the industry. The ecommerce product-list page (PLP) data demonstrates Mr Porters reach and impact in Kazakhstan, exhibiting an excellent performance in providing a seamless customer experience, showcasing a diverse product range and forging strong relationships with local and international brands. It testament to the growing significance of ecommerce in Kazakhstan and the potential for further growth in this arena.
## Link to dataset
Kazakhstan - Mr Porter - Product-level price list dataset
| [
"# Mr Porter web scraped data",
"## About the website\n\nThe ecommerce industry in the Asia Pacific region, particularly in Kazakhstan, has witnessed substantial growth, fueled by increasing internet penetration and booming digital transformation. Spearheading online luxury fashion retail, Mr Porter has established a significant presence in this emerging market, setting a high standard within the industry. The ecommerce product-list page (PLP) data demonstrates Mr Porters reach and impact in Kazakhstan, exhibiting an excellent performance in providing a seamless customer experience, showcasing a diverse product range and forging strong relationships with local and international brands. It testament to the growing significance of ecommerce in Kazakhstan and the potential for further growth in this arena.",
"## Link to dataset\n\nKazakhstan - Mr Porter - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n",
"# Mr Porter web scraped data",
"## About the website\n\nThe ecommerce industry in the Asia Pacific region, particularly in Kazakhstan, has witnessed substantial growth, fueled by increasing internet penetration and booming digital transformation. Spearheading online luxury fashion retail, Mr Porter has established a significant presence in this emerging market, setting a high standard within the industry. The ecommerce product-list page (PLP) data demonstrates Mr Porters reach and impact in Kazakhstan, exhibiting an excellent performance in providing a seamless customer experience, showcasing a diverse product range and forging strong relationships with local and international brands. It testament to the growing significance of ecommerce in Kazakhstan and the potential for further growth in this arena.",
"## Link to dataset\n\nKazakhstan - Mr Porter - Product-level price list dataset"
]
| [
180,
8,
153,
20
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Mr Porter #fashion #fashion product #image #fashion image #region-us \n# Mr Porter web scraped data## About the website\n\nThe ecommerce industry in the Asia Pacific region, particularly in Kazakhstan, has witnessed substantial growth, fueled by increasing internet penetration and booming digital transformation. Spearheading online luxury fashion retail, Mr Porter has established a significant presence in this emerging market, setting a high standard within the industry. The ecommerce product-list page (PLP) data demonstrates Mr Porters reach and impact in Kazakhstan, exhibiting an excellent performance in providing a seamless customer experience, showcasing a diverse product range and forging strong relationships with local and international brands. It testament to the growing significance of ecommerce in Kazakhstan and the potential for further growth in this arena.## Link to dataset\n\nKazakhstan - Mr Porter - Product-level price list dataset"
]
|
5232ecc41bd1cb78414edb378f7543ac0eacd3e0 | # Fendi web scraped data
## About the website
The luxury goods industry is in a dynamic state of expansion and growth in the Asia Pacific region. At the forefront of this trend is **Singapore**, a key player in this sector. The city-state is a hub for high-end fashion brands such as **Fendi**. Notably, **Fendi in Singapore** has widely adopted the use of technology, launching innovative digital campaigns and making excellent use of **Ecommerce**. Moreover, **Product-List Page (PLP) data** have played a significant role in the companys operations, allowing them to track consumer behavior and preferences, further tailoring their offerings to the discerning tastes of their customers.
## Link to **dataset**
[Singapore - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20Singapore/r/receP8aWwUPlSSsvo)
| DBQ/Fendi.Product.prices.Singapore | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Fendi",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:49:26+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Singapore - Fendi - Product-level price list", "tags": ["webscraping", "ecommerce", "Fendi", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 566974, "num_examples": 1450}], "download_size": 187873, "dataset_size": 566974}} | 2023-11-19T08:49:30+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Fendi #fashion #fashion product #image #fashion image #region-us
| # Fendi web scraped data
## About the website
The luxury goods industry is in a dynamic state of expansion and growth in the Asia Pacific region. At the forefront of this trend is Singapore, a key player in this sector. The city-state is a hub for high-end fashion brands such as Fendi. Notably, Fendi in Singapore has widely adopted the use of technology, launching innovative digital campaigns and making excellent use of Ecommerce. Moreover, Product-List Page (PLP) data have played a significant role in the companys operations, allowing them to track consumer behavior and preferences, further tailoring their offerings to the discerning tastes of their customers.
## Link to dataset
Singapore - Fendi - Product-level price list dataset
| [
"# Fendi web scraped data",
"## About the website\n\nThe luxury goods industry is in a dynamic state of expansion and growth in the Asia Pacific region. At the forefront of this trend is Singapore, a key player in this sector. The city-state is a hub for high-end fashion brands such as Fendi. Notably, Fendi in Singapore has widely adopted the use of technology, launching innovative digital campaigns and making excellent use of Ecommerce. Moreover, Product-List Page (PLP) data have played a significant role in the companys operations, allowing them to track consumer behavior and preferences, further tailoring their offerings to the discerning tastes of their customers.",
"## Link to dataset\n\nSingapore - Fendi - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Fendi #fashion #fashion product #image #fashion image #region-us \n",
"# Fendi web scraped data",
"## About the website\n\nThe luxury goods industry is in a dynamic state of expansion and growth in the Asia Pacific region. At the forefront of this trend is Singapore, a key player in this sector. The city-state is a hub for high-end fashion brands such as Fendi. Notably, Fendi in Singapore has widely adopted the use of technology, launching innovative digital campaigns and making excellent use of Ecommerce. Moreover, Product-List Page (PLP) data have played a significant role in the companys operations, allowing them to track consumer behavior and preferences, further tailoring their offerings to the discerning tastes of their customers.",
"## Link to dataset\n\nSingapore - Fendi - Product-level price list dataset"
]
| [
178,
7,
147,
17
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Fendi #fashion #fashion product #image #fashion image #region-us \n# Fendi web scraped data## About the website\n\nThe luxury goods industry is in a dynamic state of expansion and growth in the Asia Pacific region. At the forefront of this trend is Singapore, a key player in this sector. The city-state is a hub for high-end fashion brands such as Fendi. Notably, Fendi in Singapore has widely adopted the use of technology, launching innovative digital campaigns and making excellent use of Ecommerce. Moreover, Product-List Page (PLP) data have played a significant role in the companys operations, allowing them to track consumer behavior and preferences, further tailoring their offerings to the discerning tastes of their customers.## Link to dataset\n\nSingapore - Fendi - Product-level price list dataset"
]
|
42fd9d05aa4617d165a889b84eb88380ddd50a40 | # Farfetch web scraped data
## About the website
The **Ecommerce industry** in the EMEA region, specifically in Turkey, has shown significant growth in recent years. Companies like **Farfetch** have established their presence in the competitive Turkish market. Turkeys rapid digital transformation, favourable demographics, and high levels of internet penetration have resulted in the boom of online retail. This proves profitable for ecommerce platforms specialising in luxury fashion retail like Farfetch. The dataset observed contains **Ecommerce product-list page (PLP) data** on Farfetchs operations in Turkey, providing valuable insights into consumer trends, purchasing behaviour, and market dynamics in the Turkish ecommerce sector.
## Link to **dataset**
[Turkey - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20Turkey/r/reclVYdCN8kfPUbht)
| DBQ/Farfetch.Product.prices.Turkey | [
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
]
| 2023-11-19T08:50:04+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "source_datasets": ["original"], "task_categories": ["text-classification", "image-classification", "feature-extraction", "image-segmentation", "image-to-image", "image-to-text", "object-detection", "summarization", "zero-shot-image-classification"], "pretty_name": "Turkey - Farfetch - Product-level price list", "tags": ["webscraping", "ecommerce", "Farfetch", "fashion", "fashion product", "image", "fashion image"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "website_name", "dtype": "string"}, {"name": "competence_date", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "currency_code", "dtype": "string"}, {"name": "brand", "dtype": "string"}, {"name": "category1_code", "dtype": "string"}, {"name": "category2_code", "dtype": "string"}, {"name": "category3_code", "dtype": "string"}, {"name": "product_code", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "itemurl", "dtype": "string"}, {"name": "imageurl", "dtype": "string"}, {"name": "full_price", "dtype": "float64"}, {"name": "price", "dtype": "float64"}, {"name": "full_price_eur", "dtype": "float64"}, {"name": "price_eur", "dtype": "float64"}, {"name": "flg_discount", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 210951012, "num_examples": 563784}], "download_size": 74030067, "dataset_size": 210951012}} | 2023-11-19T08:50:57+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us
| # Farfetch web scraped data
## About the website
The Ecommerce industry in the EMEA region, specifically in Turkey, has shown significant growth in recent years. Companies like Farfetch have established their presence in the competitive Turkish market. Turkeys rapid digital transformation, favourable demographics, and high levels of internet penetration have resulted in the boom of online retail. This proves profitable for ecommerce platforms specialising in luxury fashion retail like Farfetch. The dataset observed contains Ecommerce product-list page (PLP) data on Farfetchs operations in Turkey, providing valuable insights into consumer trends, purchasing behaviour, and market dynamics in the Turkish ecommerce sector.
## Link to dataset
Turkey - Farfetch - Product-level price list dataset
| [
"# Farfetch web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, specifically in Turkey, has shown significant growth in recent years. Companies like Farfetch have established their presence in the competitive Turkish market. Turkeys rapid digital transformation, favourable demographics, and high levels of internet penetration have resulted in the boom of online retail. This proves profitable for ecommerce platforms specialising in luxury fashion retail like Farfetch. The dataset observed contains Ecommerce product-list page (PLP) data on Farfetchs operations in Turkey, providing valuable insights into consumer trends, purchasing behaviour, and market dynamics in the Turkish ecommerce sector.",
"## Link to dataset\n\nTurkey - Farfetch - Product-level price list dataset"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us \n",
"# Farfetch web scraped data",
"## About the website\n\nThe Ecommerce industry in the EMEA region, specifically in Turkey, has shown significant growth in recent years. Companies like Farfetch have established their presence in the competitive Turkish market. Turkeys rapid digital transformation, favourable demographics, and high levels of internet penetration have resulted in the boom of online retail. This proves profitable for ecommerce platforms specialising in luxury fashion retail like Farfetch. The dataset observed contains Ecommerce product-list page (PLP) data on Farfetchs operations in Turkey, providing valuable insights into consumer trends, purchasing behaviour, and market dynamics in the Turkish ecommerce sector.",
"## Link to dataset\n\nTurkey - Farfetch - Product-level price list dataset"
]
| [
179,
8,
150,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-image-classification #task_categories-feature-extraction #task_categories-image-segmentation #task_categories-image-to-image #task_categories-image-to-text #task_categories-object-detection #task_categories-summarization #task_categories-zero-shot-image-classification #annotations_creators-other #language_creators-other #multilinguality-monolingual #source_datasets-original #language-English #license-unknown #webscraping #ecommerce #Farfetch #fashion #fashion product #image #fashion image #region-us \n# Farfetch web scraped data## About the website\n\nThe Ecommerce industry in the EMEA region, specifically in Turkey, has shown significant growth in recent years. Companies like Farfetch have established their presence in the competitive Turkish market. Turkeys rapid digital transformation, favourable demographics, and high levels of internet penetration have resulted in the boom of online retail. This proves profitable for ecommerce platforms specialising in luxury fashion retail like Farfetch. The dataset observed contains Ecommerce product-list page (PLP) data on Farfetchs operations in Turkey, providing valuable insights into consumer trends, purchasing behaviour, and market dynamics in the Turkish ecommerce sector.## Link to dataset\n\nTurkey - Farfetch - Product-level price list dataset"
]
|
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