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10f9671835007ca2231fc07e12f4b171969c4500
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2
|
[
"region:us"
] |
2023-08-28T21:29:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2039406876, "dataset_size": 11949520000}}
|
2023-08-28T21:31:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2\"\n\nMore Information needed"
] |
[
6,
40
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd2\"\n\nMore Information needed"
] |
942e2b43c5bcf1055956dbfa00ea1ab357c9f428
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd3
|
[
"region:us"
] |
2023-08-28T21:38:56+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2051534214, "dataset_size": 11949520000}}
|
2023-08-28T21:41:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd3"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd3\"\n\nMore Information needed"
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6,
40
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
b857bcd984a096382399d1f6f9c5068d6a82fed4
|
# Dataset Card for Evaluation run of TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__Limarp-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T04:56:23.219077](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Limarp-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-22T04-56-23.219077.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": {
"em": 0.010067114093959731,
"em_stderr": 0.0010223392214785542,
"f1": 0.07404152684563742,
"f1_stderr": 0.0016679538706847923,
"acc": 0.41430390510138687,
"acc_stderr": 0.00921944153661685
},
"harness|drop|3": {
"em": 0.010067114093959731,
"em_stderr": 0.0010223392214785542,
"f1": 0.07404152684563742,
"f1_stderr": 0.0016679538706847923
},
"harness|gsm8k|5": {
"acc": 0.060652009097801364,
"acc_stderr": 0.006574733381405767
},
"harness|winogrande|5": {
"acc": 0.7679558011049724,
"acc_stderr": 0.011864149691827933
}
}
```
### 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_TFLai__Limarp-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:40:44+00:00
|
{"pretty_name": "Evaluation run of TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__Limarp-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T04:56:23.219077](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Limarp-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-22T04-56-23.219077.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 \"em\": 0.010067114093959731,\n \"em_stderr\": 0.0010223392214785542,\n \"f1\": 0.07404152684563742,\n \"f1_stderr\": 0.0016679538706847923,\n \"acc\": 0.41430390510138687,\n \"acc_stderr\": 0.00921944153661685\n },\n \"harness|drop|3\": {\n \"em\": 0.010067114093959731,\n \"em_stderr\": 0.0010223392214785542,\n \"f1\": 0.07404152684563742,\n \"f1_stderr\": 0.0016679538706847923\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.060652009097801364,\n \"acc_stderr\": 0.006574733381405767\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7679558011049724,\n \"acc_stderr\": 0.011864149691827933\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch", "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_08_28T22_39_43.026880", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:39:43.026880.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:39:43.026880.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T04_56_23.219077", "path": ["**/details_harness|drop|3_2023-10-22T04-56-23.219077.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T04-56-23.219077.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T04_56_23.219077", "path": ["**/details_harness|gsm8k|5_2023-10-22T04-56-23.219077.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T04-56-23.219077.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T22_39_43.026880", "path": ["**/details_harness|hellaswag|10_2023-08-28T22:39:43.026880.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-08-28T22:39:43.026880.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-28T22:39:43.026880.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_22T04_56_23.219077", "path": ["**/details_harness|winogrande|5_2023-10-22T04-56-23.219077.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-22T04-56-23.219077.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_28T22_39_43.026880", "path": ["results_2023-08-28T22:39:43.026880.parquet"]}, {"split": "2023_10_22T04_56_23.219077", "path": ["results_2023-10-22T04-56-23.219077.parquet"]}, {"split": "latest", "path": ["results_2023-10-22T04-56-23.219077.parquet"]}]}]}
|
2023-10-22T03:56:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T04:56:23.219077(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 TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T04:56:23.219077(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 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 TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T04:56:23.219077(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",
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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 TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T04:56:23.219077(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"
] |
a62e6a64ad99405d117ca4ee3bc4d04f506c184b
|
# Dataset Card for "scanobjectnn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jxie/scanobjectnn
|
[
"region:us"
] |
2023-08-28T21:43:01+00:00
|
{"dataset_info": {"features": [{"name": "inputs", "sequence": {"sequence": "float32"}}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "nobg_train", "num_bytes": 75689020, "num_examples": 2309}, {"name": "nobg_test", "num_bytes": 19045180, "num_examples": 581}, {"name": "bg_train", "num_bytes": 75689020, "num_examples": 2309}, {"name": "bg_test", "num_bytes": 19045180, "num_examples": 581}, {"name": "hardest_train", "num_bytes": 374216480, "num_examples": 11416}, {"name": "hardest_test", "num_bytes": 94471960, "num_examples": 2882}], "download_size": 493795631, "dataset_size": 658156840}}
|
2023-08-28T21:43:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "scanobjectnn"
More Information needed
|
[
"# Dataset Card for \"scanobjectnn\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"scanobjectnn\"\n\nMore Information needed"
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[
6,
14
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"scanobjectnn\"\n\nMore Information needed"
] |
86c70101a681dcf6b234b9ae777b88c3ab444093
|
# Dataset Card for Evaluation run of TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T04:16:45.714438](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-19T04-16-45.714438.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": {
"em": 0.345008389261745,
"em_stderr": 0.004868244118482663,
"f1": 0.4264691694630892,
"f1_stderr": 0.004672170372384348,
"acc": 0.3832876668064886,
"acc_stderr": 0.007708220968501149
},
"harness|drop|3": {
"em": 0.345008389261745,
"em_stderr": 0.004868244118482663,
"f1": 0.4264691694630892,
"f1_stderr": 0.004672170372384348
},
"harness|gsm8k|5": {
"acc": 0.014404852160727824,
"acc_stderr": 0.003282055917136951
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865348
}
}
```
### 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_TFLai__Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:45:43+00:00
|
{"pretty_name": "Evaluation run of TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T04:16:45.714438](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-19T04-16-45.714438.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 \"em\": 0.345008389261745,\n \"em_stderr\": 0.004868244118482663,\n \"f1\": 0.4264691694630892,\n \"f1_stderr\": 0.004672170372384348,\n \"acc\": 0.3832876668064886,\n \"acc_stderr\": 0.007708220968501149\n },\n \"harness|drop|3\": {\n \"em\": 0.345008389261745,\n \"em_stderr\": 0.004868244118482663,\n \"f1\": 0.4264691694630892,\n \"f1_stderr\": 0.004672170372384348\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.014404852160727824,\n \"acc_stderr\": 0.003282055917136951\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865348\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch", "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_08_28T22_44_43.350947", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:44:43.350947.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:44:43.350947.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_19T04_16_45.714438", "path": ["**/details_harness|drop|3_2023-10-19T04-16-45.714438.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T04-16-45.714438.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_19T04_16_45.714438", "path": ["**/details_harness|gsm8k|5_2023-10-19T04-16-45.714438.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-19T04-16-45.714438.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T22_44_43.350947", "path": ["**/details_harness|hellaswag|10_2023-08-28T22:44:43.350947.parquet"]}, {"split": "latest", "path": 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2023-10-19T03:16:58+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T04:16:45.714438(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 TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T04:16:45.714438(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 Card for Evaluation run of TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T04:16:45.714438(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 TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T04:16:45.714438(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"
] |
0ef7221c679aac3e19ecda4697768f87d7b3c68b
|
# Dataset Card for Evaluation run of TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__MythoMix-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-22T09:33:15.555085](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__MythoMix-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-22T09-33-15.555085.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": {
"em": 0.34186241610738255,
"em_stderr": 0.004857621548300327,
"f1": 0.41979970637584074,
"f1_stderr": 0.0046695180305142536,
"acc": 0.3822126733737321,
"acc_stderr": 0.007348726082467704
},
"harness|drop|3": {
"em": 0.34186241610738255,
"em_stderr": 0.004857621548300327,
"f1": 0.41979970637584074,
"f1_stderr": 0.0046695180305142536
},
"harness|gsm8k|5": {
"acc": 0.009097801364670205,
"acc_stderr": 0.002615326510775672
},
"harness|winogrande|5": {
"acc": 0.755327545382794,
"acc_stderr": 0.012082125654159738
}
}
```
### 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_TFLai__MythoMix-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:46:43+00:00
|
{"pretty_name": "Evaluation run of TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__MythoMix-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-22T09:33:15.555085](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__MythoMix-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-22T09-33-15.555085.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 \"em\": 0.34186241610738255,\n \"em_stderr\": 0.004857621548300327,\n \"f1\": 0.41979970637584074,\n \"f1_stderr\": 0.0046695180305142536,\n \"acc\": 0.3822126733737321,\n \"acc_stderr\": 0.007348726082467704\n },\n \"harness|drop|3\": {\n \"em\": 0.34186241610738255,\n \"em_stderr\": 0.004857621548300327,\n \"f1\": 0.41979970637584074,\n \"f1_stderr\": 0.0046695180305142536\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.009097801364670205,\n \"acc_stderr\": 0.002615326510775672\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.755327545382794,\n \"acc_stderr\": 0.012082125654159738\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch", "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_08_28T22_45_44.482040", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:45:44.482040.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:45:44.482040.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_22T09_33_15.555085", "path": ["**/details_harness|drop|3_2023-10-22T09-33-15.555085.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-22T09-33-15.555085.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_22T09_33_15.555085", "path": ["**/details_harness|gsm8k|5_2023-10-22T09-33-15.555085.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-22T09-33-15.555085.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T22_45_44.482040", "path": ["**/details_harness|hellaswag|10_2023-08-28T22:45:44.482040.parquet"]}, {"split": "latest", "path": 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|
2023-10-22T08:33:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T09:33:15.555085(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 TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T09:33:15.555085(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 Card for Evaluation run of TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T09:33:15.555085(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 TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-22T09:33:15.555085(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"
] |
86fc36130c29f3f564cc3f8157b5a8b469f120ee
|
# Dataset Card for Evaluation run of Danielbrdz/Barcenas-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Danielbrdz/Barcenas-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 [Danielbrdz/Barcenas-7b](https://huggingface.co/Danielbrdz/Barcenas-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 agregated 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_Danielbrdz__Barcenas-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T23:34:07.541919](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-7b/blob/main/results_2023-09-17T23-34-07.541919.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": {
"em": 0.004718959731543624,
"em_stderr": 0.0007018360183131257,
"f1": 0.0816715604026848,
"f1_stderr": 0.0017762083839348887,
"acc": 0.39889766050552516,
"acc_stderr": 0.009497938418122394
},
"harness|drop|3": {
"em": 0.004718959731543624,
"em_stderr": 0.0007018360183131257,
"f1": 0.0816715604026848,
"f1_stderr": 0.0017762083839348887
},
"harness|gsm8k|5": {
"acc": 0.06141015921152388,
"acc_stderr": 0.006613027536586322
},
"harness|winogrande|5": {
"acc": 0.7363851617995264,
"acc_stderr": 0.012382849299658464
}
}
```
### 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_Danielbrdz__Barcenas-7b
|
[
"region:us"
] |
2023-08-28T21:48:44+00:00
|
{"pretty_name": "Evaluation run of Danielbrdz/Barcenas-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [Danielbrdz/Barcenas-7b](https://huggingface.co/Danielbrdz/Barcenas-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 agregated 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_Danielbrdz__Barcenas-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T23:34:07.541919](https://huggingface.co/datasets/open-llm-leaderboard/details_Danielbrdz__Barcenas-7b/blob/main/results_2023-09-17T23-34-07.541919.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 \"em\": 0.004718959731543624,\n \"em_stderr\": 0.0007018360183131257,\n \"f1\": 0.0816715604026848,\n \"f1_stderr\": 0.0017762083839348887,\n \"acc\": 0.39889766050552516,\n \"acc_stderr\": 0.009497938418122394\n },\n \"harness|drop|3\": {\n \"em\": 0.004718959731543624,\n \"em_stderr\": 0.0007018360183131257,\n \"f1\": 0.0816715604026848,\n \"f1_stderr\": 0.0017762083839348887\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06141015921152388,\n \"acc_stderr\": 0.006613027536586322\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7363851617995264,\n \"acc_stderr\": 0.012382849299658464\n }\n}\n```", "repo_url": "https://huggingface.co/Danielbrdz/Barcenas-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_08_28T22_47_45.353935", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:47:45.353935.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:47:45.353935.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_17T23_34_07.541919", "path": ["**/details_harness|drop|3_2023-09-17T23-34-07.541919.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T23-34-07.541919.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T23_34_07.541919", "path": ["**/details_harness|gsm8k|5_2023-09-17T23-34-07.541919.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T23-34-07.541919.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T22_47_45.353935", "path": ["**/details_harness|hellaswag|10_2023-08-28T22:47:45.353935.parquet"]}, {"split": "latest", "path": 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2023-09-17T22:34:19+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of Danielbrdz/Barcenas-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 Danielbrdz/Barcenas-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 agregated 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 2023-09-17T23:34:07.541919(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 Danielbrdz/Barcenas-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 Danielbrdz/Barcenas-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 agregated 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 2023-09-17T23:34:07.541919(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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"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of Danielbrdz/Barcenas-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 Danielbrdz/Barcenas-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 agregated 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 2023-09-17T23:34:07.541919(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 Danielbrdz/Barcenas-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 Danielbrdz/Barcenas-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 agregated 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 2023-09-17T23:34:07.541919(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"
] |
27742d0c243df2fdaae8b218d4b39983e2feb13c
|
# Dataset Card for Evaluation run of TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__OpenOrca-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T14:11:37.243975](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__OpenOrca-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-19T14-11-37.243975.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": {
"em": 0.007969798657718121,
"em_stderr": 0.0009105960734168444,
"f1": 0.09576552013422834,
"f1_stderr": 0.001953364199146174,
"acc": 0.4345717050239562,
"acc_stderr": 0.01035518693998461
},
"harness|drop|3": {
"em": 0.007969798657718121,
"em_stderr": 0.0009105960734168444,
"f1": 0.09576552013422834,
"f1_stderr": 0.001953364199146174
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794433
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174785
}
}
```
### 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_TFLai__OpenOrca-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:51:30+00:00
|
{"pretty_name": "Evaluation run of TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__OpenOrca-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T14:11:37.243975](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__OpenOrca-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-19T14-11-37.243975.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 \"em\": 0.007969798657718121,\n \"em_stderr\": 0.0009105960734168444,\n \"f1\": 0.09576552013422834,\n \"f1_stderr\": 0.001953364199146174,\n \"acc\": 0.4345717050239562,\n \"acc_stderr\": 0.01035518693998461\n },\n \"harness|drop|3\": {\n \"em\": 0.007969798657718121,\n \"em_stderr\": 0.0009105960734168444,\n \"f1\": 0.09576552013422834,\n \"f1_stderr\": 0.001953364199146174\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \"acc_stderr\": 0.008668021353794433\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174785\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch", "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_08_28T22_50_32.447793", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:50:32.447793.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-28T22:50:32.447793.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_19T14_11_37.243975", "path": ["**/details_harness|drop|3_2023-10-19T14-11-37.243975.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T14-11-37.243975.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_19T14_11_37.243975", "path": ["**/details_harness|gsm8k|5_2023-10-19T14-11-37.243975.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-19T14-11-37.243975.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T22_50_32.447793", "path": ["**/details_harness|hellaswag|10_2023-08-28T22:50:32.447793.parquet"]}, {"split": "latest", "path": 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|
2023-10-19T13:11:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T14:11:37.243975(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 Card for Evaluation run of TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T14:11:37.243975(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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"### 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 TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T14:11:37.243975(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",
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"### Dataset Curators",
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"### Contributions"
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-19T14:11:37.243975(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"
] |
26412047118c40319bbd62071b8b9266ecaa2c8d
|
# Dataset Card for Evaluation run of TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__OrcaMini-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T23:54:59.357050](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__OrcaMini-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T23-54-59.357050.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": {
"em": 0.40216023489932884,
"em_stderr": 0.005021478569413354,
"f1": 0.47240666946308846,
"f1_stderr": 0.004780752235261512,
"acc": 0.39100918935382517,
"acc_stderr": 0.008061089924986945
},
"harness|drop|3": {
"em": 0.40216023489932884,
"em_stderr": 0.005021478569413354,
"f1": 0.47240666946308846,
"f1_stderr": 0.004780752235261512
},
"harness|gsm8k|5": {
"acc": 0.022744503411675512,
"acc_stderr": 0.004106620637749706
},
"harness|winogrande|5": {
"acc": 0.7592738752959748,
"acc_stderr": 0.012015559212224183
}
}
```
### 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_TFLai__OrcaMini-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:53:27+00:00
|
{"pretty_name": "Evaluation run of TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch) 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 agregated 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_TFLai__OrcaMini-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T23:54:59.357050](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__OrcaMini-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T23-54-59.357050.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 \"em\": 0.40216023489932884,\n \"em_stderr\": 0.005021478569413354,\n \"f1\": 0.47240666946308846,\n \"f1_stderr\": 0.004780752235261512,\n \"acc\": 0.39100918935382517,\n \"acc_stderr\": 0.008061089924986945\n },\n \"harness|drop|3\": {\n \"em\": 0.40216023489932884,\n \"em_stderr\": 0.005021478569413354,\n \"f1\": 0.47240666946308846,\n \"f1_stderr\": 0.004780752235261512\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.022744503411675512,\n \"acc_stderr\": 0.004106620637749706\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7592738752959748,\n \"acc_stderr\": 0.012015559212224183\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch", "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": 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TAGS
#region-us
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# Dataset Card for Evaluation run of TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-18T23:54:59.357050(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 TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-18T23:54:59.357050(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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"### 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 TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-18T23:54:59.357050(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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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch 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 agregated 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 2023-10-18T23:54:59.357050(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"
] |
28534032f75afc3a4a372b1f6ad410369dd536d5
|
# Dataset Card for "TinyStories_Mars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jason-lee08/TinyStories_Mars
|
[
"region:us"
] |
2023-08-28T21:55:14+00:00
|
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}, {"name": "stories", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5569066, "num_examples": 3364}], "download_size": 2292233, "dataset_size": 5569066}}
|
2023-08-28T21:55:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "TinyStories_Mars"
More Information needed
|
[
"# Dataset Card for \"TinyStories_Mars\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"TinyStories_Mars\"\n\nMore Information needed"
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6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"TinyStories_Mars\"\n\nMore Information needed"
] |
687ed9bf452d2478e4fe675354bc9e3620797bfa
|
# Dataset Card for Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T01:14:07.335372](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T01-14-07.335372.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": {
"em": 0.08420721476510067,
"em_stderr": 0.0028438907694585103,
"f1": 0.20487206375838948,
"f1_stderr": 0.0032246591490556827,
"acc": 0.35556432517758485,
"acc_stderr": 0.006369120635509223
},
"harness|drop|3": {
"em": 0.08420721476510067,
"em_stderr": 0.0028438907694585103,
"f1": 0.20487206375838948,
"f1_stderr": 0.0032246591490556827
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.7111286503551697,
"acc_stderr": 0.012738241271018446
}
}
```
### 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_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:57:04+00:00
|
{"pretty_name": "Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 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 agregated 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_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T01:14:07.335372](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-18T01-14-07.335372.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 \"em\": 0.08420721476510067,\n \"em_stderr\": 0.0028438907694585103,\n \"f1\": 0.20487206375838948,\n \"f1_stderr\": 0.0032246591490556827,\n \"acc\": 0.35556432517758485,\n \"acc_stderr\": 0.006369120635509223\n },\n \"harness|drop|3\": {\n \"em\": 0.08420721476510067,\n \"em_stderr\": 0.0028438907694585103,\n \"f1\": 0.20487206375838948,\n \"f1_stderr\": 0.0032246591490556827\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7111286503551697,\n \"acc_stderr\": 0.012738241271018446\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T01_14_07.335372", "path": ["**/details_harness|drop|3_2023-10-18T01-14-07.335372.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T01-14-07.335372.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T01_14_07.335372", "path": ["**/details_harness|gsm8k|5_2023-10-18T01-14-07.335372.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T01-14-07.335372.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T01_14_07.335372", "path": ["**/details_harness|winogrande|5_2023-10-18T01-14-07.335372.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T01-14-07.335372.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_28T22_56_04.264879", "path": ["results_2023-08-28T22:56:04.264879.parquet"]}, {"split": "2023_10_18T01_14_07.335372", "path": ["results_2023-10-18T01-14-07.335372.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T01-14-07.335372.parquet"]}]}]}
|
2023-10-18T00:14:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.
The dataset is composed of 3 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 agregated 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 2023-10-18T01:14:07.335372(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 TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-10-18T01:14:07.335372(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",
"### Data Splits",
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"### Curation Rationale",
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"#### Initial Data Collection and Normalization",
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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"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-10-18T01:14:07.335372(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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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-10-18T01:14:07.335372(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"
] |
5709b12c2ce465ac21564421fc95a6cb0d2d0175
|
# Dataset Card for Evaluation run of TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
- **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 [TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 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 agregated 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_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T17:45:47.583077](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-15T17-45-47.583077.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": {
"em": 0.4115981543624161,
"em_stderr": 0.005039800830998692,
"f1": 0.4835130033557058,
"f1_stderr": 0.004781616038785324,
"acc": 0.4006105292510111,
"acc_stderr": 0.008506118979991088
},
"harness|drop|3": {
"em": 0.4115981543624161,
"em_stderr": 0.005039800830998692,
"f1": 0.4835130033557058,
"f1_stderr": 0.004781616038785324
},
"harness|gsm8k|5": {
"acc": 0.0356330553449583,
"acc_stderr": 0.005106107853744191
},
"harness|winogrande|5": {
"acc": 0.7655880031570639,
"acc_stderr": 0.011906130106237985
}
}
```
### 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_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch
|
[
"region:us"
] |
2023-08-28T21:58:19+00:00
|
{"pretty_name": "Evaluation run of TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch](https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 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 agregated 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_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-15T17:45:47.583077](https://huggingface.co/datasets/open-llm-leaderboard/details_TFLai__Stable-Platypus2-13B-QLoRA-0.80-epoch/blob/main/results_2023-10-15T17-45-47.583077.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 \"em\": 0.4115981543624161,\n \"em_stderr\": 0.005039800830998692,\n \"f1\": 0.4835130033557058,\n \"f1_stderr\": 0.004781616038785324,\n \"acc\": 0.4006105292510111,\n \"acc_stderr\": 0.008506118979991088\n },\n \"harness|drop|3\": {\n \"em\": 0.4115981543624161,\n \"em_stderr\": 0.005039800830998692,\n \"f1\": 0.4835130033557058,\n \"f1_stderr\": 0.004781616038785324\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0356330553449583,\n \"acc_stderr\": 0.005106107853744191\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7655880031570639,\n \"acc_stderr\": 0.011906130106237985\n }\n}\n```", "repo_url": "https://huggingface.co/TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_15T17_45_47.583077", "path": ["**/details_harness|drop|3_2023-10-15T17-45-47.583077.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-15T17-45-47.583077.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_15T17_45_47.583077", "path": ["**/details_harness|gsm8k|5_2023-10-15T17-45-47.583077.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-15T17-45-47.583077.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_15T17_45_47.583077", "path": ["**/details_harness|winogrande|5_2023-10-15T17-45-47.583077.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-15T17-45-47.583077.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_28T22_57_17.888972", "path": ["results_2023-08-28T22:57:17.888972.parquet"]}, {"split": "2023_10_15T17_45_47.583077", "path": ["results_2023-10-15T17-45-47.583077.parquet"]}, {"split": "latest", "path": ["results_2023-10-15T17-45-47.583077.parquet"]}]}]}
|
2023-10-15T16:45:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.
The dataset is composed of 3 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 agregated 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 2023-10-15T17:45:47.583077(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 TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-10-15T17:45:47.583077(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 Card for Evaluation run of TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch",
"## 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 TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-10-15T17:45:47.583077(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 TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch## 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 TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 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 agregated 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 2023-10-15T17:45:47.583077(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"
] |
75e95cfe78af1eac5f8b1a8d08afe5f5886763e3
|
# Dataset Card for Evaluation run of Sao10K/Medusa-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Sao10K/Medusa-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 [Sao10K/Medusa-13b](https://huggingface.co/Sao10K/Medusa-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 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 agregated 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_Sao10K__Medusa-13b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T23:00:36.340269](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Medusa-13b/blob/main/results_2023-09-22T23-00-36.340269.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": {
"em": 0.08682885906040269,
"em_stderr": 0.0028836847948924805,
"f1": 0.20613359899328837,
"f1_stderr": 0.003265939806465616,
"acc": 0.4007308040520042,
"acc_stderr": 0.009687702523105881
},
"harness|drop|3": {
"em": 0.08682885906040269,
"em_stderr": 0.0028836847948924805,
"f1": 0.20613359899328837,
"f1_stderr": 0.003265939806465616
},
"harness|gsm8k|5": {
"acc": 0.06823351023502654,
"acc_stderr": 0.006945358944067429
},
"harness|winogrande|5": {
"acc": 0.7332280978689818,
"acc_stderr": 0.012430046102144333
}
}
```
### 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_Sao10K__Medusa-13b
|
[
"region:us"
] |
2023-08-28T22:12:54+00:00
|
{"pretty_name": "Evaluation run of Sao10K/Medusa-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [Sao10K/Medusa-13b](https://huggingface.co/Sao10K/Medusa-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 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 agregated 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_Sao10K__Medusa-13b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-22T23:00:36.340269](https://huggingface.co/datasets/open-llm-leaderboard/details_Sao10K__Medusa-13b/blob/main/results_2023-09-22T23-00-36.340269.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 \"em\": 0.08682885906040269,\n \"em_stderr\": 0.0028836847948924805,\n \"f1\": 0.20613359899328837,\n \"f1_stderr\": 0.003265939806465616,\n \"acc\": 0.4007308040520042,\n \"acc_stderr\": 0.009687702523105881\n },\n \"harness|drop|3\": {\n \"em\": 0.08682885906040269,\n \"em_stderr\": 0.0028836847948924805,\n \"f1\": 0.20613359899328837,\n \"f1_stderr\": 0.003265939806465616\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06823351023502654,\n \"acc_stderr\": 0.006945358944067429\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7332280978689818,\n \"acc_stderr\": 0.012430046102144333\n }\n}\n```", "repo_url": "https://huggingface.co/Sao10K/Medusa-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_08_28T23_11_54.790657", "path": ["**/details_harness|arc:challenge|25_2023-08-28T23:11:54.790657.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-28T23:11:54.790657.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_22T23_00_36.340269", "path": ["**/details_harness|drop|3_2023-09-22T23-00-36.340269.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-22T23-00-36.340269.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_22T23_00_36.340269", "path": ["**/details_harness|gsm8k|5_2023-09-22T23-00-36.340269.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-22T23-00-36.340269.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T23_11_54.790657", "path": ["**/details_harness|hellaswag|10_2023-08-28T23:11:54.790657.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-28T23:11:54.790657.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_28T23_11_54.790657", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-28T23:11:54.790657.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-28T23:11:54.790657.parquet", 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|
2023-09-22T22:00:48+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Sao10K/Medusa-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 Sao10K/Medusa-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 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 agregated 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 2023-09-22T23:00:36.340269(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 Sao10K/Medusa-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 Sao10K/Medusa-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 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 agregated 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 2023-09-22T23:00:36.340269(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 Sao10K/Medusa-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 Sao10K/Medusa-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 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 agregated 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 2023-09-22T23:00:36.340269(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 Sao10K/Medusa-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 Sao10K/Medusa-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 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 agregated 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 2023-09-22T23:00:36.340269(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"
] |
0a0b2391185febacec4d24996fa93c2525516060
|
# Dataset Card for Evaluation run of NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
- **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 [NobodyExistsOnTheInternet/PuffedConvo13bLoraE4](https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4) 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 agregated 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_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-16T05:37:42.901960](https://huggingface.co/datasets/open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4/blob/main/results_2023-10-16T05-37-42.901960.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": {
"em": 0.02506291946308725,
"em_stderr": 0.0016008246934367607,
"f1": 0.08258703859060393,
"f1_stderr": 0.0020041497370696586,
"acc": 0.4200579473220307,
"acc_stderr": 0.009967774108676528
},
"harness|drop|3": {
"em": 0.02506291946308725,
"em_stderr": 0.0016008246934367607,
"f1": 0.08258703859060393,
"f1_stderr": 0.0020041497370696586
},
"harness|gsm8k|5": {
"acc": 0.08794541319181198,
"acc_stderr": 0.007801162197487711
},
"harness|winogrande|5": {
"acc": 0.7521704814522494,
"acc_stderr": 0.012134386019865348
}
}
```
### 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_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4
|
[
"region:us"
] |
2023-08-28T22:16:40+00:00
|
{"pretty_name": "Evaluation run of NobodyExistsOnTheInternet/PuffedConvo13bLoraE4", "dataset_summary": "Dataset automatically created during the evaluation run of model [NobodyExistsOnTheInternet/PuffedConvo13bLoraE4](https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4) 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 agregated 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_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-16T05:37:42.901960](https://huggingface.co/datasets/open-llm-leaderboard/details_NobodyExistsOnTheInternet__PuffedConvo13bLoraE4/blob/main/results_2023-10-16T05-37-42.901960.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 \"em\": 0.02506291946308725,\n \"em_stderr\": 0.0016008246934367607,\n \"f1\": 0.08258703859060393,\n \"f1_stderr\": 0.0020041497370696586,\n \"acc\": 0.4200579473220307,\n \"acc_stderr\": 0.009967774108676528\n },\n \"harness|drop|3\": {\n \"em\": 0.02506291946308725,\n \"em_stderr\": 0.0016008246934367607,\n \"f1\": 0.08258703859060393,\n \"f1_stderr\": 0.0020041497370696586\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08794541319181198,\n \"acc_stderr\": 0.007801162197487711\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7521704814522494,\n \"acc_stderr\": 0.012134386019865348\n }\n}\n```", "repo_url": "https://huggingface.co/NobodyExistsOnTheInternet/PuffedConvo13bLoraE4", "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_08_28T23_15_40.572782", "path": ["**/details_harness|arc:challenge|25_2023-08-28T23:15:40.572782.parquet"]}, {"split": "2023_09_13T00_01_07.493301", "path": ["**/details_harness|arc:challenge|25_2023-09-13T00-01-07.493301.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-09-13T00-01-07.493301.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_16T05_37_42.901960", "path": ["**/details_harness|drop|3_2023-10-16T05-37-42.901960.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-16T05-37-42.901960.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_16T05_37_42.901960", "path": ["**/details_harness|gsm8k|5_2023-10-16T05-37-42.901960.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-16T05-37-42.901960.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_28T23_15_40.572782", "path": 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2023-10-16T04:37:59+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of NobodyExistsOnTheInternet/PuffedConvo13bLoraE4
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model NobodyExistsOnTheInternet/PuffedConvo13bLoraE4 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 agregated 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 2023-10-16T05:37:42.901960(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 NobodyExistsOnTheInternet/PuffedConvo13bLoraE4",
"## 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 NobodyExistsOnTheInternet/PuffedConvo13bLoraE4 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 agregated 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 2023-10-16T05:37:42.901960(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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"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Contributions"
] |
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model NobodyExistsOnTheInternet/PuffedConvo13bLoraE4 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 agregated 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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] |
126bad616f46f830a2235b1ea8f9a62ab4450d3a
|
# Dataset Card for "Finetuned-text-to-sql"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Mohanrajv27/Finetuned-text-to-sql
|
[
"region:us"
] |
2023-08-28T22:17:10+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 215198580.9182748, "num_examples": 235987}, {"name": "test", "num_bytes": 23911156.081725195, "num_examples": 26221}], "download_size": 85588612, "dataset_size": 239109737.0}}
|
2023-08-28T22:19:30+00:00
|
[] |
[] |
TAGS
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|
# Dataset Card for "Finetuned-text-to-sql"
More Information needed
|
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6b29ae5b40ad68f365b2e113e9e16f76e56cc7bb
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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]
|
jryan-pol/flags
|
[
"region:us"
] |
2023-08-28T22:20:36+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]}
|
2024-01-21T22:53:31+00:00
|
[] |
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|
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5b4d947c6a2ae52fe482649cdcf3e74042c3b93f
|
# Dataset Card for "prepare_dataset_train_batch3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
spsither/prepare_dataset_train_batch3
|
[
"region:us"
] |
2023-08-28T23:05:57+00:00
|
{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 95836096584, "num_examples": 99761}], "download_size": 20673138275, "dataset_size": 95836096584}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T02:00:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prepare_dataset_train_batch3"
More Information needed
|
[
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] |
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"passage: TAGS\n#region-us \n# Dataset Card for \"prepare_dataset_train_batch3\"\n\nMore Information needed"
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473e0d1feca893111a582c802f742694db96d82e
|
# Dataset Card for Evaluation run of xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1
- **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 [xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1](https://huggingface.co/xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_xxyyy123__test_merge_p_ov1_w0.66_w0.5_n1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T00:08:25.370219](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__test_merge_p_ov1_w0.66_w0.5_n1/blob/main/results_2023-08-29T00%3A08%3A25.370219.json):
```python
{
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"mc1_stderr": 0.017160273901693657,
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"mc2_stderr": 0.015820333707933832
},
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},
"harness|hendrycksTest-international_law|5": {
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"acc_norm_stderr": 0.04065578140908706
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"harness|hendrycksTest-logical_fallacies|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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"harness|hendrycksTest-moral_disputes|5": {
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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": {
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"acc_norm": 0.6591639871382636,
"acc_norm_stderr": 0.026920841260776165
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"harness|hendrycksTest-prehistory|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_law|5": {
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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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"acc_norm": 0.6272727272727273,
"acc_norm_stderr": 0.04631381319425465
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.02899690969332891
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"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.76,
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"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.038899512528272166
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"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.7894736842105263,
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"harness|truthfulqa:mc|0": {
"mc1": 0.401468788249694,
"mc1_stderr": 0.017160273901693657,
"mc2": 0.5617854888760936,
"mc2_stderr": 0.015820333707933832
}
}
```
### 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_xxyyy123__test_merge_p_ov1_w0.66_w0.5_n1
|
[
"region:us"
] |
2023-08-28T23:09:02+00:00
|
{"pretty_name": "Evaluation run of xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1", "dataset_summary": "Dataset automatically created during the evaluation run of model [xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1](https://huggingface.co/xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 agregated 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_xxyyy123__test_merge_p_ov1_w0.66_w0.5_n1\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T00:08:25.370219](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__test_merge_p_ov1_w0.66_w0.5_n1/blob/main/results_2023-08-29T00%3A08%3A25.370219.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5815765512888643,\n \"acc_stderr\": 0.034085704613422384,\n \"acc_norm\": 0.5853409830541163,\n \"acc_norm_stderr\": 0.03406560528917499,\n \"mc1\": 0.401468788249694,\n \"mc1_stderr\": 0.017160273901693657,\n \"mc2\": 0.5617854888760936,\n \"mc2_stderr\": 0.015820333707933832\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6015358361774744,\n \"acc_stderr\": 0.014306946052735567,\n \"acc_norm\": 0.6245733788395904,\n \"acc_norm_stderr\": 0.014150631435111728\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6246763592909779,\n \"acc_stderr\": 0.0048321678545016405,\n \"acc_norm\": 0.8237402907787293,\n \"acc_norm_stderr\": 0.003802622341529012\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\": 0.5111111111111111,\n \"acc_stderr\": 0.04318275491977976,\n \"acc_norm\": 0.5111111111111111,\n \"acc_norm_stderr\": 0.04318275491977976\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.5526315789473685,\n \"acc_stderr\": 0.0404633688397825,\n \"acc_norm\": 0.5526315789473685,\n \"acc_norm_stderr\": 0.0404633688397825\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\": 0.03005258057955784,\n \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955784\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6597222222222222,\n \"acc_stderr\": 0.039621355734862175,\n \"acc_norm\": 0.6597222222222222,\n \"acc_norm_stderr\": 0.039621355734862175\n },\n \"harness|hendrycksTest-college_chemistry|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_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\": 0.31,\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\": 0.5722543352601156,\n \"acc_stderr\": 0.03772446857518025,\n \"acc_norm\": 0.5722543352601156,\n \"acc_norm_stderr\": 0.03772446857518025\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3431372549019608,\n \"acc_stderr\": 0.047240073523838876,\n \"acc_norm\": 0.3431372549019608,\n \"acc_norm_stderr\": 0.047240073523838876\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.047258156262526094,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.047258156262526094\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5106382978723404,\n \"acc_stderr\": 0.03267862331014063,\n \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.03267862331014063\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3157894736842105,\n \"acc_stderr\": 0.043727482902780064,\n \"acc_norm\": 0.3157894736842105,\n \"acc_norm_stderr\": 0.043727482902780064\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3439153439153439,\n \"acc_stderr\": 0.024464426625596433,\n \"acc_norm\": 0.3439153439153439,\n \"acc_norm_stderr\": 0.024464426625596433\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\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7,\n \"acc_stderr\": 0.02606936229533513,\n \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.02606936229533513\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.41379310344827586,\n \"acc_stderr\": 0.03465304488406796,\n \"acc_norm\": 0.41379310344827586,\n \"acc_norm_stderr\": 0.03465304488406796\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7626262626262627,\n \"acc_stderr\": 0.0303137105381989,\n \"acc_norm\": 0.7626262626262627,\n \"acc_norm_stderr\": 0.0303137105381989\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.02469721693087894,\n \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.02469721693087894\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.027940457136228412,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.027940457136228412\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.5840336134453782,\n \"acc_stderr\": 0.032016501007396114,\n \"acc_norm\": 0.5840336134453782,\n \"acc_norm_stderr\": 0.032016501007396114\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.33774834437086093,\n \"acc_stderr\": 0.03861557546255169,\n \"acc_norm\": 0.33774834437086093,\n \"acc_norm_stderr\": 0.03861557546255169\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7853211009174312,\n \"acc_stderr\": 0.01760430414925648,\n \"acc_norm\": 0.7853211009174312,\n \"acc_norm_stderr\": 0.01760430414925648\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4212962962962963,\n \"acc_stderr\": 0.03367462138896078,\n \"acc_norm\": 0.4212962962962963,\n \"acc_norm_stderr\": 0.03367462138896078\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640766,\n \"acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640766\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6995515695067265,\n \"acc_stderr\": 0.030769352008229146,\n \"acc_norm\": 0.6995515695067265,\n \"acc_norm_stderr\": 0.030769352008229146\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.04118438565806297,\n \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.04118438565806297\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7272727272727273,\n \"acc_stderr\": 0.04065578140908706,\n \"acc_norm\": 0.7272727272727273,\n \"acc_norm_stderr\": 0.04065578140908706\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.6503067484662577,\n \"acc_stderr\": 0.03746668325470021,\n \"acc_norm\": 0.6503067484662577,\n \"acc_norm_stderr\": 0.03746668325470021\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.36607142857142855,\n \"acc_stderr\": 0.0457237235873743,\n \"acc_norm\": 0.36607142857142855,\n \"acc_norm_stderr\": 0.0457237235873743\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\": 0.8504273504273504,\n \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.7713920817369093,\n \"acc_stderr\": 0.015016884698539873,\n \"acc_norm\": 0.7713920817369093,\n \"acc_norm_stderr\": 0.015016884698539873\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6271676300578035,\n \"acc_stderr\": 0.02603389061357628,\n \"acc_norm\": 0.6271676300578035,\n \"acc_norm_stderr\": 0.02603389061357628\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n \"acc_stderr\": 0.0162690886639594,\n \"acc_norm\": 0.3843575418994413,\n \"acc_norm_stderr\": 0.0162690886639594\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6143790849673203,\n \"acc_stderr\": 0.02787074527829028,\n \"acc_norm\": 0.6143790849673203,\n \"acc_norm_stderr\": 0.02787074527829028\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6591639871382636,\n \"acc_stderr\": 0.026920841260776165,\n \"acc_norm\": 0.6591639871382636,\n \"acc_norm_stderr\": 0.026920841260776165\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6975308641975309,\n \"acc_stderr\": 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TAGS
#region-us
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# Dataset Card for Evaluation run of xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1 on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T00:08:25.370219:
### 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 xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1",
"## 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 xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T00:08:25.370219:",
"### 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 xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1",
"## 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 xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T00:08:25.370219:",
"### 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 xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1## 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 xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T00:08:25.370219:### 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"
] |
49942d43556b6eee191584d4cc36faa8e1c6f9e3
|
# Dataset Card for Evaluation run of Writer/palmyra-large
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Writer/palmyra-large
- **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 [Writer/palmyra-large](https://huggingface.co/Writer/palmyra-large) 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 agregated 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_Writer__palmyra-large",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-23T21:24:08.215151](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-large/blob/main/results_2023-10-23T21-24-08.215151.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": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857078,
"f1": 0.05021182885906047,
"f1_stderr": 0.0012269220327497075,
"acc": 0.3564427500923004,
"acc_stderr": 0.0090619059626658
},
"harness|drop|3": {
"em": 0.001153523489932886,
"em_stderr": 0.0003476179896857078,
"f1": 0.05021182885906047,
"f1_stderr": 0.0012269220327497075
},
"harness|gsm8k|5": {
"acc": 0.03411675511751327,
"acc_stderr": 0.005000212600773288
},
"harness|winogrande|5": {
"acc": 0.6787687450670876,
"acc_stderr": 0.01312359932455831
}
}
```
### 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_Writer__palmyra-large
|
[
"region:us"
] |
2023-08-28T23:24:14+00:00
|
{"pretty_name": "Evaluation run of Writer/palmyra-large", "dataset_summary": "Dataset automatically created during the evaluation run of model [Writer/palmyra-large](https://huggingface.co/Writer/palmyra-large) 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 agregated 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_Writer__palmyra-large\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-23T21:24:08.215151](https://huggingface.co/datasets/open-llm-leaderboard/details_Writer__palmyra-large/blob/main/results_2023-10-23T21-24-08.215151.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 \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857078,\n \"f1\": 0.05021182885906047,\n \"f1_stderr\": 0.0012269220327497075,\n \"acc\": 0.3564427500923004,\n \"acc_stderr\": 0.0090619059626658\n },\n \"harness|drop|3\": {\n \"em\": 0.001153523489932886,\n \"em_stderr\": 0.0003476179896857078,\n \"f1\": 0.05021182885906047,\n \"f1_stderr\": 0.0012269220327497075\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.03411675511751327,\n \"acc_stderr\": 0.005000212600773288\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6787687450670876,\n \"acc_stderr\": 0.01312359932455831\n }\n}\n```", "repo_url": "https://huggingface.co/Writer/palmyra-large", "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_08_29T00_23_42.233683", 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|
2023-10-23T20:24:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Writer/palmyra-large
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Writer/palmyra-large 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 agregated 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 2023-10-23T21:24:08.215151(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 Card for Evaluation run of Writer/palmyra-large",
"## 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 Writer/palmyra-large 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 agregated 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 2023-10-23T21:24:08.215151(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 Card for Evaluation run of Writer/palmyra-large",
"## 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 Writer/palmyra-large 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 agregated 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 2023-10-23T21:24:08.215151(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 Writer/palmyra-large## 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 Writer/palmyra-large 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 agregated 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 2023-10-23T21:24:08.215151(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"
] |
f213d687e49814e5105fa45633d4ad1e28499533
|
# Dataset Card for Evaluation run of radm/Philosophy-Platypus2-13b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/radm/Philosophy-Platypus2-13b
- **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 [radm/Philosophy-Platypus2-13b](https://huggingface.co/radm/Philosophy-Platypus2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_radm__Philosophy-Platypus2-13b",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T00:45:24.163346](https://huggingface.co/datasets/open-llm-leaderboard/details_radm__Philosophy-Platypus2-13b/blob/main/results_2023-08-29T00%3A45%3A24.163346.json):
```python
{
"all": {
"acc": 0.5437981691869808,
"acc_stderr": 0.03484311795554624,
"acc_norm": 0.547878610439407,
"acc_norm_stderr": 0.034826606717822575,
"mc1": 0.24357405140758873,
"mc1_stderr": 0.015026354824910782,
"mc2": 0.37335488461829447,
"mc2_stderr": 0.014112790281285795
},
"harness|arc:challenge|25": {
"acc": 0.5477815699658704,
"acc_stderr": 0.014544519880633822,
"acc_norm": 0.5861774744027304,
"acc_norm_stderr": 0.014392730009221004
},
"harness|hellaswag|10": {
"acc": 0.5828520215096594,
"acc_stderr": 0.004920800313232742,
"acc_norm": 0.785202150965943,
"acc_norm_stderr": 0.0040984271589492634
},
"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": 0.4222222222222222,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.4222222222222222,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5986842105263158,
"acc_stderr": 0.039889037033362836,
"acc_norm": 0.5986842105263158,
"acc_norm_stderr": 0.039889037033362836
},
"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": 0.5924528301886792,
"acc_stderr": 0.030242233800854494,
"acc_norm": 0.5924528301886792,
"acc_norm_stderr": 0.030242233800854494
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6388888888888888,
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"acc_norm": 0.6388888888888888,
"acc_norm_stderr": 0.04016660030451233
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.47398843930635837,
"acc_stderr": 0.03807301726504511,
"acc_norm": 0.47398843930635837,
"acc_norm_stderr": 0.03807301726504511
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105653,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105653
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.65,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.65,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm": 0.4340425531914894,
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"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.042663394431593935
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"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm": 0.46206896551724136,
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm_stderr": 0.024942368931159795
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"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.30952380952380953,
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"harness|hendrycksTest-global_facts|5": {
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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_european_history|5": {
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"acc_norm": 0.6787878787878788,
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},
"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|truthfulqa:mc|0": {
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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_radm__Philosophy-Platypus2-13b
|
[
"region:us"
] |
2023-08-28T23:46:27+00:00
|
{"pretty_name": "Evaluation run of radm/Philosophy-Platypus2-13b", "dataset_summary": "Dataset automatically created during the evaluation run of model [radm/Philosophy-Platypus2-13b](https://huggingface.co/radm/Philosophy-Platypus2-13b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 agregated 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_radm__Philosophy-Platypus2-13b\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T00:45:24.163346](https://huggingface.co/datasets/open-llm-leaderboard/details_radm__Philosophy-Platypus2-13b/blob/main/results_2023-08-29T00%3A45%3A24.163346.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5437981691869808,\n 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\"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.47398843930635837,\n \"acc_stderr\": 0.03807301726504511,\n \"acc_norm\": 0.47398843930635837,\n \"acc_norm_stderr\": 0.03807301726504511\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.65,\n \"acc_stderr\": 0.0479372485441102,\n \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4340425531914894,\n \"acc_stderr\": 0.03240038086792747,\n \"acc_norm\": 0.4340425531914894,\n \"acc_norm_stderr\": 0.03240038086792747\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.042663394431593935,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.042663394431593935\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.46206896551724136,\n \"acc_stderr\": 0.04154659671707548,\n \"acc_norm\": 0.46206896551724136,\n \"acc_norm_stderr\": 0.04154659671707548\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.37566137566137564,\n \"acc_stderr\": 0.024942368931159795,\n \"acc_norm\": 0.37566137566137564,\n \"acc_norm_stderr\": 0.024942368931159795\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n \"acc_norm_stderr\": 0.04134913018303316\n },\n 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TAGS
#region-us
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# Dataset Card for Evaluation run of radm/Philosophy-Platypus2-13b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model radm/Philosophy-Platypus2-13b on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T00:45:24.163346:
### 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 radm/Philosophy-Platypus2-13b",
"## 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 radm/Philosophy-Platypus2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T00:45:24.163346:",
"### 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 radm/Philosophy-Platypus2-13b",
"## 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 radm/Philosophy-Platypus2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T00:45:24.163346:",
"### 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"
] |
[
6,
21,
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169,
22,
10,
4,
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5,
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10,
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9,
8,
8,
7,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of radm/Philosophy-Platypus2-13b## 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 radm/Philosophy-Platypus2-13b on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T00:45:24.163346:### 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"
] |
7c8f6014d28af11ca9f27de62ecac17540b36556
|
# Dataset Card for "articulationGAN_finetuning_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
thomaslu/articulationGAN_finetuning_data
|
[
"region:us"
] |
2023-08-29T00:12:07+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 9100963.0, "num_examples": 111}, {"name": "train", "num_bytes": 32796309.0, "num_examples": 400}], "download_size": 41933143, "dataset_size": 41897272.0}}
|
2023-08-29T00:12:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "articulationGAN_finetuning_data"
More Information needed
|
[
"# Dataset Card for \"articulationGAN_finetuning_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"articulationGAN_finetuning_data\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"articulationGAN_finetuning_data\"\n\nMore Information needed"
] |
1e2b4b6e8db21cb7168a5fb2f69a4629ccc1bece
|
# Dataset Card for "prepare_dataset_train_batch2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
spsither/prepare_dataset_train_batch2
|
[
"region:us"
] |
2023-08-29T00:17:30+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 95846087800, "num_examples": 99760}], "download_size": 5077027527, "dataset_size": 95846087800}}
|
2023-08-29T05:23:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prepare_dataset_train_batch2"
More Information needed
|
[
"# Dataset Card for \"prepare_dataset_train_batch2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prepare_dataset_train_batch2\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prepare_dataset_train_batch2\"\n\nMore Information needed"
] |
1bad27c6adcc207d1d6de7b9bbdc03a10154f3b1
|
# Dataset Card for "essay_grade_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whateverweird17/essay_grade_v2
|
[
"region:us"
] |
2023-08-29T00:20:47+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2211156, "num_examples": 1427}, {"name": "validation", "num_bytes": 221580.45409950946, "num_examples": 143}], "download_size": 1200558, "dataset_size": 2432736.4540995094}}
|
2023-08-29T00:20:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "essay_grade_v2"
More Information needed
|
[
"# Dataset Card for \"essay_grade_v2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"essay_grade_v2\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"essay_grade_v2\"\n\nMore Information needed"
] |
d624dbc327763f34c8c3ed6099e7e20aaadccea3
|
# Dataset Card for "autotree_automl_MagicTelescope_gosdt_l512_d3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_MagicTelescope_gosdt_l512_d3
|
[
"region:us"
] |
2023-08-29T00:33:30+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6767200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 676720000, "num_examples": 10000}], "download_size": 2607095699, "dataset_size": 7443920000}}
|
2023-08-29T00:35:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_MagicTelescope_gosdt_l512_d3"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3\"\n\nMore Information needed"
] |
[
6,
30
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3\"\n\nMore Information needed"
] |
3983684381ccf1904f7a4e0a1b801a05a5443a13
|
A dataset consisting of questions, answers, and cryptocurrency descriptions
|
mkly/crypto-sales-question-answers
|
[
"task_categories:question-answering",
"multilinguality:monolingual",
"source_datasets:web_questions",
"language:en",
"cryptocurrency",
"adapter-transformers",
"region:us"
] |
2023-08-29T00:43:53+00:00
|
{"language": ["en"], "multilinguality": ["monolingual"], "source_datasets": ["web_questions"], "task_categories": ["question-answering"], "pretty_name": "crypto_sales", "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "cryptocurrency", "dtype": "string"}]}, "tags": ["cryptocurrency", "adapter-transformers"]}
|
2023-08-29T01:54:19+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-question-answering #multilinguality-monolingual #source_datasets-web_questions #language-English #cryptocurrency #adapter-transformers #region-us
|
A dataset consisting of questions, answers, and cryptocurrency descriptions
|
[] |
[
"TAGS\n#task_categories-question-answering #multilinguality-monolingual #source_datasets-web_questions #language-English #cryptocurrency #adapter-transformers #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#task_categories-question-answering #multilinguality-monolingual #source_datasets-web_questions #language-English #cryptocurrency #adapter-transformers #region-us \n"
] |
10d58304263052317fc80c25161a1a80c7c6f1e5
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd1
|
[
"region:us"
] |
2023-08-29T00:44:51+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2039303021, "dataset_size": 11949520000}}
|
2023-08-29T00:47:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd1"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
[
6,
40
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_default-of-credit-card-clients_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
d62cdd6bebb92965a7201a2fa7bff1e3b148705d
|
# Dataset Card for "flare-es-fns"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChanceFocus/flare-es-fns
|
[
"region:us"
] |
2023-08-29T01:14:35+00:00
|
{"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 20134903, "num_examples": 50}], "download_size": 9992059, "dataset_size": 20134903}}
|
2023-12-15T08:56:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flare-es-fns"
More Information needed
|
[
"# Dataset Card for \"flare-es-fns\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flare-es-fns\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flare-es-fns\"\n\nMore Information needed"
] |
c3c8504280cbdef0f00bb8119f16f034bea2bd9f
|
# HumTrans Dataset
- Dataset Name: HumTrans
- Dataset Type: Humming audio in .wav format and corresponding label MIDI file
- Primary Use: Humming melody transcription and as a foundation for downstream tasks such as humming melody based music generation
- Summary: 500 musical compositions of different genres and languages, 1000 music segments in total; sampled at a frequency of 44,100 Hz; approximately 56.22 hours of audio; 14,614 files in total.
- File Description: all_wav.zip includes all the humming audios in .wav format, all_midi.zip includes all the corresponding label MIDIs in .mid format. Both of these two share the same naming convention, which is personID_musicID_segmentID_repetitionID or personID_musicID_segmentID_repetitionID_[U/D/DD/DDD]. For example, F01_0005_0001_1, or F04_0055_0001_2_DD. train_valid_test_keys.json contains the official split of this dataset, including train, valid and test.
|
dadinghh2/HumTrans
|
[
"license:cc-by-nc-4.0",
"region:us"
] |
2023-08-29T01:14:37+00:00
|
{"license": "cc-by-nc-4.0"}
|
2023-09-26T05:26:09+00:00
|
[] |
[] |
TAGS
#license-cc-by-nc-4.0 #region-us
|
# HumTrans Dataset
- Dataset Name: HumTrans
- Dataset Type: Humming audio in .wav format and corresponding label MIDI file
- Primary Use: Humming melody transcription and as a foundation for downstream tasks such as humming melody based music generation
- Summary: 500 musical compositions of different genres and languages, 1000 music segments in total; sampled at a frequency of 44,100 Hz; approximately 56.22 hours of audio; 14,614 files in total.
- File Description: all_wav.zip includes all the humming audios in .wav format, all_midi.zip includes all the corresponding label MIDIs in .mid format. Both of these two share the same naming convention, which is personID_musicID_segmentID_repetitionID or personID_musicID_segmentID_repetitionID_[U/D/DD/DDD]. For example, F01_0005_0001_1, or F04_0055_0001_2_DD. train_valid_test_keys.json contains the official split of this dataset, including train, valid and test.
|
[
"# HumTrans Dataset\n\n- Dataset Name: HumTrans\n- Dataset Type: Humming audio in .wav format and corresponding label MIDI file\n- Primary Use: Humming melody transcription and as a foundation for downstream tasks such as humming melody based music generation\n- Summary: 500 musical compositions of different genres and languages, 1000 music segments in total; sampled at a frequency of 44,100 Hz; approximately 56.22 hours of audio; 14,614 files in total.\n- File Description: all_wav.zip includes all the humming audios in .wav format, all_midi.zip includes all the corresponding label MIDIs in .mid format. Both of these two share the same naming convention, which is personID_musicID_segmentID_repetitionID or personID_musicID_segmentID_repetitionID_[U/D/DD/DDD]. For example, F01_0005_0001_1, or F04_0055_0001_2_DD. train_valid_test_keys.json contains the official split of this dataset, including train, valid and test."
] |
[
"TAGS\n#license-cc-by-nc-4.0 #region-us \n",
"# HumTrans Dataset\n\n- Dataset Name: HumTrans\n- Dataset Type: Humming audio in .wav format and corresponding label MIDI file\n- Primary Use: Humming melody transcription and as a foundation for downstream tasks such as humming melody based music generation\n- Summary: 500 musical compositions of different genres and languages, 1000 music segments in total; sampled at a frequency of 44,100 Hz; approximately 56.22 hours of audio; 14,614 files in total.\n- File Description: all_wav.zip includes all the humming audios in .wav format, all_midi.zip includes all the corresponding label MIDIs in .mid format. Both of these two share the same naming convention, which is personID_musicID_segmentID_repetitionID or personID_musicID_segmentID_repetitionID_[U/D/DD/DDD]. For example, F01_0005_0001_1, or F04_0055_0001_2_DD. train_valid_test_keys.json contains the official split of this dataset, including train, valid and test."
] |
[
17,
263
] |
[
"passage: TAGS\n#license-cc-by-nc-4.0 #region-us \n# HumTrans Dataset\n\n- Dataset Name: HumTrans\n- Dataset Type: Humming audio in .wav format and corresponding label MIDI file\n- Primary Use: Humming melody transcription and as a foundation for downstream tasks such as humming melody based music generation\n- Summary: 500 musical compositions of different genres and languages, 1000 music segments in total; sampled at a frequency of 44,100 Hz; approximately 56.22 hours of audio; 14,614 files in total.\n- File Description: all_wav.zip includes all the humming audios in .wav format, all_midi.zip includes all the corresponding label MIDIs in .mid format. Both of these two share the same naming convention, which is personID_musicID_segmentID_repetitionID or personID_musicID_segmentID_repetitionID_[U/D/DD/DDD]. For example, F01_0005_0001_1, or F04_0055_0001_2_DD. train_valid_test_keys.json contains the official split of this dataset, including train, valid and test."
] |
db00d7c6dc4d9ea0c715e664a3f2aa712f437214
|
# Dataset Card for "4ee2d819"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/4ee2d819
|
[
"region:us"
] |
2023-08-29T01:19:17+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1329, "dataset_size": 178}}
|
2023-08-29T01:19:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "4ee2d819"
More Information needed
|
[
"# Dataset Card for \"4ee2d819\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"4ee2d819\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"4ee2d819\"\n\nMore Information needed"
] |
47d8dbdde6e7abf632fc95844d0a1505fe0c4dc4
|
# Dataset Card for "275949af"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/275949af
|
[
"region:us"
] |
2023-08-29T01:19:20+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1329, "dataset_size": 178}}
|
2023-08-29T01:19:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "275949af"
More Information needed
|
[
"# Dataset Card for \"275949af\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"275949af\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"275949af\"\n\nMore Information needed"
] |
a86b96446000f5f071c47dd6ec1b6abbec4729f5
|
# Dataset Card for "flare-es-multifin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChanceFocus/flare-es-multifin
|
[
"region:us"
] |
2023-08-29T01:20:15+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 117062, "num_examples": 230}], "download_size": 28388, "dataset_size": 117062}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]}
|
2023-12-14T02:33:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flare-es-multifin"
More Information needed
|
[
"# Dataset Card for \"flare-es-multifin\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flare-es-multifin\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flare-es-multifin\"\n\nMore Information needed"
] |
7b75238d30c36b77b5ec2c6cb74253e5da66f431
|
# Dataset Card for Evaluation run of chargoddard/llama-2-34b-uncode
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chargoddard/llama-2-34b-uncode
- **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 [chargoddard/llama-2-34b-uncode](https://huggingface.co/chargoddard/llama-2-34b-uncode) 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 agregated 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_chargoddard__llama-2-34b-uncode",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-17T09:49:44.237911](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-34b-uncode/blob/main/results_2023-10-17T09-49-44.237911.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": {
"em": 0.0014681208053691276,
"em_stderr": 0.000392104219029832,
"f1": 0.054323615771812044,
"f1_stderr": 0.001268355641976372,
"acc": 0.47561084340161075,
"acc_stderr": 0.01172411036273294
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.000392104219029832,
"f1": 0.054323615771812044,
"f1_stderr": 0.001268355641976372
},
"harness|gsm8k|5": {
"acc": 0.20773313115996966,
"acc_stderr": 0.011174572716705883
},
"harness|winogrande|5": {
"acc": 0.7434885556432518,
"acc_stderr": 0.012273648008759998
}
}
```
### 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_chargoddard__llama-2-34b-uncode
|
[
"region:us"
] |
2023-08-29T01:23:22+00:00
|
{"pretty_name": "Evaluation run of chargoddard/llama-2-34b-uncode", "dataset_summary": "Dataset automatically created during the evaluation run of model [chargoddard/llama-2-34b-uncode](https://huggingface.co/chargoddard/llama-2-34b-uncode) 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 agregated 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_chargoddard__llama-2-34b-uncode\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-17T09:49:44.237911](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-34b-uncode/blob/main/results_2023-10-17T09-49-44.237911.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 \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.000392104219029832,\n \"f1\": 0.054323615771812044,\n \"f1_stderr\": 0.001268355641976372,\n \"acc\": 0.47561084340161075,\n \"acc_stderr\": 0.01172411036273294\n },\n \"harness|drop|3\": {\n \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.000392104219029832,\n \"f1\": 0.054323615771812044,\n \"f1_stderr\": 0.001268355641976372\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.20773313115996966,\n \"acc_stderr\": 0.011174572716705883\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7434885556432518,\n \"acc_stderr\": 0.012273648008759998\n }\n}\n```", "repo_url": "https://huggingface.co/chargoddard/llama-2-34b-uncode", "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": 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["**/details_harness|truthfulqa:mc|0_2023-08-29T02:22:47.016201.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T02:22:47.016201.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_17T09_49_44.237911", "path": ["**/details_harness|winogrande|5_2023-10-17T09-49-44.237911.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-17T09-49-44.237911.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T02_22_47.016201", "path": ["results_2023-08-29T02:22:47.016201.parquet"]}, {"split": "2023_10_17T09_49_44.237911", "path": ["results_2023-10-17T09-49-44.237911.parquet"]}, {"split": "latest", "path": ["results_2023-10-17T09-49-44.237911.parquet"]}]}]}
|
2023-10-17T08:49:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of chargoddard/llama-2-34b-uncode
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model chargoddard/llama-2-34b-uncode 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 agregated 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 2023-10-17T09:49:44.237911(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 chargoddard/llama-2-34b-uncode",
"## 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 chargoddard/llama-2-34b-uncode 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 agregated 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 2023-10-17T09:49:44.237911(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 chargoddard/llama-2-34b-uncode",
"## 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 chargoddard/llama-2-34b-uncode 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 agregated 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 2023-10-17T09:49:44.237911(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",
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"### Data Instances",
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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 chargoddard/llama-2-34b-uncode## 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 chargoddard/llama-2-34b-uncode 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 agregated 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 2023-10-17T09:49:44.237911(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"
] |
231c14ccba97c5d413f1cb5e527b43582d180d4d
|
# Generic ConLL
Load ConLL formated files using `datasets.load_dataset`.
## Usage
Use explicitly the keyword argument `data_files`.
```python
from datasets import load_dataset
load_dataset("eduagarcia/generic_conll", data_files="https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/train/train.conll")
```
or
```python
from datasets import load_dataset
load_dataset("eduagarcia/generic_conll", data_files={
"train": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/train/train.conll",
"dev": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/dev/dev.conll",
"test": "https://raw.githubusercontent.com/peluz/lener-br/master/leNER-Br/test/test.conll",
},
separator=" ",
tag_index=-1
)
```
|
eduagarcia/generic_conll
|
[
"region:us"
] |
2023-08-29T01:32:40+00:00
|
{"pretty_name": "Generic CoNLL"}
|
2024-02-03T05:57:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Generic ConLL
Load ConLL formated files using 'datasets.load_dataset'.
## Usage
Use explicitly the keyword argument 'data_files'.
or
|
[
"# Generic ConLL\n\nLoad ConLL formated files using 'datasets.load_dataset'.",
"## Usage\nUse explicitly the keyword argument 'data_files'.\n\n\n\nor"
] |
[
"TAGS\n#region-us \n",
"# Generic ConLL\n\nLoad ConLL formated files using 'datasets.load_dataset'.",
"## Usage\nUse explicitly the keyword argument 'data_files'.\n\n\n\nor"
] |
[
6,
24,
17
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[
"passage: TAGS\n#region-us \n# Generic ConLL\n\nLoad ConLL formated files using 'datasets.load_dataset'.## Usage\nUse explicitly the keyword argument 'data_files'.\n\n\n\nor"
] |
5b0d01b26ad38f21f6be5fe79475efcb28959e94
|
# Constitution of India
Constitution of India in JSON Format
All Articles on the Indian Constitution in the following 3 Formats
1. CSV - Comma Seperated Values | [Constitution of India.csv](https://github.com/civictech-India/constitution-of-india/blob/main/Constitution%20of%20India.csv "Constitution of India.csv")
2. Db - SQLite Database File | [COI.db](https://github.com/civictech-India/constitution-of-india/blob/main/COI.db "COI.db")
3. JSON - Java Script Object Notation | [constitution_of_india.json](https://github.com/civictech-India/constitution-of-india/blob/main/constitution_of_india.json "constitution_of_india.json")
|
HarshSinyal/COI_allProvisions2019
|
[
"region:us"
] |
2023-08-29T01:45:03+00:00
|
{}
|
2023-08-29T09:44:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Constitution of India
Constitution of India in JSON Format
All Articles on the Indian Constitution in the following 3 Formats
1. CSV - Comma Seperated Values | Constitution of URL
2. Db - SQLite Database File | URL
3. JSON - Java Script Object Notation | constitution_of_india.json
|
[
"# Constitution of India\nConstitution of India in JSON Format\nAll Articles on the Indian Constitution in the following 3 Formats\n1. CSV - Comma Seperated Values | Constitution of URL\n2. Db - SQLite Database File | URL\n3. JSON - Java Script Object Notation | constitution_of_india.json"
] |
[
"TAGS\n#region-us \n",
"# Constitution of India\nConstitution of India in JSON Format\nAll Articles on the Indian Constitution in the following 3 Formats\n1. CSV - Comma Seperated Values | Constitution of URL\n2. Db - SQLite Database File | URL\n3. JSON - Java Script Object Notation | constitution_of_india.json"
] |
[
6,
70
] |
[
"passage: TAGS\n#region-us \n# Constitution of India\nConstitution of India in JSON Format\nAll Articles on the Indian Constitution in the following 3 Formats\n1. CSV - Comma Seperated Values | Constitution of URL\n2. Db - SQLite Database File | URL\n3. JSON - Java Script Object Notation | constitution_of_india.json"
] |
f1483c65b42add291a1263655c618ed621d4a71b
|
# Dataset Card for "fwv2_random_num_train_1000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_num_train_1000_eval_100
|
[
"region:us"
] |
2023-08-29T02:08:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 195871, "num_examples": 2100}, {"name": "train_doc2id", "num_bytes": 92393, "num_examples": 1100}, {"name": "train_id2doc", "num_bytes": 95693, "num_examples": 1100}, {"name": "train_find_word", "num_bytes": 100178, "num_examples": 1000}, {"name": "eval_find_word", "num_bytes": 10146, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 60493, "num_examples": 1100}], "download_size": 0, "dataset_size": 554774}}
|
2023-08-29T04:32:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_num_train_1000_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_num_train_1000_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_num_train_1000_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_num_train_1000_eval_100\"\n\nMore Information needed"
] |
18cd2ba74c4868c2edea613ac282e6f5c189005f
|
# Dataset Card for "flare-es-efp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChanceFocus/flare-es-efp
|
[
"region:us"
] |
2023-08-29T02:10:26+00:00
|
{"dataset_info": {"features": [{"name": "query:", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold", "dtype": "int64"}, {"name": "query", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 66200, "num_examples": 37}], "download_size": 43563, "dataset_size": 66200}}
|
2023-11-13T13:05:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flare-es-efp"
More Information needed
|
[
"# Dataset Card for \"flare-es-efp\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flare-es-efp\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flare-es-efp\"\n\nMore Information needed"
] |
5a2401f4cada2c992bb50476631eb39d10adb2fa
|
# Dataset Card for "flare-es-efpa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChanceFocus/flare-es-efpa
|
[
"region:us"
] |
2023-08-29T02:12:25+00:00
|
{"dataset_info": {"features": [{"name": "query:", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold", "dtype": "int64"}, {"name": "query", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 353055, "num_examples": 228}], "download_size": 141839, "dataset_size": 353055}}
|
2023-11-13T13:05:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flare-es-efpa"
More Information needed
|
[
"# Dataset Card for \"flare-es-efpa\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flare-es-efpa\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flare-es-efpa\"\n\nMore Information needed"
] |
9adf0558e684b9531f99178563eb41c08bea15f5
|
# OpenOrca-50k Dataset
## Description
OpenOrca-50k is a curated subset of the original Open-Orca dataset available on HuggingFace. This subset contains 50,000 random samples from the main dataset. It has been extracted to serve specific research purposes, especially for those requiring a smaller but representative portion of the original dataset.
Each entry in the dataset has the following structure:
- `id`: The unique identifier for the sample.
- `system_prompt`: System-generated prompt or context for the interaction.
- `question`: The main question posed, corresponding to the given prompt.
- `response`: The system's or model's response to the question.
## Source
The original dataset can be found [here](https://huggingface.co/datasets/Open-Orca/OpenOrca).
## Usage
This dataset is primarily tailored for researchers and machine learning practitioners who wish to work with a smaller version of the Open-Orca dataset. It is ideal for swift prototyping or in scenarios with limited computational resources.
To efficiently load the dataset using HuggingFace's datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("kimnt93/OpenOrca-50k")
```
## License
[Open-Orca](https://huggingface.co/datasets/Open-Orca/OpenOrca)
|
kimnt93/OpenOrca-50k
|
[
"region:us"
] |
2023-08-29T02:21:00+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 85583064, "num_examples": 50000}], "download_size": 49265986, "dataset_size": 85583064}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T02:28:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# OpenOrca-50k Dataset
## Description
OpenOrca-50k is a curated subset of the original Open-Orca dataset available on HuggingFace. This subset contains 50,000 random samples from the main dataset. It has been extracted to serve specific research purposes, especially for those requiring a smaller but representative portion of the original dataset.
Each entry in the dataset has the following structure:
- 'id': The unique identifier for the sample.
- 'system_prompt': System-generated prompt or context for the interaction.
- 'question': The main question posed, corresponding to the given prompt.
- 'response': The system's or model's response to the question.
## Source
The original dataset can be found here.
## Usage
This dataset is primarily tailored for researchers and machine learning practitioners who wish to work with a smaller version of the Open-Orca dataset. It is ideal for swift prototyping or in scenarios with limited computational resources.
To efficiently load the dataset using HuggingFace's datasets library:
## License
Open-Orca
|
[
"# OpenOrca-50k Dataset",
"## Description\n\nOpenOrca-50k is a curated subset of the original Open-Orca dataset available on HuggingFace. This subset contains 50,000 random samples from the main dataset. It has been extracted to serve specific research purposes, especially for those requiring a smaller but representative portion of the original dataset.\n\nEach entry in the dataset has the following structure:\n\n- 'id': The unique identifier for the sample.\n- 'system_prompt': System-generated prompt or context for the interaction.\n- 'question': The main question posed, corresponding to the given prompt.\n- 'response': The system's or model's response to the question.",
"## Source\n\nThe original dataset can be found here.",
"## Usage\n\nThis dataset is primarily tailored for researchers and machine learning practitioners who wish to work with a smaller version of the Open-Orca dataset. It is ideal for swift prototyping or in scenarios with limited computational resources.\n\nTo efficiently load the dataset using HuggingFace's datasets library:",
"## License\n\nOpen-Orca"
] |
[
"TAGS\n#region-us \n",
"# OpenOrca-50k Dataset",
"## Description\n\nOpenOrca-50k is a curated subset of the original Open-Orca dataset available on HuggingFace. This subset contains 50,000 random samples from the main dataset. It has been extracted to serve specific research purposes, especially for those requiring a smaller but representative portion of the original dataset.\n\nEach entry in the dataset has the following structure:\n\n- 'id': The unique identifier for the sample.\n- 'system_prompt': System-generated prompt or context for the interaction.\n- 'question': The main question posed, corresponding to the given prompt.\n- 'response': The system's or model's response to the question.",
"## Source\n\nThe original dataset can be found here.",
"## Usage\n\nThis dataset is primarily tailored for researchers and machine learning practitioners who wish to work with a smaller version of the Open-Orca dataset. It is ideal for swift prototyping or in scenarios with limited computational resources.\n\nTo efficiently load the dataset using HuggingFace's datasets library:",
"## License\n\nOpen-Orca"
] |
[
6,
8,
156,
11,
78,
6
] |
[
"passage: TAGS\n#region-us \n# OpenOrca-50k Dataset## Description\n\nOpenOrca-50k is a curated subset of the original Open-Orca dataset available on HuggingFace. This subset contains 50,000 random samples from the main dataset. It has been extracted to serve specific research purposes, especially for those requiring a smaller but representative portion of the original dataset.\n\nEach entry in the dataset has the following structure:\n\n- 'id': The unique identifier for the sample.\n- 'system_prompt': System-generated prompt or context for the interaction.\n- 'question': The main question posed, corresponding to the given prompt.\n- 'response': The system's or model's response to the question.## Source\n\nThe original dataset can be found here.## Usage\n\nThis dataset is primarily tailored for researchers and machine learning practitioners who wish to work with a smaller version of the Open-Orca dataset. It is ideal for swift prototyping or in scenarios with limited computational resources.\n\nTo efficiently load the dataset using HuggingFace's datasets library:## License\n\nOpen-Orca"
] |
81c6307e8e0b78c22be0b073d453903474ac7ce1
|
# Dataset of Sarina Shizukume
This is the dataset of Sarina Shizukume, containing 62 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 62 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 120 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 62 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 62 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 62 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 62 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 62 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 120 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 120 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 120 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/sarina_shizukume_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T02:23:06+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:25:56+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Sarina Shizukume
===========================
This is the dataset of Sarina Shizukume, containing 62 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
4a913194b900e7da03357fc4423fbb9acdcfca62
|
# Dataset of Rina Shioi
This is the dataset of Rina Shioi, containing 81 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 81 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 187 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 81 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 81 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 81 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 81 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 81 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 187 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 187 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 187 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/rina_shioi_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T02:40:45+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:25:58+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Rina Shioi
=====================
This is the dataset of Rina Shioi, containing 81 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
56d816e1912420cddb8de035fdc441e29e7b232c
|
# Dataset of Kiyoharu Suirenji
This is the dataset of Kiyoharu Suirenji, containing 24 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 24 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 53 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 24 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 24 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 24 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 24 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 24 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 53 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 53 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 53 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/kiyoharu_suirenji_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T02:46:27+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:00+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Kiyoharu Suirenji
============================
This is the dataset of Kiyoharu Suirenji, containing 24 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
ce7e98cd373fc358928f9f3106adf054abef7bf6
|
# Dataset Card for "korquad_v1.0_gqa_ab_context"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
wisenut-nlp-team/korquad_v1.0_gqa_ab_context
|
[
"region:us"
] |
2023-08-29T02:49:04+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "context_a", "dtype": "string"}, {"name": "context_b", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86477195, "num_examples": 46621}, {"name": "validation", "num_bytes": 8491857, "num_examples": 4405}], "download_size": 13204891, "dataset_size": 94969052}}
|
2023-08-29T02:49:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "korquad_v1.0_gqa_ab_context"
More Information needed
|
[
"# Dataset Card for \"korquad_v1.0_gqa_ab_context\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"korquad_v1.0_gqa_ab_context\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"korquad_v1.0_gqa_ab_context\"\n\nMore Information needed"
] |
b8c3cacce8ea6e905aefa8e9bfde7e132eee6570
|
# Dataset of Asahi Takiguchi
This is the dataset of Asahi Takiguchi, containing 21 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 21 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 46 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 21 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 21 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 21 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 21 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 21 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 46 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 46 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 46 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/asahi_takiguchi_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T02:51:09+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:03+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Asahi Takiguchi
==========================
This is the dataset of Asahi Takiguchi, containing 21 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
b67bfdce6574adcca8efce9d431665ebc8426c45
|
# Dataset of Kosame Amagai
This is the dataset of Kosame Amagai, containing 19 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 19 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 34 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 19 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 19 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 19 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 19 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 19 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 34 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 34 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 34 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/kosame_amagai_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T02:54:30+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:04+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Kosame Amagai
========================
This is the dataset of Kosame Amagai, containing 19 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
623a89b7bcc7e64328992d74e3fd14d5066da130
|
# Dataset of Mikari Izumigamine
This is the dataset of Mikari Izumigamine, containing 22 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 22 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 48 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 22 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 22 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 22 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 22 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 22 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 48 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 48 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 48 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/mikari_izumigamine_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T02:59:00+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:07+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Mikari Izumigamine
=============================
This is the dataset of Mikari Izumigamine, containing 22 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
86a293368ba1927faaf258cdd9aa91117e656fc7
|
Filtered version of code search net python subset, with filtering based on perplexity with/without docstring, learning value/quality classifiers, and manual filtering.
Original data with perplexity filtering is from [here](https://huggingface.co/datasets/bjoernp/code_search_net_python_processed_400k), with credit to bjoernp.
|
vikp/code_search_net_filtered_34k
|
[
"license:cc-by-4.0",
"region:us"
] |
2023-08-29T03:02:00+00:00
|
{"license": "cc-by-4.0", "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "signature", "dtype": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "loss_without_docstring", "dtype": "float64"}, {"name": "loss_with_docstring", "dtype": "float64"}, {"name": "factor", "dtype": "float64"}, {"name": "rendered", "dtype": "string"}, {"name": "quality_prob", "dtype": "float64"}, {"name": "learning_prob", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 59688305.816243604, "num_examples": 34488}], "download_size": 30704027, "dataset_size": 59688305.816243604}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T01:47:40+00:00
|
[] |
[] |
TAGS
#license-cc-by-4.0 #region-us
|
Filtered version of code search net python subset, with filtering based on perplexity with/without docstring, learning value/quality classifiers, and manual filtering.
Original data with perplexity filtering is from here, with credit to bjoernp.
|
[] |
[
"TAGS\n#license-cc-by-4.0 #region-us \n"
] |
[
15
] |
[
"passage: TAGS\n#license-cc-by-4.0 #region-us \n"
] |
88d1e750922bc5496f1b535b12e4e39a14a08f4d
|
# Dataset Card for "flare-es-tsa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ChanceFocus/flare-es-tsa
|
[
"region:us"
] |
2023-08-29T03:09:17+00:00
|
{"dataset_info": {"features": [{"name": "query", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "choices", "sequence": "string"}, {"name": "gold", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 1316673, "num_examples": 3829}], "download_size": 483832, "dataset_size": 1316673}}
|
2023-12-15T08:57:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flare-es-tsa"
More Information needed
|
[
"# Dataset Card for \"flare-es-tsa\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flare-es-tsa\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flare-es-tsa\"\n\nMore Information needed"
] |
83e18ac52e1f89213798f539b1a4e02cb61aa7a2
|
# Dataset of Nijimi Anazawa
This is the dataset of Nijimi Anazawa, containing 108 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 108 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 243 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 108 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 108 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 108 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 108 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 108 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 243 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 243 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 243 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/nijimi_anazawa_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T03:20:11+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:09+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Nijimi Anazawa
=========================
This is the dataset of Nijimi Anazawa, containing 108 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
be86b17c875fd8b8da84c9d081b6f63a55cacf21
|
# Dataset Card for Evaluation run of bongchoi/test-llama2-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bongchoi/test-llama2-7b
- **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 [bongchoi/test-llama2-7b](https://huggingface.co/bongchoi/test-llama2-7b) 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 agregated 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_bongchoi__test-llama2-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-16T19:36:12.019633](https://huggingface.co/datasets/open-llm-leaderboard/details_bongchoi__test-llama2-7b/blob/main/results_2023-09-16T19-36-12.019633.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": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461104,
"f1": 0.05606543624161075,
"f1_stderr": 0.0013211107078874738,
"acc": 0.4057988012013119,
"acc_stderr": 0.00970458141675358
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196461104,
"f1": 0.05606543624161075,
"f1_stderr": 0.0013211107078874738
},
"harness|gsm8k|5": {
"acc": 0.0712661106899166,
"acc_stderr": 0.007086462127954491
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
}
}
```
### 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_bongchoi__test-llama2-7b
|
[
"region:us"
] |
2023-08-29T03:26:30+00:00
|
{"pretty_name": "Evaluation run of bongchoi/test-llama2-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [bongchoi/test-llama2-7b](https://huggingface.co/bongchoi/test-llama2-7b) 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 agregated 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_bongchoi__test-llama2-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-16T19:36:12.019633](https://huggingface.co/datasets/open-llm-leaderboard/details_bongchoi__test-llama2-7b/blob/main/results_2023-09-16T19-36-12.019633.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 \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196461104,\n \"f1\": 0.05606543624161075,\n \"f1_stderr\": 0.0013211107078874738,\n \"acc\": 0.4057988012013119,\n \"acc_stderr\": 0.00970458141675358\n },\n \"harness|drop|3\": {\n \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196461104,\n \"f1\": 0.05606543624161075,\n \"f1_stderr\": 0.0013211107078874738\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0712661106899166,\n \"acc_stderr\": 0.007086462127954491\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n }\n}\n```", "repo_url": "https://huggingface.co/bongchoi/test-llama2-7b", "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_08_29T04_25_39.762695", "path": ["**/details_harness|arc:challenge|25_2023-08-29T04:25:39.762695.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T04:25:39.762695.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_16T19_36_12.019633", "path": ["**/details_harness|drop|3_2023-09-16T19-36-12.019633.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-16T19-36-12.019633.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_16T19_36_12.019633", "path": ["**/details_harness|gsm8k|5_2023-09-16T19-36-12.019633.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-16T19-36-12.019633.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T04_25_39.762695", "path": ["**/details_harness|hellaswag|10_2023-08-29T04:25:39.762695.parquet"]}, {"split": "latest", "path": 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|
2023-09-16T18:36:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of bongchoi/test-llama2-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model bongchoi/test-llama2-7b 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 agregated 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 2023-09-16T19:36:12.019633(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 bongchoi/test-llama2-7b",
"## 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 bongchoi/test-llama2-7b 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 agregated 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 2023-09-16T19:36:12.019633(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 bongchoi/test-llama2-7b",
"## 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 bongchoi/test-llama2-7b 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 agregated 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 2023-09-16T19:36:12.019633(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",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of bongchoi/test-llama2-7b## 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 bongchoi/test-llama2-7b 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 agregated 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 2023-09-16T19:36:12.019633(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"
] |
45248acbb6b7a52b18de5d91ab4e145df551b5b9
|
# Dataset of Tsuyuno Yatsumura
This is the dataset of Tsuyuno Yatsumura, containing 179 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 179 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 321 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 179 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 179 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 179 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 179 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 179 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 321 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 321 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 321 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/tsuyuno_yatsumura_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T03:54:04+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:11+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Tsuyuno Yatsumura
============================
This is the dataset of Tsuyuno Yatsumura, containing 179 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
3bc17b74f8c75ff65e9045074f0f30885bda0c95
|
This dataset was downloaded from https://www.kaggle.com/datasets/pes12017000148/food-ingredients-and-recipe-dataset-with-images?resource=download
|
Hieu-Pham/kaggle_food_recipes
|
[
"license:cc-by-sa-3.0",
"region:us"
] |
2023-08-29T04:01:09+00:00
|
{"license": "cc-by-sa-3.0"}
|
2023-08-29T12:11:57+00:00
|
[] |
[] |
TAGS
#license-cc-by-sa-3.0 #region-us
|
This dataset was downloaded from URL
|
[] |
[
"TAGS\n#license-cc-by-sa-3.0 #region-us \n"
] |
[
17
] |
[
"passage: TAGS\n#license-cc-by-sa-3.0 #region-us \n"
] |
8369d289d04222a7ee3f87cb6d1dd200fa28496a
|
# Dataset Card for "RLCD-generated-preference-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TaylorAI/RLCD-generated-preference-data
|
[
"region:us"
] |
2023-08-29T04:18:25+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "float64"}, {"name": "output_1", "dtype": "string"}, {"name": "output_2", "dtype": "string"}, {"name": "preference", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 149793678, "num_examples": 167999}], "download_size": 87743717, "dataset_size": 149793678}}
|
2023-08-29T04:19:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "RLCD-generated-preference-data"
More Information needed
|
[
"# Dataset Card for \"RLCD-generated-preference-data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"RLCD-generated-preference-data\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"RLCD-generated-preference-data\"\n\nMore Information needed"
] |
991ce904c34d29eec2070f080a0a11a39d4923cf
|
A cleaned version of the Wikihow dataset for abstractive text summarization.
# Changes made
Changes to the original dataset include:
- All words have been made lowercase
- All punctuation removed except ".", "," and "-"
- Spaces added before and after all punctuation
- NA values dropped from dataset
- Leading and trailing newline and space characters removed
These changes allow for easier tokenization.
# Citation
```
@misc{koupaee2018wikihow,
title={WikiHow: A Large Scale Text Summarization Dataset},
author={Mahnaz Koupaee and William Yang Wang},
year={2018},
eprint={1810.09305},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
gursi26/wikihow-cleaned
|
[
"task_categories:summarization",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-3.0",
"arxiv:1810.09305",
"region:us"
] |
2023-08-29T04:22:41+00:00
|
{"language": ["en"], "license": "cc-by-nc-sa-3.0", "size_categories": ["100K<n<1M"], "task_categories": ["summarization", "text-generation"]}
|
2023-08-29T04:32:23+00:00
|
[
"1810.09305"
] |
[
"en"
] |
TAGS
#task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-sa-3.0 #arxiv-1810.09305 #region-us
|
A cleaned version of the Wikihow dataset for abstractive text summarization.
# Changes made
Changes to the original dataset include:
- All words have been made lowercase
- All punctuation removed except ".", "," and "-"
- Spaces added before and after all punctuation
- NA values dropped from dataset
- Leading and trailing newline and space characters removed
These changes allow for easier tokenization.
|
[
"# Changes made\nChanges to the original dataset include:\n- All words have been made lowercase\n- All punctuation removed except \".\", \",\" and \"-\"\n- Spaces added before and after all punctuation\n- NA values dropped from dataset\n- Leading and trailing newline and space characters removed\n\nThese changes allow for easier tokenization."
] |
[
"TAGS\n#task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-sa-3.0 #arxiv-1810.09305 #region-us \n",
"# Changes made\nChanges to the original dataset include:\n- All words have been made lowercase\n- All punctuation removed except \".\", \",\" and \"-\"\n- Spaces added before and after all punctuation\n- NA values dropped from dataset\n- Leading and trailing newline and space characters removed\n\nThese changes allow for easier tokenization."
] |
[
65,
78
] |
[
"passage: TAGS\n#task_categories-summarization #task_categories-text-generation #size_categories-100K<n<1M #language-English #license-cc-by-nc-sa-3.0 #arxiv-1810.09305 #region-us \n# Changes made\nChanges to the original dataset include:\n- All words have been made lowercase\n- All punctuation removed except \".\", \",\" and \"-\"\n- Spaces added before and after all punctuation\n- NA values dropped from dataset\n- Leading and trailing newline and space characters removed\n\nThese changes allow for easier tokenization."
] |
c01735db87737bc0174c88615da7336896f8b185
|
# Dataset Card for "fwv2_random_num_train_100_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_num_train_100_eval_100
|
[
"region:us"
] |
2023-08-29T04:32:02+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27122, "num_examples": 300}, {"name": "train_doc2id", "num_bytes": 16692, "num_examples": 200}, {"name": "train_id2doc", "num_bytes": 17292, "num_examples": 200}, {"name": "train_find_word", "num_bytes": 9830, "num_examples": 100}, {"name": "eval_find_word", "num_bytes": 9946, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 10892, "num_examples": 200}], "download_size": 52332, "dataset_size": 91774}}
|
2023-08-29T04:32:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_num_train_100_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_num_train_100_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_num_train_100_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_num_train_100_eval_100\"\n\nMore Information needed"
] |
5c80e9067e87072c7f3c438f2bace321d21d3918
|
# Dataset Card for "fwv2_random_num_train_10000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_num_train_10000_eval_100
|
[
"region:us"
] |
2023-08-29T04:32:41+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1909652, "num_examples": 20100}, {"name": "train_doc2id", "num_bytes": 857494, "num_examples": 10100}, {"name": "train_id2doc", "num_bytes": 887794, "num_examples": 10100}, {"name": "train_find_word", "num_bytes": 1021858, "num_examples": 10000}, {"name": "eval_find_word", "num_bytes": 10346, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 564594, "num_examples": 10100}], "download_size": 2074803, "dataset_size": 5251738}}
|
2023-08-29T04:33:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_num_train_10000_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_num_train_10000_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_num_train_10000_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_num_train_10000_eval_100\"\n\nMore Information needed"
] |
0dd0baeefe2c221870b45c68eea37edc911ac9db
|
# Dataset of Aya Asagiri
This is the dataset of Aya Asagiri, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 200 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 379 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 200 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 200 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 200 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 200 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 200 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 379 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 379 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 379 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/aya_asagiri_mahoushoujosite
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-08-29T04:34:20+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-17T16:26:13+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Aya Asagiri
======================
This is the dataset of Aya Asagiri, containing 200 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
|
[] |
[
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
] |
03907c4cc564ddabd3799254e17ff8aba427aa09
|
# Dataset Card for "niv2_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LahiruLowe/niv2_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML
|
[
"region:us"
] |
2023-08-29T04:41:13+00:00
|
{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}, {"name": "system_message", "dtype": "string"}, {"name": "explained_targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6693778, "num_examples": 4665}], "download_size": 3173492, "dataset_size": 6693778}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-09T20:05:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "niv2_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML"
More Information needed
|
[
"# Dataset Card for \"niv2_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"niv2_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
[
6,
47
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"niv2_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
647e5ed8632574de4cc00600d5224ef26756cdea
|
# Dataset Card for "fwv2_squad_num_train_100_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_squad_num_train_100_eval_100
|
[
"region:us"
] |
2023-08-29T04:50:57+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45785, "num_examples": 300}, {"name": "train_doc2id", "num_bytes": 34449, "num_examples": 200}, {"name": "train_id2doc", "num_bytes": 35049, "num_examples": 200}, {"name": "train_find_word", "num_bytes": 10736, "num_examples": 100}, {"name": "eval_find_word", "num_bytes": 10344, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 28649, "num_examples": 200}], "download_size": 104070, "dataset_size": 165012}}
|
2023-08-29T07:05:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_num_train_100_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_num_train_100_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_num_train_100_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_num_train_100_eval_100\"\n\nMore Information needed"
] |
418c931f6eaf842f5016494d9add16b549b6e17f
|
# Dataset Card for "fwv2_squad_num_train_1000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_squad_num_train_1000_eval_100
|
[
"region:us"
] |
2023-08-29T04:51:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 300908, "num_examples": 2100}, {"name": "train_doc2id", "num_bytes": 188562, "num_examples": 1100}, {"name": "train_id2doc", "num_bytes": 191862, "num_examples": 1100}, {"name": "train_find_word", "num_bytes": 109046, "num_examples": 1000}, {"name": "eval_find_word", "num_bytes": 10620, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 156662, "num_examples": 1100}], "download_size": 513271, "dataset_size": 957660}}
|
2023-08-29T07:05:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_num_train_1000_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_num_train_1000_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_num_train_1000_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_num_train_1000_eval_100\"\n\nMore Information needed"
] |
ca6877b1c47e8b1b05a7804c10ee4018c31b224b
|
# Dataset Card for "fwv2_squad_num_train_10000_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_squad_num_train_10000_eval_100
|
[
"region:us"
] |
2023-08-29T04:51:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2877195, "num_examples": 20100}, {"name": "train_doc2id", "num_bytes": 1736997, "num_examples": 10100}, {"name": "train_id2doc", "num_bytes": 1767297, "num_examples": 10100}, {"name": "train_find_word", "num_bytes": 1109898, "num_examples": 10000}, {"name": "eval_find_word", "num_bytes": 10775, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 1444097, "num_examples": 10100}], "download_size": 4619144, "dataset_size": 8946259}}
|
2023-08-29T07:06:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_num_train_10000_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_num_train_10000_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_num_train_10000_eval_100\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_num_train_10000_eval_100\"\n\nMore Information needed"
] |
b8284832f8be18223a64813719318d619387ded7
|
# Dataset Card for "t0_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LahiruLowe/t0_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML
|
[
"region:us"
] |
2023-08-29T04:56:25+00:00
|
{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}, {"name": "system_message", "dtype": "string"}, {"name": "explained_targets", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1045060, "num_examples": 579}], "download_size": 0, "dataset_size": 1045060}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-31T04:10:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "t0_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML"
More Information needed
|
[
"# Dataset Card for \"t0_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"t0_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
[
6,
46
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"t0_explanation_targets_h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML\"\n\nMore Information needed"
] |
da52b671d390ef05202355d331bcee69fc00be6d
|
# Dataset Card for "RLCD-SFT-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
TaylorAI/RLCD-SFT-dataset
|
[
"region:us"
] |
2023-08-29T05:04:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "preferred_output", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 121911383, "num_examples": 167999}], "download_size": 71387145, "dataset_size": 121911383}}
|
2023-08-29T05:11:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "RLCD-SFT-dataset"
More Information needed
|
[
"# Dataset Card for \"RLCD-SFT-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"RLCD-SFT-dataset\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"RLCD-SFT-dataset\"\n\nMore Information needed"
] |
de970ed30244063fab2539dab3bad3c9749d24de
|
# Dataset Card for "cot_filtered_3pertask"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
LahiruLowe/cot_filtered_3pertask
|
[
"region:us"
] |
2023-08-29T05:32:19+00:00
|
{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22427, "num_examples": 54}], "download_size": 15007, "dataset_size": 22427}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T05:32:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cot_filtered_3pertask"
More Information needed
|
[
"# Dataset Card for \"cot_filtered_3pertask\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cot_filtered_3pertask\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cot_filtered_3pertask\"\n\nMore Information needed"
] |
d5fee5e36b3bea851c37552bd63987bae0a44274
|
# Dataset Card for "fwv2_random_num_tip_train_100_eval_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_num_tip_train_100_eval_100
|
[
"region:us"
] |
2023-08-29T05:41:34+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39706, "num_examples": 300}, {"name": "train_doc2id", "num_bytes": 16692, "num_examples": 200}, {"name": "train_id2doc", "num_bytes": 17292, "num_examples": 200}, {"name": "train_find_word", "num_bytes": 22414, "num_examples": 100}, {"name": "eval_find_word", "num_bytes": 16346, "num_examples": 100}, {"name": "id_context_mapping", "num_bytes": 10892, "num_examples": 200}], "download_size": 41369, "dataset_size": 123342}}
|
2023-08-29T05:43:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_num_tip_train_100_eval_100"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_num_tip_train_100_eval_100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_num_tip_train_100_eval_100\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_num_tip_train_100_eval_100\"\n\nMore Information needed"
] |
4d8030c03131f82121c786d1c8b190d70d674189
|
# Dataset Card for "stanford_dataset_qa_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vivekraina/stanford_dataset_qa_final
|
[
"region:us"
] |
2023-08-29T05:46:04+00:00
|
{"dataset_info": {"features": [{"name": "paragraphs", "list": [{"name": "context", "dtype": "string"}, {"name": "qas", "list": [{"name": "answers", "list": [{"name": "answer_start", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}, {"name": "id", "dtype": "string"}, {"name": "question", "dtype": "string"}]}]}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3745671, "num_examples": 48}], "download_size": 1775277, "dataset_size": 3745671}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T05:46:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "stanford_dataset_qa_final"
More Information needed
|
[
"# Dataset Card for \"stanford_dataset_qa_final\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"stanford_dataset_qa_final\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"stanford_dataset_qa_final\"\n\nMore Information needed"
] |
e476d02d1e42f71097bd8082264c5d41183c8123
|
# Dataset Card for "fwv2_random_num_tip_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_num_tip_train_10_eval_10
|
[
"region:us"
] |
2023-08-29T06:02:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3919, "num_examples": 30}, {"name": "train_doc2id", "num_bytes": 1651, "num_examples": 20}, {"name": "train_id2doc", "num_bytes": 1711, "num_examples": 20}, {"name": "train_find_word", "num_bytes": 2208, "num_examples": 10}, {"name": "eval_find_word", "num_bytes": 1604, "num_examples": 10}, {"name": "id_context_mapping", "num_bytes": 1071, "num_examples": 20}], "download_size": 19912, "dataset_size": 12164}}
|
2023-08-29T06:02:31+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_num_tip_train_10_eval_10"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_num_tip_train_10_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_num_tip_train_10_eval_10\"\n\nMore Information needed"
] |
[
6,
31
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_num_tip_train_10_eval_10\"\n\nMore Information needed"
] |
db83583a1e2d9c7a9d9535c11c7ad699f60eef41
|
# Dataset Card for Evaluation run of kashif/stack-llama-2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/kashif/stack-llama-2
- **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 [kashif/stack-llama-2](https://huggingface.co/kashif/stack-llama-2) 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 agregated 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_kashif__stack-llama-2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T20:19:04.146812](https://huggingface.co/datasets/open-llm-leaderboard/details_kashif__stack-llama-2/blob/main/results_2023-09-22T20-19-04.146812.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": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931194434,
"f1": 0.05443896812080537,
"f1_stderr": 0.0012685965060744062,
"acc": 0.4202036533620397,
"acc_stderr": 0.010294487617119145
},
"harness|drop|3": {
"em": 0.0012583892617449664,
"em_stderr": 0.00036305608931194434,
"f1": 0.05443896812080537,
"f1_stderr": 0.0012685965060744062
},
"harness|gsm8k|5": {
"acc": 0.10007581501137225,
"acc_stderr": 0.008266274528685624
},
"harness|winogrande|5": {
"acc": 0.7403314917127072,
"acc_stderr": 0.012322700705552667
}
}
```
### 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_kashif__stack-llama-2
|
[
"region:us"
] |
2023-08-29T06:08:23+00:00
|
{"pretty_name": "Evaluation run of kashif/stack-llama-2", "dataset_summary": "Dataset automatically created during the evaluation run of model [kashif/stack-llama-2](https://huggingface.co/kashif/stack-llama-2) 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 agregated 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_kashif__stack-llama-2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-22T20:19:04.146812](https://huggingface.co/datasets/open-llm-leaderboard/details_kashif__stack-llama-2/blob/main/results_2023-09-22T20-19-04.146812.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 \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931194434,\n \"f1\": 0.05443896812080537,\n \"f1_stderr\": 0.0012685965060744062,\n \"acc\": 0.4202036533620397,\n \"acc_stderr\": 0.010294487617119145\n },\n \"harness|drop|3\": {\n \"em\": 0.0012583892617449664,\n \"em_stderr\": 0.00036305608931194434,\n \"f1\": 0.05443896812080537,\n \"f1_stderr\": 0.0012685965060744062\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10007581501137225,\n \"acc_stderr\": 0.008266274528685624\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7403314917127072,\n \"acc_stderr\": 0.012322700705552667\n }\n}\n```", "repo_url": "https://huggingface.co/kashif/stack-llama-2", "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": 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["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-security_studies|5_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T07:07:44.494010.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T07:07:44.494010.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_22T20_19_04.146812", "path": ["**/details_harness|winogrande|5_2023-09-22T20-19-04.146812.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-22T20-19-04.146812.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T07_07_44.494010", "path": ["results_2023-08-29T07:07:44.494010.parquet"]}, {"split": "2023_09_22T20_19_04.146812", "path": ["results_2023-09-22T20-19-04.146812.parquet"]}, {"split": "latest", "path": ["results_2023-09-22T20-19-04.146812.parquet"]}]}]}
|
2023-09-22T19:19:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of kashif/stack-llama-2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model kashif/stack-llama-2 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 agregated 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 2023-09-22T20:19:04.146812(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 kashif/stack-llama-2",
"## 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 kashif/stack-llama-2 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 agregated 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 2023-09-22T20:19:04.146812(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 kashif/stack-llama-2",
"## 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 kashif/stack-llama-2 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 agregated 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 2023-09-22T20:19:04.146812(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"
] |
[
6,
19,
31,
167,
67,
10,
4,
6,
6,
5,
5,
5,
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4,
10,
10,
5,
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9,
8,
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7,
8,
7,
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6,
5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of kashif/stack-llama-2## 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 kashif/stack-llama-2 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 agregated 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 2023-09-22T20:19:04.146812(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"
] |
39999ad987981d489f8e178e2b28fe32186e4996
|
# Dataset Card for "id_panl_bppt_with_amrbart_amr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
abdiharyadi/id_panl_bppt_with_amrbart_amr
|
[
"region:us"
] |
2023-08-29T06:19:53+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "translation", "dtype": {"translation": {"languages": ["en", "id"]}}}, {"name": "topic", "dtype": {"class_label": {"names": {"0": "Economy", "1": "International", "2": "Science", "3": "Sport"}}}}, {"name": "amr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 365469, "num_examples": 1220}], "download_size": 170150, "dataset_size": 365469}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T10:15:01+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "id_panl_bppt_with_amrbart_amr"
More Information needed
|
[
"# Dataset Card for \"id_panl_bppt_with_amrbart_amr\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"id_panl_bppt_with_amrbart_amr\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"id_panl_bppt_with_amrbart_amr\"\n\nMore Information needed"
] |
24a1375094119b3ef737485494385e1141504a2a
|
# Dataset Card for "fwv2_random_num_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/fwv2_random_num_train_10_eval_10
|
[
"region:us"
] |
2023-08-29T06:26:31+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2677, "num_examples": 30}, {"name": "train_doc2id", "num_bytes": 1651, "num_examples": 20}, {"name": "train_id2doc", "num_bytes": 1711, "num_examples": 20}, {"name": "train_find_word", "num_bytes": 966, "num_examples": 10}, {"name": "eval_find_word", "num_bytes": 974, "num_examples": 10}, {"name": "id_context_mapping", "num_bytes": 1071, "num_examples": 20}], "download_size": 16570, "dataset_size": 9050}}
|
2023-08-29T06:26:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_random_num_train_10_eval_10"
More Information needed
|
[
"# Dataset Card for \"fwv2_random_num_train_10_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_random_num_train_10_eval_10\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_random_num_train_10_eval_10\"\n\nMore Information needed"
] |
959d05669d1dd6f69612b8724c7235337329a7a6
|
# Dataset Card for "quangnguyen-test-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qnquang/quangnguyen-test-llama2-1k
|
[
"region:us"
] |
2023-08-29T06:39:20+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1408214, "num_examples": 1000}], "download_size": 819674, "dataset_size": 1408214}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T06:39:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "quangnguyen-test-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"quangnguyen-test-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"quangnguyen-test-llama2-1k\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"quangnguyen-test-llama2-1k\"\n\nMore Information needed"
] |
bc52f942f9e46ba5ccfc6cd90eb46097ab883e61
|
# Dataset Card for "pubmed_sonnet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/pubmed_sonnet
|
[
"region:us"
] |
2023-08-29T06:40:33+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "MedlineCitation", "struct": [{"name": "PMID", "dtype": "int32"}, {"name": "DateCompleted", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "NumberOfReferences", "dtype": "int32"}, {"name": "DateRevised", "struct": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}, {"name": "Article", "struct": [{"name": "Abstract", "struct": [{"name": "AbstractText", "dtype": "string"}]}, {"name": "ArticleTitle", "dtype": "string"}, {"name": "AuthorList", "struct": [{"name": "Author", "sequence": [{"name": "LastName", "dtype": "string"}, {"name": "ForeName", "dtype": "string"}, {"name": "Initials", "dtype": "string"}, {"name": "CollectiveName", "dtype": "string"}]}]}, {"name": "Language", "dtype": "string"}, {"name": "GrantList", "struct": [{"name": "Grant", "sequence": [{"name": "GrantID", "dtype": "string"}, {"name": "Agency", "dtype": "string"}, {"name": "Country", "dtype": "string"}]}]}, {"name": "PublicationTypeList", "struct": [{"name": "PublicationType", "sequence": "string"}]}]}, {"name": "MedlineJournalInfo", "struct": [{"name": "Country", "dtype": "string"}]}, {"name": "ChemicalList", "struct": [{"name": "Chemical", "sequence": [{"name": "RegistryNumber", "dtype": "string"}, {"name": "NameOfSubstance", "dtype": "string"}]}]}, {"name": "CitationSubset", "dtype": "string"}, {"name": "MeshHeadingList", "struct": [{"name": "MeshHeading", "sequence": [{"name": "DescriptorName", "dtype": "string"}, {"name": "QualifierName", "dtype": "string"}]}]}]}, {"name": "PubmedData", "struct": [{"name": "ArticleIdList", "sequence": [{"name": "ArticleId", "sequence": "string"}]}, {"name": "PublicationStatus", "dtype": "string"}, {"name": "History", "struct": [{"name": "PubMedPubDate", "sequence": [{"name": "Year", "dtype": "int32"}, {"name": "Month", "dtype": "int32"}, {"name": "Day", "dtype": "int32"}]}]}, {"name": "ReferenceList", "sequence": [{"name": "Citation", "dtype": "string"}, {"name": "CitationId", "dtype": "int32"}]}]}, {"name": "text", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "original_text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 3712700.992, "num_examples": 974}], "download_size": 2134679, "dataset_size": 3712700.992}}
|
2023-08-29T12:17:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "pubmed_sonnet"
More Information needed
|
[
"# Dataset Card for \"pubmed_sonnet\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"pubmed_sonnet\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"pubmed_sonnet\"\n\nMore Information needed"
] |
ca96537132a53773bacb92c99eeff0e3caced200
|
# Bangumi Image Base of Recently, My Sister Is Unusual
This is the image base of bangumi Recently, My Sister Is Unusual, we detected 17 characters, 1618 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 51 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 9 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 82 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 466 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 33 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 394 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 102 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 7 | [Download](7/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 8 | 25 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 73 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 50 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 13 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 143 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 11 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 16 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 39 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 104 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/imocho
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-08-29T06:48:41+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-10-05T12:06:08+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Recently, My Sister Is Unusual
====================================================
This is the image base of bangumi Recently, My Sister Is Unusual, we detected 17 characters, 1618 images in total. The full dataset is here.
Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual. If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
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[] |
[
"TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-mit #art #region-us \n"
] |
2f96af78c4551cc8f0537e29f3224dfec3873c96
|
# Dataset Card for "niv2_filtered_3pertask"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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LahiruLowe/niv2_filtered_3pertask
|
[
"region:us"
] |
2023-08-29T06:50:26+00:00
|
{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4509772, "num_examples": 4668}], "download_size": 2486682, "dataset_size": 4509772}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
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2023-08-29T06:50:28+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "niv2_filtered_3pertask"
More Information needed
|
[
"# Dataset Card for \"niv2_filtered_3pertask\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"niv2_filtered_3pertask\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"niv2_filtered_3pertask\"\n\nMore Information needed"
] |
ae3afbca852dc3a3f028daa3f96b8a1dfe2168b6
|
# Dataset Card for "t0_filtered_3pertask"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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LahiruLowe/t0_filtered_3pertask
|
[
"region:us"
] |
2023-08-29T06:55:53+00:00
|
{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 702847, "num_examples": 579}], "download_size": 0, "dataset_size": 702847}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
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2023-08-29T06:55:54+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "t0_filtered_3pertask"
More Information needed
|
[
"# Dataset Card for \"t0_filtered_3pertask\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"t0_filtered_3pertask\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"t0_filtered_3pertask\"\n\nMore Information needed"
] |
3966a8601c1b980b38af1686160cdb9b00d70309
|
# Dataset Card for "flan2021_filtered_3pertask"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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LahiruLowe/flan2021_filtered_3pertask
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[
"region:us"
] |
2023-08-29T07:03:51+00:00
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{"dataset_info": {"features": [{"name": "original_index", "dtype": "int64"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 216227, "num_examples": 210}], "download_size": 0, "dataset_size": 216227}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
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2023-08-29T07:05:53+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "flan2021_filtered_3pertask"
More Information needed
|
[
"# Dataset Card for \"flan2021_filtered_3pertask\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"flan2021_filtered_3pertask\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"flan2021_filtered_3pertask\"\n\nMore Information needed"
] |
357bdcdf853b1c5038ba737a5e8e7e25f5280d74
|
# Dataset Card for "fwv2_squad_num_train_10_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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tyzhu/fwv2_squad_num_train_10_eval_10
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[
"region:us"
] |
2023-08-29T07:04:16+00:00
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{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "train_doc2id", "path": "data/train_doc2id-*"}, {"split": "train_id2doc", "path": "data/train_id2doc-*"}, {"split": "train_find_word", "path": "data/train_find_word-*"}, {"split": "eval_find_word", "path": "data/eval_find_word-*"}, {"split": "id_context_mapping", "path": "data/id_context_mapping-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4388, "num_examples": 30}, {"name": "train_doc2id", "num_bytes": 3294, "num_examples": 20}, {"name": "train_id2doc", "num_bytes": 3354, "num_examples": 20}, {"name": "train_find_word", "num_bytes": 1034, "num_examples": 10}, {"name": "eval_find_word", "num_bytes": 1009, "num_examples": 10}, {"name": "id_context_mapping", "num_bytes": 2714, "num_examples": 20}], "download_size": 23053, "dataset_size": 15793}}
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2023-08-29T07:04:29+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "fwv2_squad_num_train_10_eval_10"
More Information needed
|
[
"# Dataset Card for \"fwv2_squad_num_train_10_eval_10\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"fwv2_squad_num_train_10_eval_10\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"fwv2_squad_num_train_10_eval_10\"\n\nMore Information needed"
] |
03f467c9a44bd641189e065ffa2218bb8b40bb25
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3_sd2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_eye_movements_gosdt_l512_d3_sd2
|
[
"region:us"
] |
2023-08-29T07:29:07+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2696882416, "dataset_size": 11949520000}}
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2023-08-29T07:32:12+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3_sd2"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd2\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd2\"\n\nMore Information needed"
] |
30647af3dc0c9d4ef39263f2ee318c33855b7a3e
|
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mohanraj/test
|
[
"region:us"
] |
2023-08-29T07:34:05+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 166037621.08240023, "num_examples": 235987}, {"name": "test", "num_bytes": 18448780.917599767, "num_examples": 26221}], "download_size": 67931456, "dataset_size": 184486402.0}}
|
2023-08-29T07:35:10+00:00
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[] |
[] |
TAGS
#region-us
|
# Dataset Card for "test"
More Information needed
|
[
"# Dataset Card for \"test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"test\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed"
] |
3ab9b4e18422e9fb4959c95e2d47e8a5d1bd3320
|
# Dataset Card for "zien-llama2-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qnquang/zien-llama2-test
|
[
"region:us"
] |
2023-08-29T07:36:45+00:00
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{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4319, "num_examples": 13}], "download_size": 4354, "dataset_size": 4319}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T07:36:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "zien-llama2-test"
More Information needed
|
[
"# Dataset Card for \"zien-llama2-test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"zien-llama2-test\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"zien-llama2-test\"\n\nMore Information needed"
] |
aaba09cfa199f1fc7cbeedc05d6ea20eccf934cc
|
# Dataset Card for Evaluation run of fangloveskari/Platypus_QLoRA_LLaMA_70b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_70b
- **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 [fangloveskari/Platypus_QLoRA_LLaMA_70b](https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_70b) 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 agregated 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_fangloveskari__Platypus_QLoRA_LLaMA_70b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T21:04:30.246280](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__Platypus_QLoRA_LLaMA_70b/blob/main/results_2023-09-17T21-04-30.246280.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": {
"em": 0.3960780201342282,
"em_stderr": 0.005008647185447735,
"f1": 0.5245239093959767,
"f1_stderr": 0.00450887492882971,
"acc": 0.5682691139696489,
"acc_stderr": 0.011651409152443089
},
"harness|drop|3": {
"em": 0.3960780201342282,
"em_stderr": 0.005008647185447735,
"f1": 0.5245239093959767,
"f1_stderr": 0.00450887492882971
},
"harness|gsm8k|5": {
"acc": 0.3078089461713419,
"acc_stderr": 0.012714401009923652
},
"harness|winogrande|5": {
"acc": 0.8287292817679558,
"acc_stderr": 0.010588417294962526
}
}
```
### 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_fangloveskari__Platypus_QLoRA_LLaMA_70b
|
[
"region:us"
] |
2023-08-29T07:46:19+00:00
|
{"pretty_name": "Evaluation run of fangloveskari/Platypus_QLoRA_LLaMA_70b", "dataset_summary": "Dataset automatically created during the evaluation run of model [fangloveskari/Platypus_QLoRA_LLaMA_70b](https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_70b) 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 agregated 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_fangloveskari__Platypus_QLoRA_LLaMA_70b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T21:04:30.246280](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__Platypus_QLoRA_LLaMA_70b/blob/main/results_2023-09-17T21-04-30.246280.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 \"em\": 0.3960780201342282,\n \"em_stderr\": 0.005008647185447735,\n \"f1\": 0.5245239093959767,\n \"f1_stderr\": 0.00450887492882971,\n \"acc\": 0.5682691139696489,\n \"acc_stderr\": 0.011651409152443089\n },\n \"harness|drop|3\": {\n \"em\": 0.3960780201342282,\n \"em_stderr\": 0.005008647185447735,\n \"f1\": 0.5245239093959767,\n \"f1_stderr\": 0.00450887492882971\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.3078089461713419,\n \"acc_stderr\": 0.012714401009923652\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8287292817679558,\n \"acc_stderr\": 0.010588417294962526\n }\n}\n```", "repo_url": "https://huggingface.co/fangloveskari/Platypus_QLoRA_LLaMA_70b", "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_08_29T08_45_40.863548", "path": ["**/details_harness|arc:challenge|25_2023-08-29T08:45:40.863548.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T08:45:40.863548.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_17T21_04_30.246280", "path": ["**/details_harness|drop|3_2023-09-17T21-04-30.246280.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T21-04-30.246280.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T21_04_30.246280", "path": ["**/details_harness|gsm8k|5_2023-09-17T21-04-30.246280.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T21-04-30.246280.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T08_45_40.863548", "path": ["**/details_harness|hellaswag|10_2023-08-29T08:45:40.863548.parquet"]}, {"split": "latest", "path": 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|
2023-09-17T20:04:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of fangloveskari/Platypus_QLoRA_LLaMA_70b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model fangloveskari/Platypus_QLoRA_LLaMA_70b 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 agregated 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 2023-09-17T21:04:30.246280(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 fangloveskari/Platypus_QLoRA_LLaMA_70b",
"## 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 fangloveskari/Platypus_QLoRA_LLaMA_70b 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 agregated 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 2023-09-17T21:04:30.246280(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 fangloveskari/Platypus_QLoRA_LLaMA_70b",
"## 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 fangloveskari/Platypus_QLoRA_LLaMA_70b 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 agregated 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 2023-09-17T21:04:30.246280(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 fangloveskari/Platypus_QLoRA_LLaMA_70b## 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 fangloveskari/Platypus_QLoRA_LLaMA_70b 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 agregated 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 2023-09-17T21:04:30.246280(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"
] |
1b65443c2ce3ac6adc3e3d673f299fae3e162f9e
|
# Dataset Card for Evaluation run of fangloveskari/ORCA_LLaMA_70B_QLoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA
- **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 [fangloveskari/ORCA_LLaMA_70B_QLoRA](https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA) 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 agregated 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_fangloveskari__ORCA_LLaMA_70B_QLoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T16:47:31.229796](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__ORCA_LLaMA_70B_QLoRA/blob/main/results_2023-09-23T16-47-31.229796.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": {
"em": 0.3109270134228188,
"em_stderr": 0.004740252668251192,
"f1": 0.47044567953020594,
"f1_stderr": 0.004325159736671571,
"acc": 0.5600850420632693,
"acc_stderr": 0.011402883443890944
},
"harness|drop|3": {
"em": 0.3109270134228188,
"em_stderr": 0.004740252668251192,
"f1": 0.47044567953020594,
"f1_stderr": 0.004325159736671571
},
"harness|gsm8k|5": {
"acc": 0.2835481425322214,
"acc_stderr": 0.012415070917508125
},
"harness|winogrande|5": {
"acc": 0.8366219415943172,
"acc_stderr": 0.010390695970273764
}
}
```
### 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_fangloveskari__ORCA_LLaMA_70B_QLoRA
|
[
"region:us"
] |
2023-08-29T07:51:40+00:00
|
{"pretty_name": "Evaluation run of fangloveskari/ORCA_LLaMA_70B_QLoRA", "dataset_summary": "Dataset automatically created during the evaluation run of model [fangloveskari/ORCA_LLaMA_70B_QLoRA](https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA) 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 agregated 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_fangloveskari__ORCA_LLaMA_70B_QLoRA\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-23T16:47:31.229796](https://huggingface.co/datasets/open-llm-leaderboard/details_fangloveskari__ORCA_LLaMA_70B_QLoRA/blob/main/results_2023-09-23T16-47-31.229796.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 \"em\": 0.3109270134228188,\n \"em_stderr\": 0.004740252668251192,\n \"f1\": 0.47044567953020594,\n \"f1_stderr\": 0.004325159736671571,\n \"acc\": 0.5600850420632693,\n \"acc_stderr\": 0.011402883443890944\n },\n \"harness|drop|3\": {\n \"em\": 0.3109270134228188,\n \"em_stderr\": 0.004740252668251192,\n \"f1\": 0.47044567953020594,\n \"f1_stderr\": 0.004325159736671571\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2835481425322214,\n \"acc_stderr\": 0.012415070917508125\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8366219415943172,\n \"acc_stderr\": 0.010390695970273764\n }\n}\n```", "repo_url": "https://huggingface.co/fangloveskari/ORCA_LLaMA_70B_QLoRA", "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_08_29T08_51_06.198415", "path": ["**/details_harness|arc:challenge|25_2023-08-29T08:51:06.198415.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T08:51:06.198415.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_23T16_47_31.229796", "path": ["**/details_harness|drop|3_2023-09-23T16-47-31.229796.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-23T16-47-31.229796.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_23T16_47_31.229796", "path": ["**/details_harness|gsm8k|5_2023-09-23T16-47-31.229796.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-23T16-47-31.229796.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T08_51_06.198415", "path": ["**/details_harness|hellaswag|10_2023-08-29T08:51:06.198415.parquet"]}, {"split": "latest", "path": 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["**/details_harness|truthfulqa:mc|0_2023-08-29T08:51:06.198415.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T08:51:06.198415.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_23T16_47_31.229796", "path": ["**/details_harness|winogrande|5_2023-09-23T16-47-31.229796.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-23T16-47-31.229796.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T08_51_06.198415", "path": ["results_2023-08-29T08:51:06.198415.parquet"]}, {"split": "2023_09_23T16_47_31.229796", "path": ["results_2023-09-23T16-47-31.229796.parquet"]}, {"split": "latest", "path": ["results_2023-09-23T16-47-31.229796.parquet"]}]}]}
|
2023-09-23T15:47:43+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of fangloveskari/ORCA_LLaMA_70B_QLoRA
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model fangloveskari/ORCA_LLaMA_70B_QLoRA 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 agregated 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 2023-09-23T16:47:31.229796(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 fangloveskari/ORCA_LLaMA_70B_QLoRA",
"## 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 fangloveskari/ORCA_LLaMA_70B_QLoRA 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 agregated 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 2023-09-23T16:47:31.229796(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 Card for Evaluation run of fangloveskari/ORCA_LLaMA_70B_QLoRA",
"## 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 fangloveskari/ORCA_LLaMA_70B_QLoRA 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 agregated 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 2023-09-23T16:47:31.229796(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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"#### 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 fangloveskari/ORCA_LLaMA_70B_QLoRA## 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 fangloveskari/ORCA_LLaMA_70B_QLoRA 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 agregated 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 2023-09-23T16:47:31.229796(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"
] |
d23c9325e0411efce3dfae266b12b89a25acc1f3
|
# Dataset Card for Evaluation run of migtissera/Synthia-70B-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/Synthia-70B-v1.1
- **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/Synthia-70B-v1.1](https://huggingface.co/migtissera/Synthia-70B-v1.1) 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 agregated 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__Synthia-70B-v1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T19:08:11.059191](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.1/blob/main/results_2023-09-23T19-08-11.059191.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": {
"em": 0.33326342281879195,
"em_stderr": 0.004827370333271099,
"f1": 0.39018036912751786,
"f1_stderr": 0.004711418943333287,
"acc": 0.5775224946788872,
"acc_stderr": 0.011611460846674582
},
"harness|drop|3": {
"em": 0.33326342281879195,
"em_stderr": 0.004827370333271099,
"f1": 0.39018036912751786,
"f1_stderr": 0.004711418943333287
},
"harness|gsm8k|5": {
"acc": 0.31842304776345715,
"acc_stderr": 0.012832225723075403
},
"harness|winogrande|5": {
"acc": 0.8366219415943172,
"acc_stderr": 0.010390695970273763
}
}
```
### 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__Synthia-70B-v1.1
|
[
"region:us"
] |
2023-08-29T07:55:39+00:00
|
{"pretty_name": "Evaluation run of migtissera/Synthia-70B-v1.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/Synthia-70B-v1.1](https://huggingface.co/migtissera/Synthia-70B-v1.1) 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 agregated 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__Synthia-70B-v1.1\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-23T19:08:11.059191](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Synthia-70B-v1.1/blob/main/results_2023-09-23T19-08-11.059191.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 \"em\": 0.33326342281879195,\n \"em_stderr\": 0.004827370333271099,\n \"f1\": 0.39018036912751786,\n \"f1_stderr\": 0.004711418943333287,\n \"acc\": 0.5775224946788872,\n \"acc_stderr\": 0.011611460846674582\n },\n \"harness|drop|3\": {\n \"em\": 0.33326342281879195,\n \"em_stderr\": 0.004827370333271099,\n \"f1\": 0.39018036912751786,\n \"f1_stderr\": 0.004711418943333287\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.31842304776345715,\n \"acc_stderr\": 0.012832225723075403\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8366219415943172,\n \"acc_stderr\": 0.010390695970273763\n }\n}\n```", "repo_url": "https://huggingface.co/migtissera/Synthia-70B-v1.1", "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_08_29T08_55_05.432450", "path": ["**/details_harness|arc:challenge|25_2023-08-29T08:55:05.432450.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T08:55:05.432450.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_23T19_08_11.059191", "path": ["**/details_harness|drop|3_2023-09-23T19-08-11.059191.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-23T19-08-11.059191.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_23T19_08_11.059191", "path": ["**/details_harness|gsm8k|5_2023-09-23T19-08-11.059191.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-23T19-08-11.059191.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T08_55_05.432450", "path": ["**/details_harness|hellaswag|10_2023-08-29T08:55:05.432450.parquet"]}, {"split": "latest", "path": 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|
2023-09-23T18:08:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of migtissera/Synthia-70B-v1.1
## 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/Synthia-70B-v1.1 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 agregated 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 2023-09-23T19:08:11.059191(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 Card for Evaluation run of migtissera/Synthia-70B-v1.1",
"## 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/Synthia-70B-v1.1 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 agregated 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 2023-09-23T19:08:11.059191(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",
"### 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 migtissera/Synthia-70B-v1.1 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 agregated 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 2023-09-23T19:08:11.059191(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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"#### 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 migtissera/Synthia-70B-v1.1## 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/Synthia-70B-v1.1 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 agregated 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 2023-09-23T19:08:11.059191(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"
] |
921defdd6f8ae5271f2d4925150c48dba0cb3199
|
# JAT Dataset
## Dataset Description
The Jack of All Trades (JAT) dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The JAT dataset is part of the JAT project, which aims to build a multimodal generalist agent.
**Paper**: https://huggingface.co/papers/2402.09844
### Usage
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("jat-project/jat-dataset", "metaworld-assembly")
>>> first_episode = dataset["train"][0]
>>> first_episode.keys()
dict_keys(['continuous_observations', 'continuous_actions', 'rewards'])
>>> len(first_episode["rewards"])
500
>>> first_episode["continuous_actions"][0]
[6.459120273590088, 2.2422609329223633, -5.914587020874023, -19.799840927124023]
```
## Dataset Structure
### Data Instances
<details>
<summary>Click to expand the score information for each task</summary>
The following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.
| Task | Random Agent Score | Dataset Episode Score |
| ----------------------------------- | :-----------------: | :-------------------: |
| **Atari** | | |
| atari-alien | 205.50 ± 111.97 | 16912.50 ± 7087.42 |
| atari-amidar | 2.38 ± 2.50 | 2164.71 ± 1229.47 |
| atari-assault | 262.50 ± 89.61 | 15699.12 ± 9572.12 |
| atari-asterix | 213.50 ± 110.87 | 3699.62 ± 2421.30 |
| atari-asteroids | 856.40 ± 434.32 | 177011.05 ± 35334.20 |
| atari-atlantis | 17764.00 ± 6662.43 | 320679.59 ± 418247.37 |
| atari-bankheist | 13.40 ± 11.07 | 1322.43 ± 60.84 |
| atari-battlezone | 2170.00 ± 2121.58 | 295592.59 ± 161960.96 |
| atari-beamrider | 357.28 ± 143.97 | 29589.35 ± 16132.96 |
| atari-berzerk | 160.10 ± 118.87 | 57085.26 ± 13104.53 |
| atari-bowling | 23.81 ± 6.07 | 20.40 ± 7.29 |
| atari-boxing | 0.52 ± 4.37 | 97.97 ± 3.77 |
| atari-breakout | 1.24 ± 1.30 | 702.97 ± 203.62 |
| atari-centipede | 2150.06 ± 1113.28 | 11624.29 ± 4918.34 |
| atari-choppercommand | 875.00 ± 416.98 | 90990.62 ± 270876.93 |
| atari-crazyclimber | 7376.00 ± 2253.09 | 179296.94 ± 39862.06 |
| atari-defender | 3417.50 ± 1443.41 | 351958.33 ± 40466.82 |
| atari-demonattack | 165.55 ± 92.93 | 92195.25 ± 26174.79 |
| atari-doubledunk | -18.54 ± 3.07 | 20.94 ± 3.65 |
| atari-enduro | 0.00 ± 0.00 | 2292.22 ± 147.54 |
| atari-fishingderby | -93.90 ± 3.51 | 7.18 ± 25.06 |
| atari-freeway | 0.01 ± 0.10 | 33.88 ± 0.35 |
| atari-frostbite | 67.60 ± 37.61 | 13196.12 ± 4341.00 |
| atari-gopher | 319.40 ± 228.24 | 81676.15 ± 46329.48 |
| atari-gravitar | 188.50 ± 203.33 | 3986.57 ± 1729.05 |
| atari-hero | 475.25 ± 894.95 | 44677.35 ± 1754.42 |
| atari-icehockey | -9.83 ± 3.24 | 25.17 ± 5.79 |
| atari-jamesbond | 28.50 ± 45.42 | 27786.89 ± 33819.20 |
| atari-kangaroo | 52.00 ± 108.15 | 574.05 ± 636.94 |
| atari-krull | 1754.00 ± 583.56 | 11439.83 ± 1218.34 |
| atari-kungfumaster | 390.00 ± 359.03 | 32392.81 ± 10006.55 |
| atari-montezumarevenge | 0.00 ± 0.00 | 393.53 ± 50.45 |
| atari-mspacman | 246.40 ± 121.22 | 6896.08 ± 2031.99 |
| atari-namethisgame | 2447.40 ± 888.97 | 22991.18 ± 2473.15 |
| atari-phoenix | 776.80 ± 635.86 | 424583.16 ± 97649.17 |
| atari-pitfall | -259.75 ± 384.26 | -1.45 ± 4.50 |
| atari-pong | -20.22 ± 0.95 | 20.99 ± 0.18 |
| atari-privateeye | 41.65 ± 191.83 | 100.00 ± 0.00 |
| atari-qbert | 164.25 ± 151.79 | 42971.37 ± 85070.72 |
| atari-riverraid | 1474.40 ± 314.59 | 14800.94 ± 7924.56 |
| atari-roadrunner | 11.00 ± 42.18 | 77942.80 ± 6088.62 |
| atari-robotank | 1.87 ± 1.59 | 80.51 ± 13.28 |
| atari-seaquest | 73.20 ± 57.91 | 2597.34 ± 386.09 |
| atari-skiing | -16299.52 ± 1850.70 | -10738.06 ± 111.13 |
| atari-solaris | 2360.40 ± 1852.03 | 1353.68 ± 516.96 |
| atari-spaceinvaders | 137.20 ± 95.82 | 29425.29 ± 23623.89 |
| atari-stargunner | 652.00 ± 312.24 | 360588.57 ± 49207.71 |
| atari-surround | -9.99 ± 0.10 | 9.39 ± 0.85 |
| atari-tennis | -23.95 ± 0.22 | 11.11 ± 7.57 |
| atari-timepilot | 3396.00 ± 2128.85 | 69583.33 ± 29838.67 |
| atari-tutankham | 12.73 ± 17.40 | 291.16 ± 30.37 |
| atari-upndown | 358.90 ± 380.11 | 429418.33 ± 7187.43 |
| atari-venture | 0.00 ± 0.00 | 0.00 ± 0.00 |
| atari-videopinball | 23917.17 ± 19449.59 | 441507.92 ± 283264.62 |
| atari-wizardofwor | 620.00 ± 837.85 | 49333.33 ± 16157.08 |
| atari-yarsrevenge | 3503.91 ± 906.14 | 270262.86 ± 161815.96 |
| atari-zaxxon | 21.00 ± 102.27 | 73097.22 ± 14825.77 |
| **BabyAI** | | |
| babyai-action-obj-door | 0.37 ± 0.39 | 0.99 ± 0.01 |
| babyai-blocked-unlock-pickup | 0.00 ± 0.02 | 0.95 ± 0.01 |
| babyai-boss-level | 0.06 ± 0.21 | 0.94 ± 0.05 |
| babyai-boss-level-no-unlock | 0.06 ± 0.19 | 0.94 ± 0.05 |
| babyai-find-obj-s5 | 0.08 ± 0.23 | 0.95 ± 0.04 |
| babyai-go-to | 0.13 ± 0.29 | 0.92 ± 0.07 |
| babyai-go-to-door | 0.45 ± 0.38 | 0.99 ± 0.00 |
| babyai-go-to-imp-unlock | 0.08 ± 0.23 | 0.83 ± 0.13 |
| babyai-go-to-local | 0.16 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-obj | 0.13 ± 0.27 | 0.93 ± 0.03 |
| babyai-go-to-obj-door | 0.53 ± 0.39 | 0.99 ± 0.01 |
| babyai-go-to-red-ball | 0.17 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-red-ball-grey | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-red-ball-no-dists | 0.14 ± 0.28 | 0.93 ± 0.03 |
| babyai-go-to-red-blue-ball | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-seq | 0.08 ± 0.23 | 0.94 ± 0.05 |
| babyai-key-corridor | 0.00 ± 0.00 | 0.91 ± 0.01 |
| babyai-mini-boss-level | 0.07 ± 0.21 | 0.89 ± 0.10 |
| babyai-move-two-across-s8n9 | 0.00 ± 0.00 | 0.96 ± 0.01 |
| babyai-one-room-s8 | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open | 0.10 ± 0.24 | 0.95 ± 0.05 |
| babyai-open-door | 0.23 ± 0.34 | 0.99 ± 0.00 |
| babyai-open-doors-order-n4 | 0.16 ± 0.30 | 0.99 ± 0.01 |
| babyai-open-red-door | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open-two-doors | 0.08 ± 0.20 | 0.98 ± 0.00 |
| babyai-pickup | 0.08 ± 0.22 | 0.92 ± 0.07 |
| babyai-pickup-above | 0.02 ± 0.09 | 0.91 ± 0.07 |
| babyai-pickup-dist | 0.10 ± 0.24 | 0.86 ± 0.21 |
| babyai-pickup-loc | 0.08 ± 0.23 | 0.91 ± 0.04 |
| babyai-put-next | 0.00 ± 0.03 | 0.96 ± 0.01 |
| babyai-put-next-local | 0.00 ± 0.05 | 0.92 ± 0.03 |
| babyai-synth | 0.11 ± 0.26 | 0.93 ± 0.06 |
| babyai-synth-loc | 0.13 ± 0.29 | 0.94 ± 0.06 |
| babyai-synth-seq | 0.07 ± 0.20 | 0.95 ± 0.04 |
| babyai-unblock-pickup | 0.08 ± 0.22 | 0.91 ± 0.08 |
| babyai-unlock | 0.03 ± 0.15 | 0.87 ± 0.10 |
| babyai-unlock-local | 0.01 ± 0.09 | 0.98 ± 0.01 |
| babyai-unlock-pickup | 0.00 ± 0.00 | 0.75 ± 0.04 |
| babyai-unlock-to-unlock | 0.00 ± 0.00 | 0.96 ± 0.00 |
| **Meta-World** | | |
| metaworld-assembly | 45.30 ± 4.13 | 245.99 ± 3.50 |
| metaworld-basketball | 2.81 ± 1.24 | 627.99 ± 1.98 |
| metaworld-bin-picking | 1.89 ± 0.45 | 425.58 ± 101.86 |
| metaworld-box-close | 76.39 ± 17.91 | 512.49 ± 107.81 |
| metaworld-button-press | 31.73 ± 5.20 | 643.10 ± 12.85 |
| metaworld-button-press-topdown | 28.97 ± 10.37 | 490.18 ± 27.21 |
| metaworld-button-press-topdown-wall | 29.04 ± 10.52 | 497.19 ± 31.37 |
| metaworld-button-press-wall | 8.98 ± 3.99 | 675.41 ± 15.04 |
| metaworld-coffee-button | 31.72 ± 6.36 | 731.08 ± 29.34 |
| metaworld-coffee-pull | 4.09 ± 0.38 | 259.86 ± 88.48 |
| metaworld-coffee-push | 4.17 ± 0.76 | 496.78 ± 118.20 |
| metaworld-dial-turn | 29.64 ± 16.67 | 793.56 ± 80.06 |
| metaworld-disassemble | 40.31 ± 7.53 | 42.83 ± 6.30 |
| metaworld-door-close | 5.30 ± 1.33 | 529.75 ± 27.24 |
| metaworld-door-lock | 112.35 ± 28.63 | 811.52 ± 34.07 |
| metaworld-door-open | 56.37 ± 11.23 | 581.94 ± 19.67 |
| metaworld-door-unlock | 94.17 ± 15.56 | 802.88 ± 17.05 |
| metaworld-drawer-close | 116.73 ± 253.11 | 867.92 ± 4.48 |
| metaworld-drawer-open | 126.85 ± 25.22 | 492.99 ± 2.52 |
| metaworld-faucet-close | 253.12 ± 22.94 | 753.92 ± 13.42 |
| metaworld-faucet-open | 244.10 ± 23.25 | 705.76 ± 7.15 |
| metaworld-hammer | 95.33 ± 9.02 | 693.17 ± 34.62 |
| metaworld-hand-insert | 2.75 ± 3.53 | 740.53 ± 36.69 |
| metaworld-handle-press | 80.41 ± 110.19 | 855.91 ± 72.75 |
| metaworld-handle-press-side | 57.00 ± 39.47 | 861.12 ± 20.01 |
| metaworld-handle-pull | 10.34 ± 13.54 | 669.35 ± 24.81 |
| metaworld-handle-pull-side | 2.13 ± 2.76 | 384.65 ± 102.89 |
| metaworld-lever-pull | 60.31 ± 15.77 | 612.04 ± 38.85 |
| metaworld-peg-insert-side | 1.71 ± 0.36 | 315.23 ± 140.07 |
| metaworld-peg-unplug-side | 4.75 ± 2.83 | 456.12 ± 81.65 |
| metaworld-pick-out-of-hole | 1.51 ± 0.24 | 219.61 ± 88.85 |
| metaworld-pick-place | 1.61 ± 0.99 | 419.10 ± 98.19 |
| metaworld-pick-place-wall | 0.00 ± 0.01 | 450.57 ± 64.10 |
| metaworld-plate-slide | 74.64 ± 13.84 | 527.01 ± 155.34 |
| metaworld-plate-slide-back | 33.47 ± 11.22 | 718.22 ± 87.41 |
| metaworld-plate-slide-back-side | 34.34 ± 11.53 | 729.61 ± 69.15 |
| metaworld-plate-slide-side | 22.61 ± 17.36 | 662.81 ± 102.81 |
| metaworld-push | 5.51 ± 2.43 | 750.57 ± 43.98 |
| metaworld-push-back | 1.21 ± 0.16 | 85.05 ± 107.12 |
| metaworld-push-wall | 6.13 ± 3.17 | 748.87 ± 10.62 |
| metaworld-reach | 149.67 ± 44.70 | 681.37 ± 133.68 |
| metaworld-reach-wall | 143.26 ± 36.56 | 746.12 ± 104.19 |
| metaworld-shelf-place | 0.00 ± 0.01 | 241.34 ± 24.60 |
| metaworld-soccer | 5.66 ± 4.61 | 375.15 ± 140.24 |
| metaworld-stick-pull | 2.64 ± 1.41 | 523.55 ± 18.94 |
| metaworld-stick-push | 2.81 ± 1.04 | 627.95 ± 10.20 |
| metaworld-sweep | 11.23 ± 7.28 | 494.85 ± 43.29 |
| metaworld-sweep-into | 12.55 ± 10.72 | 799.21 ± 19.07 |
| metaworld-window-close | 57.46 ± 7.11 | 591.30 ± 38.63 |
| metaworld-window-open | 43.36 ± 2.09 | 590.82 ± 57.08 |
| **MuJoCo** | | |
| mujoco-ant | -59.95 ± 99.62 | 5846.42 ± 942.55 |
| mujoco-doublependulum | 57.46 ± 17.54 | 9338.69 ± 352.61 |
| mujoco-halfcheetah | -284.97 ± 79.83 | 7437.77 ± 173.30 |
| mujoco-hopper | 18.38 ± 17.09 | 1858.73 ± 534.07 |
| mujoco-humanoid | 122.02 ± 35.28 | 6281.02 ± 1795.84 |
| mujoco-pendulum | 6.07 ± 3.47 | 475.40 ± 178.96 |
| mujoco-pusher | -149.69 ± 7.41 | -25.21 ± 6.66 |
| mujoco-reacher | -43.00 ± 3.91 | -5.68 ± 2.53 |
| mujoco-standup | 33135.75 ± 2481.89 | 273574.16 ± 85253.26 |
| mujoco-swimmer | 0.80 ± 10.71 | 92.18 ± 4.44 |
| mujoco-walker | 2.68 ± 6.06 | 4631.22 ± 1059.01 |
</details>
### Data Fields
- `text`: a `string` feature
- `images`: a `image` feature
- `image_observations` : a `Sequence(image)` feature
- `text_observations` : a `Sequence(string)` feature
- `discrete_observations`: a `Sequence(Sequence(int64))` feature
- `continuous_observations`: a `Sequence(Sequence(float32))` feature
- `continuous_actions`: a `Sequence(Sequence(float32))` feature
- `discrete_actions`: a `Sequence(int64)` feature
- `rewards`: a `Sequence(float32)` feature
### Data Splits
- `train`: `` examples
- `test`: `` examples
## Dataset Creation
This section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the [JAT repository](https://github.com/huggingface/jat). For RL tasks, we trained one agent per task using the [Sample Factory](https://www.samplefactory.dev). Then we used the trained agent to generate episodes.
### Atari
We used the 57 [ALE/Atari](https://github.com/Farama-Foundation/Arcade-Learning-Environment) games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.
### BabyAI
We used BabyAI's implementation from [Minigrid](https://github.com/Farama-Foundation/Minigrid).
We reused the [bot agent](https://github.com/mila-iqia/babyai) provided with BabyAI's paper and adapted it to the new Minigrid API.
Using the bot, we generated 1.000.000 interractions for each of the 39 tasks of [Minigrid's BabyAI](https://minigrid.farama.org/environments/babyai/) and stored for each step:
- the mission: str
- the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)
- the action: integer
- the reward: float
### Conceptual Captions
The [Conceptual Captions](https://github.com/google-research-datasets/conceptual-captions/tree/master) dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.
### Meta-World
We used the 50 tasks from [Meta-World v2](https://github.com/Farama-Foundation/Metaworld). We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.
### MuJoCo
We used the 11 environments of Gymnasium MuJoCo.
### OK-VQA
The [OK-VQA](https://okvqa.allenai.org/index.html) dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.
The data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.
### OSCAR
We modified the "unshuffled_deduplicated_en" split of [OSCAR 2019](https://huggingface.co/datasets/oscar) dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under [CC BY 4.0](https://oscar-project.github.io/documentation/versions/oscar-2019/#license).
We cleaned and deduplicated the dataset using [the methods](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/01b_oscar_cleaning_and_filtering) and parameters used for the [ROOTS dataset](https://arxiv.org/abs/2303.03915) (Lurençon et al., 2023).
The dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.
### Wikipedia
We used the english version of the [Wikipedia dataset](https://huggingface.co/datasets/wikipedia).
## Considerations for Using the Data
### Known Issues
- Some BabyAI tasks are missing due to incompatibility with the training bot:
- `babyai-key-in-box`
- `babyai-go-to-imp-unlock`
- `babyai-unlock-to-unlock`
- `babyai-unlock`
- For some atari tasks, the episode is too long, causing an `OverflowError` when loading the dataset:
- `atari-enduro`
- For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:
- `atari-bowling`
- `atari-privateeye`
- `atari-solaris`
- `atari-venture`
- `metaworld-bin-picking`
- `metaworld-disassemble`
- `metaworld-peg-insert-side`
- `metaworld-plate-slide`
- `metaworld-push-back`
### Future Developments
We plan to expand the dataset to include the following additional domains:
- [ ] DM Lab
- [ ] Sokoban
- [ ] Procgen
- [ ] DM Control Suite (w and w/o pixels)
## Additional Information
### Licensing Information
This dataset is release under the Apache 2.0 license.
### Citation Information
```bibtex
@article{gallouedec2024jack,
title = {{Jack of All Trades, Master of Some: a Multi-Purpose Transformer Agent}},
author = {Gallouédec, Quentin and Beeching, Edward and Romac, Clément and Dellandréa, Emmanuel},
journal = {arXiv preprint arXiv:2402.09844},
year = {2024},
url = {https://arxiv.org/abs/2402.09844}
}
```
## Acknowledgment
We would like to extend our sincere gratitude to:
- [Shengyi Costa Huang](https://huggingface.co/vwxyzjn) for his invaluable assistance with the pretrained models used in this research
|
jat-project/jat-dataset
|
[
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
"imitation-learning",
"reinforcement-learning",
"text-generation",
"question-answering",
"generalist-agent",
"arxiv:2402.09844",
"arxiv:2303.03915",
"region:us"
] |
2023-08-29T08:03:24+00:00
|
{"annotations_creators": ["found", "machine-generated"], "license": "apache-2.0", "source_datasets": ["conceptual-captions", "ok-vqa", "oscar"], "task_categories": ["reinforcement-learning", "text-generation", "question-answering"], "pretty_name": "JAT-dataset", "configs": [{"config_name": "atari-alien", "data_files": [{"split": "train", "path": "atari-alien/train-*"}, {"split": "test", "path": "atari-alien/test-*"}]}, {"config_name": "atari-amidar", "data_files": [{"split": "train", "path": "atari-amidar/train-*"}, {"split": "test", "path": "atari-amidar/test-*"}]}, {"config_name": "atari-assault", "data_files": [{"split": "train", "path": "atari-assault/train-*"}, {"split": "test", "path": "atari-assault/test-*"}]}, {"config_name": "atari-asterix", "data_files": [{"split": "train", "path": "atari-asterix/train-*"}, {"split": "test", "path": "atari-asterix/test-*"}]}, {"config_name": "atari-asteroids", "data_files": [{"split": "train", "path": "atari-asteroids/train-*"}, {"split": "test", "path": "atari-asteroids/test-*"}]}, {"config_name": "atari-atlantis", "data_files": [{"split": "train", "path": "atari-atlantis/train-*"}, {"split": "test", "path": "atari-atlantis/test-*"}]}, {"config_name": "atari-bankheist", "data_files": [{"split": "train", "path": "atari-bankheist/train-*"}, {"split": "test", "path": "atari-bankheist/test-*"}]}, {"config_name": "atari-battlezone", "data_files": [{"split": "train", "path": "atari-battlezone/train-*"}, {"split": "test", "path": "atari-battlezone/test-*"}]}, {"config_name": "atari-beamrider", "data_files": [{"split": "train", "path": "atari-beamrider/train-*"}, {"split": "test", "path": "atari-beamrider/test-*"}]}, {"config_name": "atari-berzerk", "data_files": [{"split": "train", "path": "atari-berzerk/train-*"}, {"split": "test", "path": "atari-berzerk/test-*"}]}, {"config_name": "atari-bowling", "data_files": [{"split": "train", "path": "atari-bowling/train-*"}, {"split": "test", "path": "atari-bowling/test-*"}]}, {"config_name": "atari-boxing", "data_files": [{"split": "train", "path": "atari-boxing/train-*"}, {"split": "test", "path": "atari-boxing/test-*"}]}, {"config_name": "atari-breakout", "data_files": [{"split": "train", "path": "atari-breakout/train-*"}, {"split": "test", "path": "atari-breakout/test-*"}]}, {"config_name": "atari-centipede", "data_files": [{"split": "train", "path": "atari-centipede/train-*"}, {"split": "test", "path": "atari-centipede/test-*"}]}, {"config_name": "atari-choppercommand", "data_files": [{"split": "train", "path": "atari-choppercommand/train-*"}, {"split": "test", "path": "atari-choppercommand/test-*"}]}, {"config_name": "atari-crazyclimber", "data_files": [{"split": "train", "path": "atari-crazyclimber/train-*"}, {"split": "test", "path": "atari-crazyclimber/test-*"}]}, {"config_name": "atari-defender", "data_files": [{"split": "train", "path": "atari-defender/train-*"}, {"split": "test", "path": "atari-defender/test-*"}]}, {"config_name": "atari-demonattack", "data_files": [{"split": "train", "path": "atari-demonattack/train-*"}, {"split": "test", "path": "atari-demonattack/test-*"}]}, {"config_name": "atari-doubledunk", "data_files": [{"split": "test", "path": "atari-doubledunk/test-*"}, {"split": "train", "path": "atari-doubledunk/train-*"}]}, {"config_name": "atari-enduro", "data_files": [{"split": "train", "path": "atari-enduro/train-*"}, {"split": "test", "path": "atari-enduro/test-*"}]}, {"config_name": "atari-fishingderby", "data_files": [{"split": "train", "path": "atari-fishingderby/train-*"}, {"split": "test", "path": "atari-fishingderby/test-*"}]}, {"config_name": "atari-freeway", "data_files": [{"split": "train", "path": "atari-freeway/train-*"}, {"split": "test", "path": "atari-freeway/test-*"}]}, {"config_name": "atari-frostbite", "data_files": [{"split": "train", "path": "atari-frostbite/train-*"}, {"split": "test", "path": "atari-frostbite/test-*"}]}, {"config_name": "atari-gopher", "data_files": 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|
2024-02-16T13:52:52+00:00
|
[
"2402.09844",
"2303.03915"
] |
[] |
TAGS
#task_categories-reinforcement-learning #task_categories-text-generation #task_categories-question-answering #annotations_creators-found #annotations_creators-machine-generated #source_datasets-conceptual-captions #source_datasets-ok-vqa #source_datasets-oscar #license-apache-2.0 #imitation-learning #reinforcement-learning #text-generation #question-answering #generalist-agent #arxiv-2402.09844 #arxiv-2303.03915 #region-us
|
JAT Dataset
===========
Dataset Description
-------------------
The Jack of All Trades (JAT) dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The JAT dataset is part of the JAT project, which aims to build a multimodal generalist agent.
Paper: URL
### Usage
Dataset Structure
-----------------
### Data Instances
Click to expand the score information for each task
The following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.
### Data Fields
* 'text': a 'string' feature
* 'images': a 'image' feature
* 'image\_observations' : a 'Sequence(image)' feature
* 'text\_observations' : a 'Sequence(string)' feature
* 'discrete\_observations': a 'Sequence(Sequence(int64))' feature
* 'continuous\_observations': a 'Sequence(Sequence(float32))' feature
* 'continuous\_actions': a 'Sequence(Sequence(float32))' feature
* 'discrete\_actions': a 'Sequence(int64)' feature
* 'rewards': a 'Sequence(float32)' feature
### Data Splits
* 'train': '' examples
* 'test': '' examples
Dataset Creation
----------------
This section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the JAT repository. For RL tasks, we trained one agent per task using the Sample Factory. Then we used the trained agent to generate episodes.
### Atari
We used the 57 ALE/Atari games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.
### BabyAI
We used BabyAI's implementation from Minigrid.
We reused the bot agent provided with BabyAI's paper and adapted it to the new Minigrid API.
Using the bot, we generated 1.000.000 interractions for each of the 39 tasks of Minigrid's BabyAI and stored for each step:
* the mission: str
* the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)
* the action: integer
* the reward: float
### Conceptual Captions
The Conceptual Captions dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.
### Meta-World
We used the 50 tasks from Meta-World v2. We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.
### MuJoCo
We used the 11 environments of Gymnasium MuJoCo.
### OK-VQA
The OK-VQA dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.
The data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.
### OSCAR
We modified the "unshuffled\_deduplicated\_en" split of OSCAR 2019 dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under CC BY 4.0.
We cleaned and deduplicated the dataset using the methods and parameters used for the ROOTS dataset (Lurençon et al., 2023).
The dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.
### Wikipedia
We used the english version of the Wikipedia dataset.
Considerations for Using the Data
---------------------------------
### Known Issues
* Some BabyAI tasks are missing due to incompatibility with the training bot:
+ 'babyai-key-in-box'
+ 'babyai-go-to-imp-unlock'
+ 'babyai-unlock-to-unlock'
+ 'babyai-unlock'
* For some atari tasks, the episode is too long, causing an 'OverflowError' when loading the dataset:
+ 'atari-enduro'
* For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:
+ 'atari-bowling'
+ 'atari-privateeye'
+ 'atari-solaris'
+ 'atari-venture'
+ 'metaworld-bin-picking'
+ 'metaworld-disassemble'
+ 'metaworld-peg-insert-side'
+ 'metaworld-plate-slide'
+ 'metaworld-push-back'
### Future Developments
We plan to expand the dataset to include the following additional domains:
* [ ] DM Lab
* [ ] Sokoban
* [ ] Procgen
* [ ] DM Control Suite (w and w/o pixels)
Additional Information
----------------------
### Licensing Information
This dataset is release under the Apache 2.0 license.
Acknowledgment
--------------
We would like to extend our sincere gratitude to:
* Shengyi Costa Huang for his invaluable assistance with the pretrained models used in this research
|
[
"### Usage\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\n\nClick to expand the score information for each task\nThe following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.",
"### Data Fields\n\n\n* 'text': a 'string' feature\n* 'images': a 'image' feature\n* 'image\\_observations' : a 'Sequence(image)' feature\n* 'text\\_observations' : a 'Sequence(string)' feature\n* 'discrete\\_observations': a 'Sequence(Sequence(int64))' feature\n* 'continuous\\_observations': a 'Sequence(Sequence(float32))' feature\n* 'continuous\\_actions': a 'Sequence(Sequence(float32))' feature\n* 'discrete\\_actions': a 'Sequence(int64)' feature\n* 'rewards': a 'Sequence(float32)' feature",
"### Data Splits\n\n\n* 'train': '' examples\n* 'test': '' examples\n\n\nDataset Creation\n----------------\n\n\nThis section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the JAT repository. For RL tasks, we trained one agent per task using the Sample Factory. Then we used the trained agent to generate episodes.",
"### Atari\n\n\nWe used the 57 ALE/Atari games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.",
"### BabyAI\n\n\nWe used BabyAI's implementation from Minigrid.\nWe reused the bot agent provided with BabyAI's paper and adapted it to the new Minigrid API.\nUsing the bot, we generated 1.000.000 interractions for each of the 39 tasks of Minigrid's BabyAI and stored for each step:\n\n\n* the mission: str\n* the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)\n* the action: integer\n* the reward: float",
"### Conceptual Captions\n\n\nThe Conceptual Captions dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.",
"### Meta-World\n\n\nWe used the 50 tasks from Meta-World v2. We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.",
"### MuJoCo\n\n\nWe used the 11 environments of Gymnasium MuJoCo.",
"### OK-VQA\n\n\nThe OK-VQA dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.\nThe data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.",
"### OSCAR\n\n\nWe modified the \"unshuffled\\_deduplicated\\_en\" split of OSCAR 2019 dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under CC BY 4.0.\nWe cleaned and deduplicated the dataset using the methods and parameters used for the ROOTS dataset (Lurençon et al., 2023).\n\n\nThe dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.",
"### Wikipedia\n\n\nWe used the english version of the Wikipedia dataset.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Known Issues\n\n\n* Some BabyAI tasks are missing due to incompatibility with the training bot:\n\t+ 'babyai-key-in-box'\n\t+ 'babyai-go-to-imp-unlock'\n\t+ 'babyai-unlock-to-unlock'\n\t+ 'babyai-unlock'\n* For some atari tasks, the episode is too long, causing an 'OverflowError' when loading the dataset:\n\t+ 'atari-enduro'\n* For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:\n\t+ 'atari-bowling'\n\t+ 'atari-privateeye'\n\t+ 'atari-solaris'\n\t+ 'atari-venture'\n\t+ 'metaworld-bin-picking'\n\t+ 'metaworld-disassemble'\n\t+ 'metaworld-peg-insert-side'\n\t+ 'metaworld-plate-slide'\n\t+ 'metaworld-push-back'",
"### Future Developments\n\n\nWe plan to expand the dataset to include the following additional domains:\n\n\n* [ ] DM Lab\n* [ ] Sokoban\n* [ ] Procgen\n* [ ] DM Control Suite (w and w/o pixels)\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThis dataset is release under the Apache 2.0 license.\n\n\nAcknowledgment\n--------------\n\n\nWe would like to extend our sincere gratitude to:\n\n\n* Shengyi Costa Huang for his invaluable assistance with the pretrained models used in this research"
] |
[
"TAGS\n#task_categories-reinforcement-learning #task_categories-text-generation #task_categories-question-answering #annotations_creators-found #annotations_creators-machine-generated #source_datasets-conceptual-captions #source_datasets-ok-vqa #source_datasets-oscar #license-apache-2.0 #imitation-learning #reinforcement-learning #text-generation #question-answering #generalist-agent #arxiv-2402.09844 #arxiv-2303.03915 #region-us \n",
"### Usage\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\n\nClick to expand the score information for each task\nThe following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.",
"### Data Fields\n\n\n* 'text': a 'string' feature\n* 'images': a 'image' feature\n* 'image\\_observations' : a 'Sequence(image)' feature\n* 'text\\_observations' : a 'Sequence(string)' feature\n* 'discrete\\_observations': a 'Sequence(Sequence(int64))' feature\n* 'continuous\\_observations': a 'Sequence(Sequence(float32))' feature\n* 'continuous\\_actions': a 'Sequence(Sequence(float32))' feature\n* 'discrete\\_actions': a 'Sequence(int64)' feature\n* 'rewards': a 'Sequence(float32)' feature",
"### Data Splits\n\n\n* 'train': '' examples\n* 'test': '' examples\n\n\nDataset Creation\n----------------\n\n\nThis section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the JAT repository. For RL tasks, we trained one agent per task using the Sample Factory. Then we used the trained agent to generate episodes.",
"### Atari\n\n\nWe used the 57 ALE/Atari games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.",
"### BabyAI\n\n\nWe used BabyAI's implementation from Minigrid.\nWe reused the bot agent provided with BabyAI's paper and adapted it to the new Minigrid API.\nUsing the bot, we generated 1.000.000 interractions for each of the 39 tasks of Minigrid's BabyAI and stored for each step:\n\n\n* the mission: str\n* the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)\n* the action: integer\n* the reward: float",
"### Conceptual Captions\n\n\nThe Conceptual Captions dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.",
"### Meta-World\n\n\nWe used the 50 tasks from Meta-World v2. We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.",
"### MuJoCo\n\n\nWe used the 11 environments of Gymnasium MuJoCo.",
"### OK-VQA\n\n\nThe OK-VQA dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.\nThe data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.",
"### OSCAR\n\n\nWe modified the \"unshuffled\\_deduplicated\\_en\" split of OSCAR 2019 dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under CC BY 4.0.\nWe cleaned and deduplicated the dataset using the methods and parameters used for the ROOTS dataset (Lurençon et al., 2023).\n\n\nThe dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.",
"### Wikipedia\n\n\nWe used the english version of the Wikipedia dataset.\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Known Issues\n\n\n* Some BabyAI tasks are missing due to incompatibility with the training bot:\n\t+ 'babyai-key-in-box'\n\t+ 'babyai-go-to-imp-unlock'\n\t+ 'babyai-unlock-to-unlock'\n\t+ 'babyai-unlock'\n* For some atari tasks, the episode is too long, causing an 'OverflowError' when loading the dataset:\n\t+ 'atari-enduro'\n* For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:\n\t+ 'atari-bowling'\n\t+ 'atari-privateeye'\n\t+ 'atari-solaris'\n\t+ 'atari-venture'\n\t+ 'metaworld-bin-picking'\n\t+ 'metaworld-disassemble'\n\t+ 'metaworld-peg-insert-side'\n\t+ 'metaworld-plate-slide'\n\t+ 'metaworld-push-back'",
"### Future Developments\n\n\nWe plan to expand the dataset to include the following additional domains:\n\n\n* [ ] DM Lab\n* [ ] Sokoban\n* [ ] Procgen\n* [ ] DM Control Suite (w and w/o pixels)\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThis dataset is release under the Apache 2.0 license.\n\n\nAcknowledgment\n--------------\n\n\nWe would like to extend our sincere gratitude to:\n\n\n* Shengyi Costa Huang for his invaluable assistance with the pretrained models used in this research"
] |
[
149,
11,
57,
181,
98,
98,
119,
60,
43,
18,
67,
124,
24,
219,
58,
60
] |
[
"passage: TAGS\n#task_categories-reinforcement-learning #task_categories-text-generation #task_categories-question-answering #annotations_creators-found #annotations_creators-machine-generated #source_datasets-conceptual-captions #source_datasets-ok-vqa #source_datasets-oscar #license-apache-2.0 #imitation-learning #reinforcement-learning #text-generation #question-answering #generalist-agent #arxiv-2402.09844 #arxiv-2303.03915 #region-us \n### Usage\n\n\nDataset Structure\n-----------------### Data Instances\n\n\n\nClick to expand the score information for each task\nThe following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.### Data Fields\n\n\n* 'text': a 'string' feature\n* 'images': a 'image' feature\n* 'image\\_observations' : a 'Sequence(image)' feature\n* 'text\\_observations' : a 'Sequence(string)' feature\n* 'discrete\\_observations': a 'Sequence(Sequence(int64))' feature\n* 'continuous\\_observations': a 'Sequence(Sequence(float32))' feature\n* 'continuous\\_actions': a 'Sequence(Sequence(float32))' feature\n* 'discrete\\_actions': a 'Sequence(int64)' feature\n* 'rewards': a 'Sequence(float32)' feature### Data Splits\n\n\n* 'train': '' examples\n* 'test': '' examples\n\n\nDataset Creation\n----------------\n\n\nThis section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the JAT repository. For RL tasks, we trained one agent per task using the Sample Factory. Then we used the trained agent to generate episodes.",
"passage: ### Atari\n\n\nWe used the 57 ALE/Atari games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.### BabyAI\n\n\nWe used BabyAI's implementation from Minigrid.\nWe reused the bot agent provided with BabyAI's paper and adapted it to the new Minigrid API.\nUsing the bot, we generated 1.000.000 interractions for each of the 39 tasks of Minigrid's BabyAI and stored for each step:\n\n\n* the mission: str\n* the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)\n* the action: integer\n* the reward: float### Conceptual Captions\n\n\nThe Conceptual Captions dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.### Meta-World\n\n\nWe used the 50 tasks from Meta-World v2. We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.### MuJoCo\n\n\nWe used the 11 environments of Gymnasium MuJoCo.### OK-VQA\n\n\nThe OK-VQA dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.\nThe data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.### OSCAR\n\n\nWe modified the \"unshuffled\\_deduplicated\\_en\" split of OSCAR 2019 dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under CC BY 4.0.\nWe cleaned and deduplicated the dataset using the methods and parameters used for the ROOTS dataset (Lurençon et al., 2023).\n\n\nThe dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.### Wikipedia\n\n\nWe used the english version of the Wikipedia dataset.\n\n\nConsiderations for Using the Data\n---------------------------------"
] |
bbfb87381c7142a4d3cc45e7cd92800e0a52b588
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3_sd3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_eye_movements_gosdt_l512_d3_sd3
|
[
"region:us"
] |
2023-08-29T08:06:29+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2678676167, "dataset_size": 11949520000}}
|
2023-08-29T08:09:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3_sd3"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd3\"\n\nMore Information needed"
] |
2f5cc8dcfa8f6f9d0383260e8afb3d8637cd9e02
|
# Dataset Card for emotion-custom
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("saroj502/emotion-custom")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("saroj502/emotion-custom")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| text | Text | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| sentiment | Sentiment | LabelQuestion | True | N/A | ['positive', 'neutral', 'negative'] |
| mixed-emotion | Mixed-emotion | MultiLabelQuestion | True | N/A | ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'] |
**✨ NEW** Additionally, we also have **suggestions**, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"fields": {
"text": "i didnt feel humiliated"
},
"metadata": {},
"responses": [],
"suggestions": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": null,
"metadata": "{}",
"mixed-emotion": [],
"mixed-emotion-suggestion": null,
"mixed-emotion-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"sentiment": [],
"sentiment-suggestion": null,
"sentiment-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
},
"text": "i didnt feel humiliated"
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **text** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **sentiment** is of type `LabelQuestion` with the following allowed values ['positive', 'neutral', 'negative'].
* **mixed-emotion** is of type `MultiLabelQuestion` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
* **✨ NEW** **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **sentiment-suggestion** is of type `label_selection` with the following allowed values ['positive', 'neutral', 'negative'].
* (optional) **mixed-emotion-suggestion** is of type `multi_label_selection` with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
#### 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]
|
saroj502/emotion-custom
|
[
"size_categories:n<1K",
"rlfh",
"argilla",
"human-feedback",
"region:us"
] |
2023-08-29T08:07:08+00:00
|
{"size_categories": "n<1K", "tags": ["rlfh", "argilla", "human-feedback"]}
|
2023-08-29T08:07:11+00:00
|
[] |
[] |
TAGS
#size_categories-n<1K #rlfh #argilla #human-feedback #region-us
|
Dataset Card for emotion-custom
===============================
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the 'datasets' library in Load with 'datasets'.
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\_huggingface' method in Argilla.
* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\_huggingface' and can be loaded independently using the 'datasets' library via 'load\_dataset'.
* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:
### Load with 'datasets'
To load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:
### Supported Tasks and Leaderboards
This dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.
There are no leaderboards associated with this dataset.
### Languages
Dataset Structure
-----------------
### Data in Argilla
The dataset is created in Argilla with: fields, questions, suggestions, and guidelines.
The fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
The questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
NEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.
Finally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
While the same record in HuggingFace 'datasets' looks as follows:
### Data Fields
Among the dataset fields, we differentiate between the following:
* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
+ text is of type 'TextField'.
* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.
+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].
+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
+ (optional) sentiment-suggestion is of type 'label\_selection' with the following allowed values ['positive', 'neutral', 'negative'].
+ (optional) mixed-emotion-suggestion is of type 'multi\_label\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].
Additionally, we also have one more field which is optional and is the following:
* external\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is 'train'.
Dataset Creation
----------------
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation guidelines
Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.
#### 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 Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.",
"### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:",
"### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:",
"### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.",
"### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:",
"### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.",
"### Data Splits\n\n\nThe dataset contains a single split, which is 'train'.\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation guidelines\n\n\nEmotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise.",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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27,
162,
40,
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] |
[
"passage: TAGS\n#size_categories-n<1K #rlfh #argilla #human-feedback #region-us \n### Dataset Summary\n\n\nThis dataset contains:\n\n\n* A dataset configuration file conforming to the Argilla dataset format named 'URL'. This configuration file will be used to configure the dataset when using the 'FeedbackDataset.from\\_huggingface' method in Argilla.\n* Dataset records in a format compatible with HuggingFace 'datasets'. These records will be loaded automatically when using 'FeedbackDataset.from\\_huggingface' and can be loaded independently using the 'datasets' library via 'load\\_dataset'.\n* The annotation guidelines that have been used for building and curating the dataset, if they've been defined in Argilla.### Load with Argilla\n\n\nTo load with Argilla, you'll just need to install Argilla as 'pip install argilla --upgrade' and then use the following code:### Load with 'datasets'\n\n\nTo load this dataset with 'datasets', you'll just need to install 'datasets' as 'pip install datasets --upgrade' and then use the following code:### Supported Tasks and Leaderboards\n\n\nThis dataset can contain multiple fields, questions and responses so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the Dataset Structure section.\n\n\nThere are no leaderboards associated with this dataset.### Languages\n\n\nDataset Structure\n-----------------",
"passage: ### Data in Argilla\n\n\nThe dataset is created in Argilla with: fields, questions, suggestions, and guidelines.\n\n\nThe fields are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\nThe questions are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.\n\n\n\nNEW Additionally, we also have suggestions, which are linked to the existing questions, and so on, named appending \"-suggestion\" and \"-suggestion-metadata\" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above.\n\n\nFinally, the guidelines are just a plain string that can be used to provide instructions to the annotators. Find those in the annotation guidelines section.### Data Instances\n\n\nAn example of a dataset instance in Argilla looks as follows:\n\n\nWhile the same record in HuggingFace 'datasets' looks as follows:### Data Fields\n\n\nAmong the dataset fields, we differentiate between the following:\n\n\n* Fields: These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.\n\n\n\t+ text is of type 'TextField'.\n* Questions: These are the questions that will be asked to the annotators. They can be of different types, such as 'RatingQuestion', 'TextQuestion', 'LabelQuestion', 'MultiLabelQuestion', and 'RankingQuestion'.\n\n\n\t+ sentiment is of type 'LabelQuestion' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ mixed-emotion is of type 'MultiLabelQuestion' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n* NEW Suggestions: As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.\n\n\n\t+ (optional) sentiment-suggestion is of type 'label\\_selection' with the following allowed values ['positive', 'neutral', 'negative'].\n\t+ (optional) mixed-emotion-suggestion is of type 'multi\\_label\\_selection' with the following allowed values ['joy', 'anger', 'sadness', 'fear', 'surprise', 'love'].\n\n\nAdditionally, we also have one more field which is optional and is the following:\n\n\n* external\\_id: This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file."
] |
14be59243252bf11cead17265a11cdce47df071d
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3_sd1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_eye_movements_gosdt_l512_d3_sd1
|
[
"region:us"
] |
2023-08-29T08:07:18+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10863200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 1086320000, "num_examples": 10000}], "download_size": 2726682247, "dataset_size": 11949520000}}
|
2023-08-29T08:10:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_eye_movements_gosdt_l512_d3_sd1"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_eye_movements_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
d685b0e44d54f7321b258d68641a6451ee9a157a
|
# Dataset Card for Evaluation run of yeontaek/llama-2-13B-ensemble-v5
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-13B-ensemble-v5
- **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 [yeontaek/llama-2-13B-ensemble-v5](https://huggingface.co/yeontaek/llama-2-13B-ensemble-v5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_yeontaek__llama-2-13B-ensemble-v5",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T09:17:14.183323](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v5/blob/main/results_2023-08-29T09%3A17%3A14.183323.json):
```python
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"mc2": 0.5327328500103707,
"mc2_stderr": 0.015551697577870274
},
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},
"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": 0.4819277108433735,
"acc_stderr": 0.03889951252827217,
"acc_norm": 0.4819277108433735,
"acc_norm_stderr": 0.03889951252827217
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.030611116557432528,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.030611116557432528
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3843329253365973,
"mc1_stderr": 0.017028707301245203,
"mc2": 0.5327328500103707,
"mc2_stderr": 0.015551697577870274
}
}
```
### 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_yeontaek__llama-2-13B-ensemble-v5
|
[
"region:us"
] |
2023-08-29T08:18:15+00:00
|
{"pretty_name": "Evaluation run of yeontaek/llama-2-13B-ensemble-v5", "dataset_summary": "Dataset automatically created during the evaluation run of model [yeontaek/llama-2-13B-ensemble-v5](https://huggingface.co/yeontaek/llama-2-13B-ensemble-v5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 agregated 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_yeontaek__llama-2-13B-ensemble-v5\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T09:17:14.183323](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v5/blob/main/results_2023-08-29T09%3A17%3A14.183323.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.5953117661059801,\n \"acc_stderr\": 0.03391896483304526,\n \"acc_norm\": 0.5994365516843435,\n \"acc_norm_stderr\": 0.033896234769528244,\n \"mc1\": 0.3843329253365973,\n \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5327328500103707,\n \"mc2_stderr\": 0.015551697577870274\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5844709897610921,\n \"acc_stderr\": 0.014401366641216388,\n \"acc_norm\": 0.6262798634812287,\n \"acc_norm_stderr\": 0.014137708601759084\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6290579565823541,\n \"acc_stderr\": 0.004820697457420419,\n \"acc_norm\": 0.8306114319856602,\n \"acc_norm_stderr\": 0.0037432817493736324\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\": 0.5185185185185185,\n \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\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\": 0.6264150943396226,\n \"acc_stderr\": 0.029773082713319875,\n \"acc_norm\": 0.6264150943396226,\n \"acc_norm_stderr\": 0.029773082713319875\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.03942082639927213,\n \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.03942082639927213\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\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5722543352601156,\n \"acc_stderr\": 0.037724468575180255,\n \"acc_norm\": 0.5722543352601156,\n \"acc_norm_stderr\": 0.037724468575180255\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105653,\n \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105653\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\": 0.49361702127659574,\n \"acc_stderr\": 0.032683358999363366,\n \"acc_norm\": 0.49361702127659574,\n \"acc_norm_stderr\": 0.032683358999363366\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.36243386243386244,\n \"acc_stderr\": 0.02475747390275206,\n \"acc_norm\": 0.36243386243386244,\n \"acc_norm_stderr\": 0.02475747390275206\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\": 0.37,\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\": 0.6806451612903226,\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\": 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n \"acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n },\n \"harness|hendrycksTest-high_school_computer_science|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-high_school_european_history|5\": {\n \"acc\": 0.7212121212121212,\n \"acc_stderr\": 0.03501438706296781,\n \"acc_norm\": 0.7212121212121212,\n \"acc_norm_stderr\": 0.03501438706296781\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8756476683937824,\n \"acc_stderr\": 0.023814477086593552,\n \"acc_norm\": 0.8756476683937824,\n \"acc_norm_stderr\": 0.023814477086593552\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.02475600038213095,\n \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.02475600038213095\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.028742040903948496,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.028742040903948496\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.634453781512605,\n \"acc_stderr\": 0.031282177063684614,\n \"acc_norm\": 0.634453781512605,\n \"acc_norm_stderr\": 0.031282177063684614\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\": 0.8036697247706422,\n \"acc_stderr\": 0.017030719339154336,\n \"acc_norm\": 0.8036697247706422,\n \"acc_norm_stderr\": 0.017030719339154336\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321616,\n \"acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321616\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7679324894514767,\n \"acc_stderr\": 0.02747974455080852,\n \"acc_norm\": 0.7679324894514767,\n \"acc_norm_stderr\": 0.02747974455080852\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6367713004484304,\n \"acc_stderr\": 0.032277904428505,\n \"acc_norm\": 0.6367713004484304,\n \"acc_norm_stderr\": 0.032277904428505\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6717557251908397,\n \"acc_stderr\": 0.041184385658062976,\n \"acc_norm\": 0.6717557251908397,\n \"acc_norm_stderr\": 0.041184385658062976\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.71900826446281,\n \"acc_stderr\": 0.04103203830514512,\n \"acc_norm\": 0.71900826446281,\n \"acc_norm_stderr\": 0.04103203830514512\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.035123852837050475,\n \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.035123852837050475\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.36607142857142855,\n \"acc_stderr\": 0.045723723587374296,\n \"acc_norm\": 0.36607142857142855,\n \"acc_norm_stderr\": 0.045723723587374296\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.04354631077260595,\n \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.04354631077260595\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n \"acc_stderr\": 0.02336505149175372,\n \"acc_norm\": 0.8504273504273504,\n \"acc_norm_stderr\": 0.02336505149175372\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7879948914431673,\n \"acc_stderr\": 0.014616099385833685,\n \"acc_norm\": 0.7879948914431673,\n \"acc_norm_stderr\": 0.014616099385833685\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6416184971098265,\n \"acc_stderr\": 0.025816756791584194,\n \"acc_norm\": 0.6416184971098265,\n \"acc_norm_stderr\": 0.025816756791584194\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4849162011173184,\n \"acc_stderr\": 0.01671489037999606,\n \"acc_norm\": 0.4849162011173184,\n \"acc_norm_stderr\": 0.01671489037999606\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6405228758169934,\n \"acc_stderr\": 0.027475969910660952,\n \"acc_norm\": 0.6405228758169934,\n \"acc_norm_stderr\": 0.027475969910660952\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n \"acc_stderr\": 0.02600330111788514,\n \"acc_norm\": 0.7009646302250804,\n \"acc_norm_stderr\": 0.02600330111788514\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.024922001168886335,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.024922001168886335\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4654498044328553,\n \"acc_stderr\": 0.012739711554045708,\n \"acc_norm\": 0.4654498044328553,\n \"acc_norm_stderr\": 0.012739711554045708\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.5992647058823529,\n \"acc_stderr\": 0.029768263528933105,\n \"acc_norm\": 0.5992647058823529,\n \"acc_norm_stderr\": 0.029768263528933105\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.019794488900024117,\n \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.019794488900024117\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n \"acc_stderr\": 0.04461272175910507,\n \"acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.04461272175910507\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6530612244897959,\n \"acc_stderr\": 0.030472526026726496,\n \"acc_norm\": 0.6530612244897959,\n \"acc_norm_stderr\": 0.030472526026726496\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7711442786069652,\n \"acc_stderr\": 0.02970528405677244,\n \"acc_norm\": 0.7711442786069652,\n \"acc_norm_stderr\": 0.02970528405677244\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4819277108433735,\n \"acc_stderr\": 0.03889951252827217,\n \"acc_norm\": 0.4819277108433735,\n \"acc_norm_stderr\": 0.03889951252827217\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3843329253365973,\n \"mc1_stderr\": 0.017028707301245203,\n \"mc2\": 0.5327328500103707,\n \"mc2_stderr\": 0.015551697577870274\n }\n}\n```", "repo_url": "https://huggingface.co/yeontaek/llama-2-13B-ensemble-v5", "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_08_29T09_17_14.183323", "path": ["**/details_harness|arc:challenge|25_2023-08-29T09:17:14.183323.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T09:17:14.183323.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T09_17_14.183323", "path": ["**/details_harness|hellaswag|10_2023-08-29T09:17:14.183323.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T09:17:14.183323.parquet"]}]}, {"config_name": "harness_hendrycksTest", "data_files": [{"split": "2023_08_29T09_17_14.183323", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T09:17:14.183323.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T09:17:14.183323.parquet", 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|
2023-08-29T08:19:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of yeontaek/llama-2-13B-ensemble-v5
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model yeontaek/llama-2-13B-ensemble-v5 on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T09:17:14.183323:
### 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 yeontaek/llama-2-13B-ensemble-v5",
"## 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 yeontaek/llama-2-13B-ensemble-v5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T09:17:14.183323:",
"### 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 yeontaek/llama-2-13B-ensemble-v5",
"## 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 yeontaek/llama-2-13B-ensemble-v5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T09:17:14.183323:",
"### 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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8,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of yeontaek/llama-2-13B-ensemble-v5## 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 yeontaek/llama-2-13B-ensemble-v5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T09:17:14.183323:### 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"
] |
ee2cde4f3a36fe26014cc53c8a8af244a097f65a
|
# Dataset Card for "eng-conversations_no-tokenizer_no-time"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
aimona/eng-conversations_no-tokenizer_no-time
|
[
"region:us"
] |
2023-08-29T08:22:06+00:00
|
{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instructions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 384592697, "num_examples": 30052}], "download_size": 177374362, "dataset_size": 384592697}}
|
2023-08-29T08:22:21+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eng-conversations_no-tokenizer_no-time"
More Information needed
|
[
"# Dataset Card for \"eng-conversations_no-tokenizer_no-time\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eng-conversations_no-tokenizer_no-time\"\n\nMore Information needed"
] |
[
6,
25
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eng-conversations_no-tokenizer_no-time\"\n\nMore Information needed"
] |
0a440115f1b7047b5b9ebc9a7aaa8175cce15ebb
|
# Dataset Card for "autotree_automl_MagicTelescope_gosdt_l512_d3_sd1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yzhuang/autotree_automl_MagicTelescope_gosdt_l512_d3_sd1
|
[
"region:us"
] |
2023-08-29T08:29:19+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "input_x", "sequence": {"sequence": "float64"}}, {"name": "input_y", "sequence": {"sequence": "float32"}}, {"name": "rtg", "sequence": "float64"}, {"name": "status", "sequence": {"sequence": "float32"}}, {"name": "split_threshold", "sequence": {"sequence": "float64"}}, {"name": "split_dimension", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6767200000, "num_examples": 100000}, {"name": "validation", "num_bytes": 676720000, "num_examples": 10000}], "download_size": 2605525039, "dataset_size": 7443920000}}
|
2023-08-29T08:31:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "autotree_automl_MagicTelescope_gosdt_l512_d3_sd1"
More Information needed
|
[
"# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"autotree_automl_MagicTelescope_gosdt_l512_d3_sd1\"\n\nMore Information needed"
] |
89aebd2450b484717979423890f8ce992fffcc20
|
# Hair Detection & Segmentation Dataset
The dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.
The dataset contains a diverse collection of images depicting people with different *hair styles, colors, lengths, and textures*. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.
The dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=hair-detection-and-segmentation) to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- **images** - contains of original images of people
- **masks** - includes segmentation masks for the original images
- **collages** - includes original images with colored hairs within the oval of the face
- **annotations.xml** - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.
### Tags for the images:
- **is_hair** - contains of original images of people
- **no_hair** - includes segmentation masks for the original images
# Example of XML file structure
# Hair Detection & Segmentation might be made in accordance with your requirements.
## [**TrainingData**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=hair-detection-and-segmentation) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**
|
TrainingDataPro/hair-detection-and-segmentation
|
[
"task_categories:image-segmentation",
"task_categories:image-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"region:us"
] |
2023-08-29T08:39:36+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-segmentation", "image-classification"], "tags": ["code"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}, {"name": "collage", "dtype": "image"}, {"name": "shapes", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 482079410, "num_examples": 98}], "download_size": 478206925, "dataset_size": 482079410}}
|
2023-09-14T15:24:05+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-segmentation #task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #code #region-us
|
# Hair Detection & Segmentation Dataset
The dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.
The dataset contains a diverse collection of images depicting people with different *hair styles, colors, lengths, and textures*. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.
The dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.
![](URL
# Get the dataset
### This is just an example of the data
Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.
# Dataset structure
- images - contains of original images of people
- masks - includes segmentation masks for the original images
- collages - includes original images with colored hairs within the oval of the face
- URL - contains coordinates of the bounding boxes and labels, created for the original photo
# Data Format
Each image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.
### Tags for the images:
- is_hair - contains of original images of people
- no_hair - includes segmentation masks for the original images
# Example of XML file structure
![](URL
# Hair Detection & Segmentation might be made in accordance with your requirements.
## TrainingData provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: URL
TrainingData's GitHub: URL
|
[
"# Hair Detection & Segmentation Dataset\n\nThe dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.\n\nThe dataset contains a diverse collection of images depicting people with different *hair styles, colors, lengths, and textures*. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.\n\nThe dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Dataset structure\n- images - contains of original images of people\n- masks - includes segmentation masks for the original images\n- collages - includes original images with colored hairs within the oval of the face\n- URL - contains coordinates of the bounding boxes and labels, created for the original photo",
"# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.",
"### Tags for the images:\n- is_hair - contains of original images of people\n- no_hair - includes segmentation masks for the original images",
"# Example of XML file structure\n![](URL",
"# Hair Detection & Segmentation might be made in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
"TAGS\n#task_categories-image-segmentation #task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #code #region-us \n",
"# Hair Detection & Segmentation Dataset\n\nThe dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.\n\nThe dataset contains a diverse collection of images depicting people with different *hair styles, colors, lengths, and textures*. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.\n\nThe dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.\n\n![](URL",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Dataset structure\n- images - contains of original images of people\n- masks - includes segmentation masks for the original images\n- collages - includes original images with colored hairs within the oval of the face\n- URL - contains coordinates of the bounding boxes and labels, created for the original photo",
"# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.",
"### Tags for the images:\n- is_hair - contains of original images of people\n- no_hair - includes segmentation masks for the original images",
"# Example of XML file structure\n![](URL",
"# Hair Detection & Segmentation might be made in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
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48,
193,
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[
"passage: TAGS\n#task_categories-image-segmentation #task_categories-image-classification #language-English #license-cc-by-nc-nd-4.0 #code #region-us \n# Hair Detection & Segmentation Dataset\n\nThe dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.\n\nThe dataset contains a diverse collection of images depicting people with different *hair styles, colors, lengths, and textures*. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.\n\nThe dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.\n\n![](URL# Get the dataset### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Dataset structure\n- images - contains of original images of people\n- masks - includes segmentation masks for the original images\n- collages - includes original images with colored hairs within the oval of the face\n- URL - contains coordinates of the bounding boxes and labels, created for the original photo# Data Format\n\nEach image from 'images' folder is accompanied by an XML-annotation in the 'URL' file indicating the coordinates of the bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.### Tags for the images:\n- is_hair - contains of original images of people\n- no_hair - includes segmentation masks for the original images# Example of XML file structure\n![](URL# Hair Detection & Segmentation might be made in accordance with your requirements.## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
8196c6b8981d457c8dc23b70a5a7ed2b9bc18aa9
|
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[https://hackmd.io/@cinnachroma/cinnachroma-blood-sugar-support](https://hackmd.io/@cinnachroma/cinnachroma-blood-sugar-support)
|
cinnachroma/cinnachroma-blood-sugar-support
|
[
"region:us"
] |
2023-08-29T08:42:57+00:00
|
{}
|
2023-08-29T08:44:42+00:00
|
[] |
[] |
TAGS
#region-us
|
Product Name - CinnaChroma
Category - Blood Sugar Support Formula
Result - 2-3 Months
Country - USA
Availability - Online
Official Website - URL
CinnaChroma, - a brand well-known in the health market - is a complete nutritional supplement. The natural ingredients in this supplement work together in a synergistic way. Each ingredient was selected for its ability to promote healthy blood. This powerful combination is designed to help people maintain healthy blood sugar levels and control their blood pressure. Due to their effectiveness, CinnaChroma tablets are increasingly popular among those who want to find natural solutions to their blood health problems.
What Is CinnaChroma?
------------------------
According to the creators of CinnaChroma, this is a brand-new product that promises to control blood pressure and sugar levels. CinnaChroma's makers have made it a point to say that the product is superior to other products currently available. CinnaChroma is said to contain scientifically-proven natural ingredients that regulate blood pressure and high blood pressure. CinnaChroma operates with an action triple that results in immediate results. They believe that CinnaChroma will aid in blood pressure and blood sugar management, in addition to losing weight and reducing weight loss.
How Does CinnaChroma Work?
------------------------------
Human health is at risk from high blood sugar levels. People who have this condition need to find immediate treatment. Due to its effectiveness in maintaining and regulating blood sugar levels, it stands as the best remedy for fluctuating blood sugar levels.
It’s significant that it contains six potent ingredients that attack the source of diabetes and help to naturally cure it. Users of this formula are not required to give up their favourite foods or alter their lifestyles, according to the manufacturer. Users can lose weight naturally and securely with blood sugar support formula.
Benefits of CinnaChroma Supplement
--------------------------------------
If you consume this supplement regularly for three to six months, you’re bound to experience life-changing prostate health benefits such as…
Help Ease Worries of Diabetic Blindness: The most common cause of blindness in the world is diabetic retinopathy. CinnaChroma, according to Barton Nutrition, can support severe retinal and optic nerve damage, easing concerns about diabetic blindness. Your optic nerves and retina are damaged by high blood sugar levels, but CinnaChroma is said to support these areas of your eyes to reduce concerns about blindness.
Eliminate Blurry Vision and Floaters: CinnaChroma allegedly treats eye nerve damage to eliminate floaters and blurry vision. Usually, diabetic retinopathy patients must endure a lifetime of retinal injections, vision loss, and eventual blindness. CinnaChroma shields damaged blood vessels from these issues.
Insulin resistance is the primary cause of Type 2 diabetes, and this supplement works to reverse it in order to keep you healthy.
Support Healthy Blood Pressure: It exclusively combines the most tried-and-true ingredients in order to support healthy blood pressure that is already within the normal range.
Protect Retina Cells from Diabetes: Retinal cells are impacted by diabetes. Your retina and optic nerve can become damaged by high blood sugar levels over time. CinnaChroma, however, is said to be able to shield your retinal cells from the effects of diabetes.
Drop Blood Sugar Levels by 24%: Diabetics typically use prescription drugs to bring their blood sugar levels down to normal levels. However, CinnaChroma can reduce blood sugar levels by 24%, according to the official Barton Nutrition website. According to the manufacturer, the formula has been "proven to reduce blood glucose levels dramatically fast" and can protect retina cells by "naturally dropping blood sugar levels by 24%."
Promote a Healthy Insulin Response: When blood sugar levels increase, your body responds by producing insulin. If you have diabetes, your body reacts to insulin differently from someone who does not. CinnaChroma, however, reportedly helps nitric oxide production, which in turn supports a healthy insulin response. Nitric oxide is essential for the delivery of insulin.
 Visit The Official CinnaChroma Website To Order
How To Use CinnaChroma For Best Results?
--------------------------------------------
CinnaChroma comes in capsule form, and every capsule contains a potent blend of 12 natural ingredients rich in vital vitamins, minerals, and nutrients. For best results, three capsules should be taken daily, ideally one capsule after each meal. It is recommended to remain consistent to see CinnaChroma work in full force.
Side Effects Of Using CinnaChroma
-------------------------------------
CinnaChroma is a pure solution made with the best ingredients. Side effects were rare for those who took the recommended dose. The product was made in an FDA-approved, GMP-certified manufacturing facility.
Each bottle of CinnaChroma is free from any preservatives or herbicides, stimulants or other chemicals that could harm your health. It may take some time but the results you will get are real. CinnaChroma reviews have shown that the ingredients are completely natural and safe. CinnaChroma side effects are therefore minimal.
CinnaChroma Pricing
-----------------------
_1 Bottle - $59 per bottle - Plus Shipping & Handling_
_3 Bottles - $49 per bottle + Free Shipping in USA + Bonus! Free Digital Book_
_6 Bottles - $39 per bottle + Free Shipping in USA + Bonus! Free Digital Book_
 Order CinnaChroma From Its Official Website Here!
PLUS+ Exclusive Online FREE Recommended Bonus The Blood Sugar Solution Kit
------------------------------------------------------------------------------
* The Stable Blood Sugar Resource Guide
* Natural Remedies for Erratic Blood Sugar
* The Low Blood Sugar Cookbook
* Carb-Counting Cheat Sheet
* Personal Meal & Exercise Planner
* Blood Sugar Solution Grocery List
* Free (One on One) Program Customization Coaching Call
![.png)](URL
100% Money Back Guarantee
-----------------------------
CinnaChroma comes with a 365 Day, 100% Money Back Guarantee. That means if you change your mind about this decision at any point in the next 365 days... all you need to do is contact our US based Customer Service and we will refund your purchase. You can even keep the FREE Blood Sugar Solution Kit bonus as our way of saying "thanks" for giving CinnaChroma a try...
Where To Buy CinnaChroma?
-----------------------------
The CinnaChroma supplement is available on the official website only. Nevertheless, you may find these supplements on third-party websites, but there is no guarantee of their quality or efficiency. Therefore, purchasing the CinnaChroma from the official website.
The Bottom Line
-------------------
The natural diabetes plant extracts of Gymnema Sylvestre, berberine, black plum, shilajit, licorice root, guggul, and bitter melon have all received excellent research attention. Interestingly enough, CinnaChroma is the only available natural supplement with all of these ingredients in its formulation.
The unique formula of CinnaChroma is what makes it stand out among all other natural diabetes supplements, and this is also the reason behind the immediate results CinnaChroma can produce.
![.png)](URL
### Do Not Miss Out On Special Discount At The Official Website Of CinnaChroma
URL
URL
URL
URL
URL
URL
URL
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URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
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[
"### Click Here – OFFICIAL WEBSITE (CinnaChroma)\n\nWhat Is CinnaChroma?\n------------------------\n\nAccording to the creators of CinnaChroma, this is a brand-new product that promises to control blood pressure and sugar levels. CinnaChroma's makers have made it a point to say that the product is superior to other products currently available. CinnaChroma is said to contain scientifically-proven natural ingredients that regulate blood pressure and high blood pressure. CinnaChroma operates with an action triple that results in immediate results. They believe that CinnaChroma will aid in blood pressure and blood sugar management, in addition to losing weight and reducing weight loss.\n\nHow Does CinnaChroma Work?\n------------------------------\n\nHuman health is at risk from high blood sugar levels. People who have this condition need to find immediate treatment. Due to its effectiveness in maintaining and regulating blood sugar levels, it stands as the best remedy for fluctuating blood sugar levels.\n\nIt’s significant that it contains six potent ingredients that attack the source of diabetes and help to naturally cure it. Users of this formula are not required to give up their favourite foods or alter their lifestyles, according to the manufacturer. Users can lose weight naturally and securely with blood sugar support formula.\n\nBenefits of CinnaChroma Supplement\n--------------------------------------\n\nIf you consume this supplement regularly for three to six months, you’re bound to experience life-changing prostate health benefits such as…\n\nHelp Ease Worries of Diabetic Blindness: The most common cause of blindness in the world is diabetic retinopathy. CinnaChroma, according to Barton Nutrition, can support severe retinal and optic nerve damage, easing concerns about diabetic blindness. Your optic nerves and retina are damaged by high blood sugar levels, but CinnaChroma is said to support these areas of your eyes to reduce concerns about blindness.\n\nEliminate Blurry Vision and Floaters: CinnaChroma allegedly treats eye nerve damage to eliminate floaters and blurry vision. Usually, diabetic retinopathy patients must endure a lifetime of retinal injections, vision loss, and eventual blindness. CinnaChroma shields damaged blood vessels from these issues.\n\nInsulin resistance is the primary cause of Type 2 diabetes, and this supplement works to reverse it in order to keep you healthy.\n\nSupport Healthy Blood Pressure: It exclusively combines the most tried-and-true ingredients in order to support healthy blood pressure that is already within the normal range.\n\nProtect Retina Cells from Diabetes: Retinal cells are impacted by diabetes. Your retina and optic nerve can become damaged by high blood sugar levels over time. CinnaChroma, however, is said to be able to shield your retinal cells from the effects of diabetes.\n\nDrop Blood Sugar Levels by 24%: Diabetics typically use prescription drugs to bring their blood sugar levels down to normal levels. However, CinnaChroma can reduce blood sugar levels by 24%, according to the official Barton Nutrition website. According to the manufacturer, the formula has been \"proven to reduce blood glucose levels dramatically fast\" and can protect retina cells by \"naturally dropping blood sugar levels by 24%.\"\n\nPromote a Healthy Insulin Response: When blood sugar levels increase, your body responds by producing insulin. If you have diabetes, your body reacts to insulin differently from someone who does not. CinnaChroma, however, reportedly helps nitric oxide production, which in turn supports a healthy insulin response. Nitric oxide is essential for the delivery of insulin.\n\n![.png)](URL",
"### Enjoy The Benefits Of CinnaChroma – Order Now By Clicking Here!\n\nIngredients Used To Manufacture CinnaChroma\n-----------------------------------------------\n\nThe CinnaChroma formula is a potent mix of all-natural ingredients that help in enhancing retinal health. The list of these super-ingredients along with their properties are listed below:\n\nChromium Picolinate : Blood sugar control is another benefit of chromium picolinate. To reduce fasting blood glucose, chromium and picolinate both contribute. Together, they can reduce blood glucose levels by 300% and post-meal glucose levels by 200%. Chromium is guided into the cells of your digestive tract by the picolinate, preventing it from being lost along the way.\n\nVitamin D3 : Another advantageous component that functions as a regulatory hormone is vitamin D3. Vitamin D3 supports glucose uptake into cells and increases insulin release to ensure that your biological processes run as smoothly as possible. Additionally, vitamin D3 has been shown to reduce blood pressure and treat depression. Additionally, it strengthens the immune system. According to Dr. Saunders' advice, the CinnaChroma supplement contains 5000 IUs.\n\nVitamin K2 : Additionally crucial to lowering high blood sugar levels is vitamin K2. It effectively enables the body to use vitamin D3 where it is needed. Along with enhancing your general health, vitamin K2 also aids in reducing inflammation.\n\nVanadium : Vanadium works by transporting blood glucose into your cells where it can be converted to energy. Vanadium and chromium work together to produce amazing effects. It first aids in lowering blood sugar levels and aids in suppressing cravings, enabling weight loss.\n\nSelenium : The final component, selenium, works similarly to vitamin K2 in reducing inflammation. Selenium has also been shown to reduce the risk of cancer by 37%. Additionally, it works wonders for enhancing general wellbeing.\n\nBenfotiamine : This component aids in blood sugar regulation and has been used for many years to treat diabetes-related nerve damage. Additionally, it improves nerve conduction, nerve damage, blood vessel health, and other problems brought on by diabetes. Additionally, this component lessens retinal cell death and shields them from high blood glucose levels.\n\n![](URL",
"### (Promo Offer) Visit The Official CinnaChroma Website To Order\n\nHow To Use CinnaChroma For Best Results?\n--------------------------------------------\n\nCinnaChroma comes in capsule form, and every capsule contains a potent blend of 12 natural ingredients rich in vital vitamins, minerals, and nutrients. For best results, three capsules should be taken daily, ideally one capsule after each meal. It is recommended to remain consistent to see CinnaChroma work in full force.\n\nSide Effects Of Using CinnaChroma\n-------------------------------------\n\nCinnaChroma is a pure solution made with the best ingredients. Side effects were rare for those who took the recommended dose. The product was made in an FDA-approved, GMP-certified manufacturing facility.\n\nEach bottle of CinnaChroma is free from any preservatives or herbicides, stimulants or other chemicals that could harm your health. It may take some time but the results you will get are real. CinnaChroma reviews have shown that the ingredients are completely natural and safe. CinnaChroma side effects are therefore minimal.\n\nCinnaChroma Pricing\n-----------------------\n\n_1 Bottle - $59 per bottle - Plus Shipping & Handling_\n\n_3 Bottles - $49 per bottle + Free Shipping in USA + Bonus! Free Digital Book_\n\n_6 Bottles - $39 per bottle + Free Shipping in USA + Bonus! Free Digital Book_\n\n![.png)](URL",
"### (Free Bonus Ebook) Order CinnaChroma From Its Official Website Here!\n\nPLUS+ Exclusive Online FREE Recommended Bonus The Blood Sugar Solution Kit\n------------------------------------------------------------------------------\n\n* The Stable Blood Sugar Resource Guide\n* Natural Remedies for Erratic Blood Sugar\n* The Low Blood Sugar Cookbook\n* Carb-Counting Cheat Sheet\n* Personal Meal & Exercise Planner\n* Blood Sugar Solution Grocery List\n* Free (One on One) Program Customization Coaching Call\n\n![.png)](URL\n\n100% Money Back Guarantee\n-----------------------------\n\nCinnaChroma comes with a 365 Day, 100% Money Back Guarantee. That means if you change your mind about this decision at any point in the next 365 days... all you need to do is contact our US based Customer Service and we will refund your purchase. You can even keep the FREE Blood Sugar Solution Kit bonus as our way of saying \"thanks\" for giving CinnaChroma a try...\n\nWhere To Buy CinnaChroma?\n-----------------------------\n\nThe CinnaChroma supplement is available on the official website only. Nevertheless, you may find these supplements on third-party websites, but there is no guarantee of their quality or efficiency. Therefore, purchasing the CinnaChroma from the official website.\n\nThe Bottom Line\n-------------------\n\nThe natural diabetes plant extracts of Gymnema Sylvestre, berberine, black plum, shilajit, licorice root, guggul, and bitter melon have all received excellent research attention. Interestingly enough, CinnaChroma is the only available natural supplement with all of these ingredients in its formulation. \nThe unique formula of CinnaChroma is what makes it stand out among all other natural diabetes supplements, and this is also the reason behind the immediate results CinnaChroma can produce.\n\n![.png)](URL",
"### Do Not Miss Out On Special Discount At The Official Website Of CinnaChroma\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL"
] |
[
"TAGS\n#region-us \n",
"### Click Here – OFFICIAL WEBSITE (CinnaChroma)\n\nWhat Is CinnaChroma?\n------------------------\n\nAccording to the creators of CinnaChroma, this is a brand-new product that promises to control blood pressure and sugar levels. CinnaChroma's makers have made it a point to say that the product is superior to other products currently available. CinnaChroma is said to contain scientifically-proven natural ingredients that regulate blood pressure and high blood pressure. CinnaChroma operates with an action triple that results in immediate results. They believe that CinnaChroma will aid in blood pressure and blood sugar management, in addition to losing weight and reducing weight loss.\n\nHow Does CinnaChroma Work?\n------------------------------\n\nHuman health is at risk from high blood sugar levels. People who have this condition need to find immediate treatment. Due to its effectiveness in maintaining and regulating blood sugar levels, it stands as the best remedy for fluctuating blood sugar levels.\n\nIt’s significant that it contains six potent ingredients that attack the source of diabetes and help to naturally cure it. Users of this formula are not required to give up their favourite foods or alter their lifestyles, according to the manufacturer. Users can lose weight naturally and securely with blood sugar support formula.\n\nBenefits of CinnaChroma Supplement\n--------------------------------------\n\nIf you consume this supplement regularly for three to six months, you’re bound to experience life-changing prostate health benefits such as…\n\nHelp Ease Worries of Diabetic Blindness: The most common cause of blindness in the world is diabetic retinopathy. CinnaChroma, according to Barton Nutrition, can support severe retinal and optic nerve damage, easing concerns about diabetic blindness. Your optic nerves and retina are damaged by high blood sugar levels, but CinnaChroma is said to support these areas of your eyes to reduce concerns about blindness.\n\nEliminate Blurry Vision and Floaters: CinnaChroma allegedly treats eye nerve damage to eliminate floaters and blurry vision. Usually, diabetic retinopathy patients must endure a lifetime of retinal injections, vision loss, and eventual blindness. CinnaChroma shields damaged blood vessels from these issues.\n\nInsulin resistance is the primary cause of Type 2 diabetes, and this supplement works to reverse it in order to keep you healthy.\n\nSupport Healthy Blood Pressure: It exclusively combines the most tried-and-true ingredients in order to support healthy blood pressure that is already within the normal range.\n\nProtect Retina Cells from Diabetes: Retinal cells are impacted by diabetes. Your retina and optic nerve can become damaged by high blood sugar levels over time. CinnaChroma, however, is said to be able to shield your retinal cells from the effects of diabetes.\n\nDrop Blood Sugar Levels by 24%: Diabetics typically use prescription drugs to bring their blood sugar levels down to normal levels. However, CinnaChroma can reduce blood sugar levels by 24%, according to the official Barton Nutrition website. According to the manufacturer, the formula has been \"proven to reduce blood glucose levels dramatically fast\" and can protect retina cells by \"naturally dropping blood sugar levels by 24%.\"\n\nPromote a Healthy Insulin Response: When blood sugar levels increase, your body responds by producing insulin. If you have diabetes, your body reacts to insulin differently from someone who does not. CinnaChroma, however, reportedly helps nitric oxide production, which in turn supports a healthy insulin response. Nitric oxide is essential for the delivery of insulin.\n\n![.png)](URL",
"### Enjoy The Benefits Of CinnaChroma – Order Now By Clicking Here!\n\nIngredients Used To Manufacture CinnaChroma\n-----------------------------------------------\n\nThe CinnaChroma formula is a potent mix of all-natural ingredients that help in enhancing retinal health. The list of these super-ingredients along with their properties are listed below:\n\nChromium Picolinate : Blood sugar control is another benefit of chromium picolinate. To reduce fasting blood glucose, chromium and picolinate both contribute. Together, they can reduce blood glucose levels by 300% and post-meal glucose levels by 200%. Chromium is guided into the cells of your digestive tract by the picolinate, preventing it from being lost along the way.\n\nVitamin D3 : Another advantageous component that functions as a regulatory hormone is vitamin D3. Vitamin D3 supports glucose uptake into cells and increases insulin release to ensure that your biological processes run as smoothly as possible. Additionally, vitamin D3 has been shown to reduce blood pressure and treat depression. Additionally, it strengthens the immune system. According to Dr. Saunders' advice, the CinnaChroma supplement contains 5000 IUs.\n\nVitamin K2 : Additionally crucial to lowering high blood sugar levels is vitamin K2. It effectively enables the body to use vitamin D3 where it is needed. Along with enhancing your general health, vitamin K2 also aids in reducing inflammation.\n\nVanadium : Vanadium works by transporting blood glucose into your cells where it can be converted to energy. Vanadium and chromium work together to produce amazing effects. It first aids in lowering blood sugar levels and aids in suppressing cravings, enabling weight loss.\n\nSelenium : The final component, selenium, works similarly to vitamin K2 in reducing inflammation. Selenium has also been shown to reduce the risk of cancer by 37%. Additionally, it works wonders for enhancing general wellbeing.\n\nBenfotiamine : This component aids in blood sugar regulation and has been used for many years to treat diabetes-related nerve damage. Additionally, it improves nerve conduction, nerve damage, blood vessel health, and other problems brought on by diabetes. Additionally, this component lessens retinal cell death and shields them from high blood glucose levels.\n\n![](URL",
"### (Promo Offer) Visit The Official CinnaChroma Website To Order\n\nHow To Use CinnaChroma For Best Results?\n--------------------------------------------\n\nCinnaChroma comes in capsule form, and every capsule contains a potent blend of 12 natural ingredients rich in vital vitamins, minerals, and nutrients. For best results, three capsules should be taken daily, ideally one capsule after each meal. It is recommended to remain consistent to see CinnaChroma work in full force.\n\nSide Effects Of Using CinnaChroma\n-------------------------------------\n\nCinnaChroma is a pure solution made with the best ingredients. Side effects were rare for those who took the recommended dose. The product was made in an FDA-approved, GMP-certified manufacturing facility.\n\nEach bottle of CinnaChroma is free from any preservatives or herbicides, stimulants or other chemicals that could harm your health. It may take some time but the results you will get are real. CinnaChroma reviews have shown that the ingredients are completely natural and safe. CinnaChroma side effects are therefore minimal.\n\nCinnaChroma Pricing\n-----------------------\n\n_1 Bottle - $59 per bottle - Plus Shipping & Handling_\n\n_3 Bottles - $49 per bottle + Free Shipping in USA + Bonus! Free Digital Book_\n\n_6 Bottles - $39 per bottle + Free Shipping in USA + Bonus! Free Digital Book_\n\n![.png)](URL",
"### (Free Bonus Ebook) Order CinnaChroma From Its Official Website Here!\n\nPLUS+ Exclusive Online FREE Recommended Bonus The Blood Sugar Solution Kit\n------------------------------------------------------------------------------\n\n* The Stable Blood Sugar Resource Guide\n* Natural Remedies for Erratic Blood Sugar\n* The Low Blood Sugar Cookbook\n* Carb-Counting Cheat Sheet\n* Personal Meal & Exercise Planner\n* Blood Sugar Solution Grocery List\n* Free (One on One) Program Customization Coaching Call\n\n![.png)](URL\n\n100% Money Back Guarantee\n-----------------------------\n\nCinnaChroma comes with a 365 Day, 100% Money Back Guarantee. That means if you change your mind about this decision at any point in the next 365 days... all you need to do is contact our US based Customer Service and we will refund your purchase. You can even keep the FREE Blood Sugar Solution Kit bonus as our way of saying \"thanks\" for giving CinnaChroma a try...\n\nWhere To Buy CinnaChroma?\n-----------------------------\n\nThe CinnaChroma supplement is available on the official website only. Nevertheless, you may find these supplements on third-party websites, but there is no guarantee of their quality or efficiency. Therefore, purchasing the CinnaChroma from the official website.\n\nThe Bottom Line\n-------------------\n\nThe natural diabetes plant extracts of Gymnema Sylvestre, berberine, black plum, shilajit, licorice root, guggul, and bitter melon have all received excellent research attention. Interestingly enough, CinnaChroma is the only available natural supplement with all of these ingredients in its formulation. \nThe unique formula of CinnaChroma is what makes it stand out among all other natural diabetes supplements, and this is also the reason behind the immediate results CinnaChroma can produce.\n\n![.png)](URL",
"### Do Not Miss Out On Special Discount At The Official Website Of CinnaChroma\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL\n\nURL"
] |
[
6,
827,
557,
315,
393,
42
] |
[
"passage: TAGS\n#region-us \n",
"passage: ### Click Here – OFFICIAL WEBSITE (CinnaChroma)\n\nWhat Is CinnaChroma?\n------------------------\n\nAccording to the creators of CinnaChroma, this is a brand-new product that promises to control blood pressure and sugar levels. CinnaChroma's makers have made it a point to say that the product is superior to other products currently available. CinnaChroma is said to contain scientifically-proven natural ingredients that regulate blood pressure and high blood pressure. CinnaChroma operates with an action triple that results in immediate results. They believe that CinnaChroma will aid in blood pressure and blood sugar management, in addition to losing weight and reducing weight loss.\n\nHow Does CinnaChroma Work?\n------------------------------\n\nHuman health is at risk from high blood sugar levels. People who have this condition need to find immediate treatment. Due to its effectiveness in maintaining and regulating blood sugar levels, it stands as the best remedy for fluctuating blood sugar levels.\n\nIt’s significant that it contains six potent ingredients that attack the source of diabetes and help to naturally cure it. Users of this formula are not required to give up their favourite foods or alter their lifestyles, according to the manufacturer. Users can lose weight naturally and securely with blood sugar support formula.\n\nBenefits of CinnaChroma Supplement\n--------------------------------------\n\nIf you consume this supplement regularly for three to six months, you’re bound to experience life-changing prostate health benefits such as…\n\nHelp Ease Worries of Diabetic Blindness: The most common cause of blindness in the world is diabetic retinopathy. CinnaChroma, according to Barton Nutrition, can support severe retinal and optic nerve damage, easing concerns about diabetic blindness. Your optic nerves and retina are damaged by high blood sugar levels, but CinnaChroma is said to support these areas of your eyes to reduce concerns about blindness.\n\nEliminate Blurry Vision and Floaters: CinnaChroma allegedly treats eye nerve damage to eliminate floaters and blurry vision. Usually, diabetic retinopathy patients must endure a lifetime of retinal injections, vision loss, and eventual blindness. CinnaChroma shields damaged blood vessels from these issues.\n\nInsulin resistance is the primary cause of Type 2 diabetes, and this supplement works to reverse it in order to keep you healthy.\n\nSupport Healthy Blood Pressure: It exclusively combines the most tried-and-true ingredients in order to support healthy blood pressure that is already within the normal range.\n\nProtect Retina Cells from Diabetes: Retinal cells are impacted by diabetes. Your retina and optic nerve can become damaged by high blood sugar levels over time. CinnaChroma, however, is said to be able to shield your retinal cells from the effects of diabetes.\n\nDrop Blood Sugar Levels by 24%: Diabetics typically use prescription drugs to bring their blood sugar levels down to normal levels. However, CinnaChroma can reduce blood sugar levels by 24%, according to the official Barton Nutrition website. According to the manufacturer, the formula has been \"proven to reduce blood glucose levels dramatically fast\" and can protect retina cells by \"naturally dropping blood sugar levels by 24%.\"\n\nPromote a Healthy Insulin Response: When blood sugar levels increase, your body responds by producing insulin. If you have diabetes, your body reacts to insulin differently from someone who does not. CinnaChroma, however, reportedly helps nitric oxide production, which in turn supports a healthy insulin response. Nitric oxide is essential for the delivery of insulin.\n\n![.png)](URL",
"passage: ### Enjoy The Benefits Of CinnaChroma – Order Now By Clicking Here!\n\nIngredients Used To Manufacture CinnaChroma\n-----------------------------------------------\n\nThe CinnaChroma formula is a potent mix of all-natural ingredients that help in enhancing retinal health. The list of these super-ingredients along with their properties are listed below:\n\nChromium Picolinate : Blood sugar control is another benefit of chromium picolinate. To reduce fasting blood glucose, chromium and picolinate both contribute. Together, they can reduce blood glucose levels by 300% and post-meal glucose levels by 200%. Chromium is guided into the cells of your digestive tract by the picolinate, preventing it from being lost along the way.\n\nVitamin D3 : Another advantageous component that functions as a regulatory hormone is vitamin D3. Vitamin D3 supports glucose uptake into cells and increases insulin release to ensure that your biological processes run as smoothly as possible. Additionally, vitamin D3 has been shown to reduce blood pressure and treat depression. Additionally, it strengthens the immune system. According to Dr. Saunders' advice, the CinnaChroma supplement contains 5000 IUs.\n\nVitamin K2 : Additionally crucial to lowering high blood sugar levels is vitamin K2. It effectively enables the body to use vitamin D3 where it is needed. Along with enhancing your general health, vitamin K2 also aids in reducing inflammation.\n\nVanadium : Vanadium works by transporting blood glucose into your cells where it can be converted to energy. Vanadium and chromium work together to produce amazing effects. It first aids in lowering blood sugar levels and aids in suppressing cravings, enabling weight loss.\n\nSelenium : The final component, selenium, works similarly to vitamin K2 in reducing inflammation. Selenium has also been shown to reduce the risk of cancer by 37%. Additionally, it works wonders for enhancing general wellbeing.\n\nBenfotiamine : This component aids in blood sugar regulation and has been used for many years to treat diabetes-related nerve damage. Additionally, it improves nerve conduction, nerve damage, blood vessel health, and other problems brought on by diabetes. Additionally, this component lessens retinal cell death and shields them from high blood glucose levels.\n\n Visit The Official CinnaChroma Website To Order\n\nHow To Use CinnaChroma For Best Results?\n--------------------------------------------\n\nCinnaChroma comes in capsule form, and every capsule contains a potent blend of 12 natural ingredients rich in vital vitamins, minerals, and nutrients. For best results, three capsules should be taken daily, ideally one capsule after each meal. It is recommended to remain consistent to see CinnaChroma work in full force.\n\nSide Effects Of Using CinnaChroma\n-------------------------------------\n\nCinnaChroma is a pure solution made with the best ingredients. Side effects were rare for those who took the recommended dose. The product was made in an FDA-approved, GMP-certified manufacturing facility.\n\nEach bottle of CinnaChroma is free from any preservatives or herbicides, stimulants or other chemicals that could harm your health. It may take some time but the results you will get are real. CinnaChroma reviews have shown that the ingredients are completely natural and safe. CinnaChroma side effects are therefore minimal.\n\nCinnaChroma Pricing\n-----------------------\n\n_1 Bottle - $59 per bottle - Plus Shipping & Handling_\n\n_3 Bottles - $49 per bottle + Free Shipping in USA + Bonus! Free Digital Book_\n\n_6 Bottles - $39 per bottle + Free Shipping in USA + Bonus! Free Digital Book_\n\n![.png)](URL"
] |
2f0cfa84479732e0131c602b4e5f51f2e610c9de
|
This is a small 30-minute dataset for testing ASR on medical domain, based on this [video lecture](https://www.youtube.com/watch?v=p_8IhrOWRGw).
The manifest file is in NeMo format, "text" is the reference text.
|
bene-ges/asr_med_ru_tuberculosis
|
[
"size_categories:n<1K",
"language:ru",
"license:cc-by-4.0",
"automatic_speech_recognition",
"Speech-to-Text",
"asr",
"medical",
"region:us"
] |
2023-08-29T08:54:05+00:00
|
{"language": ["ru"], "license": "cc-by-4.0", "size_categories": ["n<1K"], "tags": ["automatic_speech_recognition", "Speech-to-Text", "asr", "medical"]}
|
2023-08-29T09:15:44+00:00
|
[] |
[
"ru"
] |
TAGS
#size_categories-n<1K #language-Russian #license-cc-by-4.0 #automatic_speech_recognition #Speech-to-Text #asr #medical #region-us
|
This is a small 30-minute dataset for testing ASR on medical domain, based on this video lecture.
The manifest file is in NeMo format, "text" is the reference text.
|
[] |
[
"TAGS\n#size_categories-n<1K #language-Russian #license-cc-by-4.0 #automatic_speech_recognition #Speech-to-Text #asr #medical #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#size_categories-n<1K #language-Russian #license-cc-by-4.0 #automatic_speech_recognition #Speech-to-Text #asr #medical #region-us \n"
] |
86df7cbe70ed487e145243a9ea8d4537c68abfa9
|
# Dataset Card for "GPT4-10k-standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/GPT4-10k-standardized
|
[
"region:us"
] |
2023-08-29T08:56:56+00:00
|
{"dataset_info": {"features": [{"name": "message", "dtype": "string"}, {"name": "message_type", "dtype": "string"}, {"name": "message_id", "dtype": "int64"}, {"name": "conversation_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4380667, "num_examples": 4052}], "download_size": 2227926, "dataset_size": 4380667}}
|
2023-08-30T19:29:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "GPT4-10k-standardized"
More Information needed
|
[
"# Dataset Card for \"GPT4-10k-standardized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"GPT4-10k-standardized\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"GPT4-10k-standardized\"\n\nMore Information needed"
] |
73aa84bb30d1db032939a992ffbf7717acdae31e
|
# Dataset Card for Evaluation run of RobbeD/Orca-Platypus-3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/RobbeD/Orca-Platypus-3B
- **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 [RobbeD/Orca-Platypus-3B](https://huggingface.co/RobbeD/Orca-Platypus-3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_RobbeD__Orca-Platypus-3B",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T10:07:29.426848](https://huggingface.co/datasets/open-llm-leaderboard/details_RobbeD__Orca-Platypus-3B/blob/main/results_2023-08-29T10%3A07%3A29.426848.json):
```python
{
"all": {
"acc": 0.27366722319077513,
"acc_stderr": 0.03210093803398038,
"acc_norm": 0.2768555704328155,
"acc_norm_stderr": 0.0320995646677269,
"mc1": 0.27539779681762544,
"mc1_stderr": 0.01563813566777552,
"mc2": 0.41928517905056045,
"mc2_stderr": 0.0152672030417133
},
"harness|arc:challenge|25": {
"acc": 0.3993174061433447,
"acc_stderr": 0.014312094557946707,
"acc_norm": 0.4308873720136519,
"acc_norm_stderr": 0.014471133392642476
},
"harness|hellaswag|10": {
"acc": 0.4967138020314678,
"acc_stderr": 0.004989673640014264,
"acc_norm": 0.6532563234415455,
"acc_norm_stderr": 0.004749606196363324
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.040943762699967926,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.040943762699967926
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.28289473684210525,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.28289473684210525,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2792452830188679,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.2792452830188679,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.3055555555555556,
"acc_stderr": 0.03852084696008534,
"acc_norm": 0.3055555555555556,
"acc_norm_stderr": 0.03852084696008534
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.15,
"acc_stderr": 0.035887028128263714,
"acc_norm": 0.15,
"acc_norm_stderr": 0.035887028128263714
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.22,
"acc_stderr": 0.041633319989322695,
"acc_norm": 0.22,
"acc_norm_stderr": 0.041633319989322695
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.23699421965317918,
"acc_stderr": 0.03242414757483098,
"acc_norm": 0.23699421965317918,
"acc_norm_stderr": 0.03242414757483098
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.19607843137254902,
"acc_stderr": 0.03950581861179963,
"acc_norm": 0.19607843137254902,
"acc_norm_stderr": 0.03950581861179963
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.25957446808510637,
"acc_stderr": 0.028659179374292323,
"acc_norm": 0.25957446808510637,
"acc_norm_stderr": 0.028659179374292323
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.21929824561403508,
"acc_stderr": 0.03892431106518754,
"acc_norm": 0.21929824561403508,
"acc_norm_stderr": 0.03892431106518754
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2689655172413793,
"acc_stderr": 0.036951833116502325,
"acc_norm": 0.2689655172413793,
"acc_norm_stderr": 0.036951833116502325
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2566137566137566,
"acc_stderr": 0.022494510767503154,
"acc_norm": 0.2566137566137566,
"acc_norm_stderr": 0.022494510767503154
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.19047619047619047,
"acc_norm_stderr": 0.03512207412302052
},
"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": {
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"acc_stderr": 0.023664216671642535,
"acc_norm": 0.22258064516129034,
"acc_norm_stderr": 0.023664216671642535
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_stderr": 0.029678333141444437,
"acc_norm": 0.2315270935960591,
"acc_norm_stderr": 0.029678333141444437
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_stderr": 0.035886248000917075,
"acc_norm": 0.30303030303030304,
"acc_norm_stderr": 0.035886248000917075
},
"harness|hendrycksTest-high_school_geography|5": {
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"acc_stderr": 0.03173071239071724,
"acc_norm": 0.2727272727272727,
"acc_norm_stderr": 0.03173071239071724
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"acc_stderr": 0.031618779179354094,
"acc_norm": 0.25906735751295334,
"acc_norm_stderr": 0.031618779179354094
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm_stderr": 0.021606294494647727
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"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.25925925925925924,
"acc_norm_stderr": 0.026719240783712163
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.22268907563025211,
"acc_stderr": 0.027025433498882364,
"acc_norm": 0.22268907563025211,
"acc_norm_stderr": 0.027025433498882364
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2781456953642384,
"acc_stderr": 0.03658603262763743,
"acc_norm": 0.2781456953642384,
"acc_norm_stderr": 0.03658603262763743
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm": 0.24403669724770644,
"acc_norm_stderr": 0.01841528635141641
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm_stderr": 0.027696910713093936
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_norm": 0.24019607843137256,
"acc_norm_stderr": 0.02998373305591361
},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm": 0.26582278481012656,
"acc_norm_stderr": 0.028756799629658342
},
"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-human_sexuality|5": {
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"acc_norm": 0.20610687022900764,
"acc_norm_stderr": 0.035477710041594626
},
"harness|hendrycksTest-international_law|5": {
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"acc_norm": 0.4049586776859504,
"acc_norm_stderr": 0.044811377559424694
},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_norm": 0.3055555555555556,
"acc_norm_stderr": 0.044531975073749834
},
"harness|hendrycksTest-logical_fallacies|5": {
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"acc_norm": 0.3128834355828221,
"acc_norm_stderr": 0.036429145782924055
},
"harness|hendrycksTest-machine_learning|5": {
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"acc_norm": 0.19642857142857142,
"acc_norm_stderr": 0.03770970049347018
},
"harness|hendrycksTest-management|5": {
"acc": 0.2524271844660194,
"acc_stderr": 0.04301250399690877,
"acc_norm": 0.2524271844660194,
"acc_norm_stderr": 0.04301250399690877
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.3076923076923077,
"acc_stderr": 0.03023638994217309,
"acc_norm": 0.3076923076923077,
"acc_norm_stderr": 0.03023638994217309
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.26,
"acc_norm_stderr": 0.044084400227680794
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.34738186462324394,
"acc_norm_stderr": 0.01702667174865574
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.30346820809248554,
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"acc_norm": 0.30346820809248554,
"acc_norm_stderr": 0.02475241196091721
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2446927374301676,
"acc_stderr": 0.014378169884098447,
"acc_norm": 0.2446927374301676,
"acc_norm_stderr": 0.014378169884098447
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.28104575163398693,
"acc_stderr": 0.025738854797818723,
"acc_norm": 0.28104575163398693,
"acc_norm_stderr": 0.025738854797818723
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.3633440514469453,
"acc_stderr": 0.027316847674192707,
"acc_norm": 0.3633440514469453,
"acc_norm_stderr": 0.027316847674192707
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.2716049382716049,
"acc_stderr": 0.024748624490537365,
"acc_norm": 0.2716049382716049,
"acc_norm_stderr": 0.024748624490537365
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.2553191489361702,
"acc_stderr": 0.026011992930902006,
"acc_norm": 0.2553191489361702,
"acc_norm_stderr": 0.026011992930902006
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2457627118644068,
"acc_stderr": 0.010996156635142692,
"acc_norm": 0.2457627118644068,
"acc_norm_stderr": 0.010996156635142692
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.16176470588235295,
"acc_stderr": 0.02236867256288675,
"acc_norm": 0.16176470588235295,
"acc_norm_stderr": 0.02236867256288675
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.29411764705882354,
"acc_stderr": 0.018433427649401892,
"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.018433427649401892
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.19090909090909092,
"acc_stderr": 0.03764425585984926,
"acc_norm": 0.19090909090909092,
"acc_norm_stderr": 0.03764425585984926
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.1836734693877551,
"acc_stderr": 0.02478907133200767,
"acc_norm": 0.1836734693877551,
"acc_norm_stderr": 0.02478907133200767
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.31840796019900497,
"acc_stderr": 0.032941184790540944,
"acc_norm": 0.31840796019900497,
"acc_norm_stderr": 0.032941184790540944
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110175,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110175
},
"harness|hendrycksTest-virology|5": {
"acc": 0.30120481927710846,
"acc_stderr": 0.035716092300534796,
"acc_norm": 0.30120481927710846,
"acc_norm_stderr": 0.035716092300534796
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.3391812865497076,
"acc_stderr": 0.03631053496488905,
"acc_norm": 0.3391812865497076,
"acc_norm_stderr": 0.03631053496488905
},
"harness|truthfulqa:mc|0": {
"mc1": 0.27539779681762544,
"mc1_stderr": 0.01563813566777552,
"mc2": 0.41928517905056045,
"mc2_stderr": 0.0152672030417133
}
}
```
### 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_RobbeD__Orca-Platypus-3B
|
[
"region:us"
] |
2023-08-29T09:08:19+00:00
|
{"pretty_name": "Evaluation run of RobbeD/Orca-Platypus-3B", "dataset_summary": "Dataset automatically created during the evaluation run of model [RobbeD/Orca-Platypus-3B](https://huggingface.co/RobbeD/Orca-Platypus-3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 agregated 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_RobbeD__Orca-Platypus-3B\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T10:07:29.426848](https://huggingface.co/datasets/open-llm-leaderboard/details_RobbeD__Orca-Platypus-3B/blob/main/results_2023-08-29T10%3A07%3A29.426848.json):\n\n```python\n{\n \"all\": {\n \"acc\": 0.27366722319077513,\n \"acc_stderr\": 0.03210093803398038,\n \"acc_norm\": 0.2768555704328155,\n \"acc_norm_stderr\": 0.0320995646677269,\n \"mc1\": 0.27539779681762544,\n \"mc1_stderr\": 0.01563813566777552,\n \"mc2\": 0.41928517905056045,\n \"mc2_stderr\": 0.0152672030417133\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.3993174061433447,\n \"acc_stderr\": 0.014312094557946707,\n \"acc_norm\": 0.4308873720136519,\n \"acc_norm_stderr\": 0.014471133392642476\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4967138020314678,\n \"acc_stderr\": 0.004989673640014264,\n \"acc_norm\": 0.6532563234415455,\n \"acc_norm_stderr\": 0.004749606196363324\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.34074074074074073,\n \"acc_stderr\": 0.040943762699967926,\n \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.040943762699967926\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.28289473684210525,\n \"acc_stderr\": 0.03665349695640767,\n \"acc_norm\": 0.28289473684210525,\n \"acc_norm_stderr\": 0.03665349695640767\n },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\": {\n \"acc\": 0.2792452830188679,\n \"acc_stderr\": 0.027611163402399715,\n \"acc_norm\": 0.2792452830188679,\n \"acc_norm_stderr\": 0.027611163402399715\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3055555555555556,\n \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.3055555555555556,\n \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\": 0.22,\n \"acc_stderr\": 0.041633319989322695,\n \"acc_norm\": 0.22,\n \"acc_norm_stderr\": 0.041633319989322695\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.23699421965317918,\n \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.23699421965317918,\n \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179963,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179963\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.25957446808510637,\n \"acc_stderr\": 0.028659179374292323,\n \"acc_norm\": 0.25957446808510637,\n \"acc_norm_stderr\": 0.028659179374292323\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\": 0.2689655172413793,\n \"acc_stderr\": 0.036951833116502325,\n \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.036951833116502325\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2566137566137566,\n \"acc_stderr\": 0.022494510767503154,\n \"acc_norm\": 0.2566137566137566,\n \"acc_norm_stderr\": 0.022494510767503154\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.19047619047619047,\n \"acc_stderr\": 0.03512207412302052,\n \"acc_norm\": 0.19047619047619047,\n \"acc_norm_stderr\": 0.03512207412302052\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\": 0.22258064516129034,\n \"acc_stderr\": 0.023664216671642535,\n \"acc_norm\": 0.22258064516129034,\n \"acc_norm_stderr\": 0.023664216671642535\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2315270935960591,\n \"acc_stderr\": 0.029678333141444437,\n \"acc_norm\": 0.2315270935960591,\n \"acc_norm_stderr\": 0.029678333141444437\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.30303030303030304,\n \"acc_stderr\": 0.035886248000917075,\n \"acc_norm\": 0.30303030303030304,\n \"acc_norm_stderr\": 0.035886248000917075\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.2727272727272727,\n \"acc_stderr\": 0.03173071239071724,\n \"acc_norm\": 0.2727272727272727,\n \"acc_norm_stderr\": 0.03173071239071724\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.25906735751295334,\n \"acc_stderr\": 0.031618779179354094,\n \"acc_norm\": 0.25906735751295334,\n \"acc_norm_stderr\": 0.031618779179354094\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.23846153846153847,\n \"acc_stderr\": 0.021606294494647727,\n \"acc_norm\": 0.23846153846153847,\n \"acc_norm_stderr\": 0.021606294494647727\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712163,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712163\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.22268907563025211,\n \"acc_stderr\": 0.027025433498882364,\n \"acc_norm\": 0.22268907563025211,\n \"acc_norm_stderr\": 0.027025433498882364\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.24403669724770644,\n \"acc_stderr\": 0.01841528635141641,\n \"acc_norm\": 0.24403669724770644,\n \"acc_norm_stderr\": 0.01841528635141641\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.20833333333333334,\n \"acc_stderr\": 0.027696910713093936,\n \"acc_norm\": 0.20833333333333334,\n \"acc_norm_stderr\": 0.027696910713093936\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.24019607843137256,\n \"acc_stderr\": 0.02998373305591361,\n \"acc_norm\": 0.24019607843137256,\n \"acc_norm_stderr\": 0.02998373305591361\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658342,\n \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658342\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.2600896860986547,\n \"acc_stderr\": 0.029442495585857476,\n \"acc_norm\": 0.2600896860986547,\n \"acc_norm_stderr\": 0.029442495585857476\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.20610687022900764,\n \"acc_stderr\": 0.035477710041594626,\n \"acc_norm\": 0.20610687022900764,\n \"acc_norm_stderr\": 0.035477710041594626\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.4049586776859504,\n \"acc_stderr\": 0.044811377559424694,\n \"acc_norm\": 0.4049586776859504,\n \"acc_norm_stderr\": 0.044811377559424694\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\": 0.3128834355828221,\n \"acc_stderr\": 0.036429145782924055,\n \"acc_norm\": 0.3128834355828221,\n \"acc_norm_stderr\": 0.036429145782924055\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.19642857142857142,\n \"acc_stderr\": 0.03770970049347018,\n \"acc_norm\": 0.19642857142857142,\n \"acc_norm_stderr\": 0.03770970049347018\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690877,\n \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690877\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.3076923076923077,\n \"acc_stderr\": 0.03023638994217309,\n \"acc_norm\": 0.3076923076923077,\n \"acc_norm_stderr\": 0.03023638994217309\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.044084400227680794,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.044084400227680794\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.34738186462324394,\n \"acc_stderr\": 0.01702667174865574,\n \"acc_norm\": 0.34738186462324394,\n \"acc_norm_stderr\": 0.01702667174865574\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.30346820809248554,\n \"acc_stderr\": 0.02475241196091721,\n \"acc_norm\": 0.30346820809248554,\n \"acc_norm_stderr\": 0.02475241196091721\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2446927374301676,\n \"acc_stderr\": 0.014378169884098447,\n \"acc_norm\": 0.2446927374301676,\n \"acc_norm_stderr\": 0.014378169884098447\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.28104575163398693,\n \"acc_stderr\": 0.025738854797818723,\n \"acc_norm\": 0.28104575163398693,\n \"acc_norm_stderr\": 0.025738854797818723\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.3633440514469453,\n \"acc_stderr\": 0.027316847674192707,\n \"acc_norm\": 0.3633440514469453,\n \"acc_norm_stderr\": 0.027316847674192707\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2716049382716049,\n \"acc_stderr\": 0.024748624490537365,\n \"acc_norm\": 0.2716049382716049,\n \"acc_norm_stderr\": 0.024748624490537365\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.2553191489361702,\n \"acc_stderr\": 0.026011992930902006,\n \"acc_norm\": 0.2553191489361702,\n \"acc_norm_stderr\": 0.026011992930902006\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.16176470588235295,\n \"acc_stderr\": 0.02236867256288675,\n \"acc_norm\": 0.16176470588235295,\n \"acc_norm_stderr\": 0.02236867256288675\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.018433427649401892,\n \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.018433427649401892\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.19090909090909092,\n \"acc_stderr\": 0.03764425585984926,\n \"acc_norm\": 0.19090909090909092,\n \"acc_norm_stderr\": 0.03764425585984926\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.1836734693877551,\n \"acc_stderr\": 0.02478907133200767,\n \"acc_norm\": 0.1836734693877551,\n \"acc_norm_stderr\": 0.02478907133200767\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.31840796019900497,\n \"acc_stderr\": 0.032941184790540944,\n \"acc_norm\": 0.31840796019900497,\n \"acc_norm_stderr\": 0.032941184790540944\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110175,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110175\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.30120481927710846,\n \"acc_stderr\": 0.035716092300534796,\n \"acc_norm\": 0.30120481927710846,\n \"acc_norm_stderr\": 0.035716092300534796\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3391812865497076,\n \"acc_stderr\": 0.03631053496488905,\n \"acc_norm\": 0.3391812865497076,\n \"acc_norm_stderr\": 0.03631053496488905\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.27539779681762544,\n \"mc1_stderr\": 0.01563813566777552,\n \"mc2\": 0.41928517905056045,\n \"mc2_stderr\": 0.0152672030417133\n }\n}\n```", "repo_url": "https://huggingface.co/RobbeD/Orca-Platypus-3B", "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_08_29T10_07_29.426848", "path": ["**/details_harness|arc:challenge|25_2023-08-29T10:07:29.426848.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T10:07:29.426848.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T10_07_29.426848", "path": ["**/details_harness|hellaswag|10_2023-08-29T10:07:29.426848.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T10:07:29.426848.parquet"]}]}, {"config_name": "harness_hendrycksTest", "data_files": [{"split": "2023_08_29T10_07_29.426848", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-29T10:07:29.426848.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-29T10:07:29.426848.parquet", 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|
2023-08-29T09:09:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of RobbeD/Orca-Platypus-3B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model RobbeD/Orca-Platypus-3B on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T10:07:29.426848:
### 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 RobbeD/Orca-Platypus-3B",
"## 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 RobbeD/Orca-Platypus-3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T10:07:29.426848:",
"### 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 Card for Evaluation run of RobbeD/Orca-Platypus-3B",
"## 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 RobbeD/Orca-Platypus-3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T10:07:29.426848:",
"### 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 RobbeD/Orca-Platypus-3B## 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 RobbeD/Orca-Platypus-3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T10:07:29.426848:### 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"
] |
32773396537fb94fa80cca5375a5f12ba7af6e3d
|
# Dataset Card for Evaluation run of RobbeD/OpenLlama-Platypus-3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/RobbeD/OpenLlama-Platypus-3B
- **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 [RobbeD/OpenLlama-Platypus-3B](https://huggingface.co/RobbeD/OpenLlama-Platypus-3B) 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 agregated 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_RobbeD__OpenLlama-Platypus-3B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-23T06:28:14.000432](https://huggingface.co/datasets/open-llm-leaderboard/details_RobbeD__OpenLlama-Platypus-3B/blob/main/results_2023-09-23T06-28-14.000432.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": {
"em": 0.06145134228187919,
"em_stderr": 0.002459425856611146,
"f1": 0.11012269295302003,
"f1_stderr": 0.002656818706713483,
"acc": 0.3355993065948289,
"acc_stderr": 0.008117942480603072
},
"harness|drop|3": {
"em": 0.06145134228187919,
"em_stderr": 0.002459425856611146,
"f1": 0.11012269295302003,
"f1_stderr": 0.002656818706713483
},
"harness|gsm8k|5": {
"acc": 0.011372251705837756,
"acc_stderr": 0.0029206661987887473
},
"harness|winogrande|5": {
"acc": 0.65982636148382,
"acc_stderr": 0.013315218762417397
}
}
```
### 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_RobbeD__OpenLlama-Platypus-3B
|
[
"region:us"
] |
2023-08-29T09:13:45+00:00
|
{"pretty_name": "Evaluation run of RobbeD/OpenLlama-Platypus-3B", "dataset_summary": "Dataset automatically created during the evaluation run of model [RobbeD/OpenLlama-Platypus-3B](https://huggingface.co/RobbeD/OpenLlama-Platypus-3B) 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 agregated 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_RobbeD__OpenLlama-Platypus-3B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-23T06:28:14.000432](https://huggingface.co/datasets/open-llm-leaderboard/details_RobbeD__OpenLlama-Platypus-3B/blob/main/results_2023-09-23T06-28-14.000432.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 \"em\": 0.06145134228187919,\n \"em_stderr\": 0.002459425856611146,\n \"f1\": 0.11012269295302003,\n \"f1_stderr\": 0.002656818706713483,\n \"acc\": 0.3355993065948289,\n \"acc_stderr\": 0.008117942480603072\n },\n \"harness|drop|3\": {\n \"em\": 0.06145134228187919,\n \"em_stderr\": 0.002459425856611146,\n \"f1\": 0.11012269295302003,\n \"f1_stderr\": 0.002656818706713483\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.011372251705837756,\n \"acc_stderr\": 0.0029206661987887473\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.65982636148382,\n \"acc_stderr\": 0.013315218762417397\n }\n}\n```", "repo_url": "https://huggingface.co/RobbeD/OpenLlama-Platypus-3B", "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_08_29T10_12_53.419020", "path": ["**/details_harness|arc:challenge|25_2023-08-29T10:12:53.419020.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T10:12:53.419020.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_09_23T06_28_14.000432", "path": ["**/details_harness|drop|3_2023-09-23T06-28-14.000432.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-23T06-28-14.000432.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_23T06_28_14.000432", "path": ["**/details_harness|gsm8k|5_2023-09-23T06-28-14.000432.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-23T06-28-14.000432.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T10_12_53.419020", "path": ["**/details_harness|hellaswag|10_2023-08-29T10:12:53.419020.parquet"]}, {"split": "latest", "path": 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2023-09-23T05:28:25+00:00
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TAGS
#region-us
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# Dataset Card for Evaluation run of RobbeD/OpenLlama-Platypus-3B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model RobbeD/OpenLlama-Platypus-3B 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 agregated 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 2023-09-23T06:28:14.000432(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 RobbeD/OpenLlama-Platypus-3B",
"## 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 RobbeD/OpenLlama-Platypus-3B 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 agregated 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 2023-09-23T06:28:14.000432(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 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 RobbeD/OpenLlama-Platypus-3B 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 agregated 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 2023-09-23T06:28:14.000432(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",
"### 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 RobbeD/OpenLlama-Platypus-3B## 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 RobbeD/OpenLlama-Platypus-3B 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 agregated 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 2023-09-23T06:28:14.000432(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"
] |
5efb99122b1d4d29bc70d83dcc0e6b0eebea53be
|
# Dataset Card for Evaluation run of nathan0/mpt_delta_tuned_model_v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nathan0/mpt_delta_tuned_model_v3
- **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 [nathan0/mpt_delta_tuned_model_v3](https://huggingface.co/nathan0/mpt_delta_tuned_model_v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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 agregated 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_nathan0__mpt_delta_tuned_model_v3",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-29T18:53:57.396321](https://huggingface.co/datasets/open-llm-leaderboard/details_nathan0__mpt_delta_tuned_model_v3/blob/main/results_2023-08-29T18%3A53%3A57.396321.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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"acc_stderr": 0.032405505734312466,
"acc_norm": 0.2851491508040904,
"acc_norm_stderr": 0.03239478354615427,
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"mc1_stderr": 0.014948812679062133,
"mc2": 0.35460998683456907,
"mc2_stderr": 0.013780749850644137
},
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"acc_norm_stderr": 0.014610348300255795
},
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},
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"acc_norm_stderr": 0.03502553170678318
},
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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_nathan0__mpt_delta_tuned_model_v3
|
[
"region:us"
] |
2023-08-29T09:14:36+00:00
|
{"pretty_name": "Evaluation run of nathan0/mpt_delta_tuned_model_v3", "dataset_summary": "Dataset automatically created during the evaluation run of model [nathan0/mpt_delta_tuned_model_v3](https://huggingface.co/nathan0/mpt_delta_tuned_model_v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 61 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 agregated 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_nathan0__mpt_delta_tuned_model_v3\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-08-29T18:53:57.396321](https://huggingface.co/datasets/open-llm-leaderboard/details_nathan0__mpt_delta_tuned_model_v3/blob/main/results_2023-08-29T18%3A53%3A57.396321.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\": 0.28112521141201186,\n \"acc_stderr\": 0.032405505734312466,\n \"acc_norm\": 0.2851491508040904,\n \"acc_norm_stderr\": 0.03239478354615427,\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062133,\n \"mc2\": 0.35460998683456907,\n \"mc2_stderr\": 0.013780749850644137\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.454778156996587,\n \"acc_stderr\": 0.014551507060836353,\n \"acc_norm\": 0.5059726962457338,\n \"acc_norm_stderr\": 0.014610348300255795\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5777733519219279,\n \"acc_stderr\": 0.004929048482760455,\n \"acc_norm\": 0.7639912368054173,\n \"acc_norm_stderr\": 0.004237598142007246\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2074074074074074,\n \"acc_stderr\": 0.03502553170678318,\n \"acc_norm\": 0.2074074074074074,\n \"acc_norm_stderr\": 0.03502553170678318\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.2565789473684211,\n \"acc_stderr\": 0.0355418036802569,\n \"acc_norm\": 0.2565789473684211,\n \"acc_norm_stderr\": 0.0355418036802569\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768076,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768076\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.27169811320754716,\n \"acc_stderr\": 0.027377706624670713,\n \"acc_norm\": 0.27169811320754716,\n \"acc_norm_stderr\": 0.027377706624670713\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\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_computer_science|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_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\": 0.2138728323699422,\n \"acc_stderr\": 0.031265112061730424,\n \"acc_norm\": 0.2138728323699422,\n \"acc_norm_stderr\": 0.031265112061730424\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.19607843137254902,\n \"acc_stderr\": 0.03950581861179961,\n \"acc_norm\": 0.19607843137254902,\n \"acc_norm_stderr\": 0.03950581861179961\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\": 0.28085106382978725,\n \"acc_stderr\": 0.029379170464124825,\n \"acc_norm\": 0.28085106382978725,\n \"acc_norm_stderr\": 0.029379170464124825\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n \"acc_stderr\": 0.03999423879281334,\n \"acc_norm\": 0.23684210526315788,\n \"acc_norm_stderr\": 0.03999423879281334\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.2689655172413793,\n \"acc_stderr\": 0.03695183311650232,\n \"acc_norm\": 0.2689655172413793,\n \"acc_norm_stderr\": 0.03695183311650232\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\": 0.035122074123020514,\n \"acc_norm\": 0.19047619047619047,\n \"acc_norm_stderr\": 0.035122074123020514\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.3161290322580645,\n \"acc_stderr\": 0.02645087448904276,\n \"acc_norm\": 0.3161290322580645,\n \"acc_norm_stderr\": 0.02645087448904276\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.2512315270935961,\n \"acc_stderr\": 0.030516530732694433,\n \"acc_norm\": 0.2512315270935961,\n \"acc_norm_stderr\": 0.030516530732694433\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.2545454545454545,\n \"acc_stderr\": 0.03401506715249039,\n \"acc_norm\": 0.2545454545454545,\n \"acc_norm_stderr\": 0.03401506715249039\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.22727272727272727,\n \"acc_stderr\": 0.029857515673386407,\n \"acc_norm\": 0.22727272727272727,\n \"acc_norm_stderr\": 0.029857515673386407\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.30569948186528495,\n \"acc_stderr\": 0.033248379397581594,\n \"acc_norm\": 0.30569948186528495,\n \"acc_norm_stderr\": 0.033248379397581594\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.2948717948717949,\n \"acc_stderr\": 0.023119362758232287,\n \"acc_norm\": 0.2948717948717949,\n \"acc_norm_stderr\": 0.023119362758232287\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25925925925925924,\n \"acc_stderr\": 0.026719240783712177,\n \"acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.026719240783712177\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.25210084033613445,\n \"acc_stderr\": 0.028205545033277733,\n \"acc_norm\": 0.25210084033613445,\n \"acc_norm_stderr\": 0.028205545033277733\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2119205298013245,\n \"acc_stderr\": 0.03336767086567978,\n \"acc_norm\": 0.2119205298013245,\n \"acc_norm_stderr\": 0.03336767086567978\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.27889908256880735,\n \"acc_stderr\": 0.019227468876463514,\n \"acc_norm\": 0.27889908256880735,\n \"acc_norm_stderr\": 0.019227468876463514\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.026491914727355147,\n \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.026491914727355147\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.0309645179269234,\n \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.0309645179269234\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.2742616033755274,\n \"acc_stderr\": 0.029041333510598035,\n \"acc_norm\": 0.2742616033755274,\n \"acc_norm_stderr\": 0.029041333510598035\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.3721973094170404,\n \"acc_stderr\": 0.03244305283008731,\n \"acc_norm\": 0.3721973094170404,\n \"acc_norm_stderr\": 0.03244305283008731\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.2900763358778626,\n \"acc_stderr\": 0.03980066246467765,\n \"acc_norm\": 0.2900763358778626,\n \"acc_norm_stderr\": 0.03980066246467765\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\": 0.32407407407407407,\n \"acc_stderr\": 0.04524596007030049,\n \"acc_norm\": 0.32407407407407407,\n \"acc_norm_stderr\": 0.04524596007030049\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.2392638036809816,\n \"acc_stderr\": 0.03351953879521269,\n \"acc_norm\": 0.2392638036809816,\n \"acc_norm_stderr\": 0.03351953879521269\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n \"acc_stderr\": 0.046355501356099754,\n \"acc_norm\": 0.39285714285714285,\n \"acc_norm_stderr\": 0.046355501356099754\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.2524271844660194,\n \"acc_stderr\": 0.04301250399690878,\n \"acc_norm\": 0.2524271844660194,\n \"acc_norm_stderr\": 0.04301250399690878\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n \"acc_stderr\": 0.029745048572674033,\n \"acc_norm\": 0.2905982905982906,\n \"acc_norm_stderr\": 0.029745048572674033\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.29757343550446996,\n \"acc_stderr\": 0.01634911191290943,\n \"acc_norm\": 0.29757343550446996,\n \"acc_norm_stderr\": 0.01634911191290943\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.2861271676300578,\n \"acc_stderr\": 0.02433214677913413,\n \"acc_norm\": 0.2861271676300578,\n \"acc_norm_stderr\": 0.02433214677913413\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\": 0.28104575163398693,\n \"acc_stderr\": 0.025738854797818702,\n \"acc_norm\": 0.28104575163398693,\n \"acc_norm_stderr\": 0.025738854797818702\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2861736334405145,\n \"acc_stderr\": 0.02567025924218894,\n \"acc_norm\": 0.2861736334405145,\n \"acc_norm_stderr\": 0.02567025924218894\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.2932098765432099,\n \"acc_stderr\": 0.02532988817190092,\n \"acc_norm\": 0.2932098765432099,\n \"acc_norm_stderr\": 0.02532988817190092\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3120567375886525,\n \"acc_stderr\": 0.027640120545169927,\n \"acc_norm\": 0.3120567375886525,\n \"acc_norm_stderr\": 0.027640120545169927\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.26597131681877445,\n \"acc_stderr\": 0.01128503316555127,\n \"acc_norm\": 0.26597131681877445,\n \"acc_norm_stderr\": 0.01128503316555127\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.20220588235294118,\n \"acc_stderr\": 0.02439819298665492,\n \"acc_norm\": 0.20220588235294118,\n \"acc_norm_stderr\": 0.02439819298665492\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.2696078431372549,\n \"acc_stderr\": 0.017952449196987862,\n \"acc_norm\": 0.2696078431372549,\n \"acc_norm_stderr\": 0.017952449196987862\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.35454545454545455,\n \"acc_stderr\": 0.04582004841505416,\n \"acc_norm\": 0.35454545454545455,\n \"acc_norm_stderr\": 0.04582004841505416\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.2897959183673469,\n \"acc_stderr\": 0.02904308868330433,\n \"acc_norm\": 0.2897959183673469,\n \"acc_norm_stderr\": 0.02904308868330433\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.23383084577114427,\n \"acc_stderr\": 0.02992941540834839,\n \"acc_norm\": 0.23383084577114427,\n \"acc_norm_stderr\": 0.02992941540834839\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\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\": 0.30409356725146197,\n \"acc_stderr\": 0.03528211258245232,\n \"acc_norm\": 0.30409356725146197,\n \"acc_norm_stderr\": 0.03528211258245232\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.23990208078335373,\n \"mc1_stderr\": 0.014948812679062133,\n \"mc2\": 0.35460998683456907,\n \"mc2_stderr\": 0.013780749850644137\n }\n}\n```", "repo_url": "https://huggingface.co/nathan0/mpt_delta_tuned_model_v3", "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_08_29T10_14_18.725363", "path": ["**/details_harness|arc:challenge|25_2023-08-29T10:14:18.725363.parquet"]}, {"split": "2023_08_29T18_53_57.396321", "path": ["**/details_harness|arc:challenge|25_2023-08-29T18:53:57.396321.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-08-29T18:53:57.396321.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_08_29T10_14_18.725363", "path": ["**/details_harness|hellaswag|10_2023-08-29T10:14:18.725363.parquet"]}, {"split": "2023_08_29T18_53_57.396321", "path": ["**/details_harness|hellaswag|10_2023-08-29T18:53:57.396321.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-08-29T18:53:57.396321.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_08_29T10_14_18.725363", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-29T10:14:18.725363.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-08-29T10:14:18.725363.parquet", 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"latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-08-29T18:53:57.396321.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_08_29T10_14_18.725363", "path": ["results_2023-08-29T10:14:18.725363.parquet"]}, {"split": "2023_08_29T18_53_57.396321", "path": ["results_2023-08-29T18:53:57.396321.parquet"]}, {"split": "latest", "path": ["results_2023-08-29T18:53:57.396321.parquet"]}]}]}
|
2023-08-29T17:55:12+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of nathan0/mpt_delta_tuned_model_v3
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model nathan0/mpt_delta_tuned_model_v3 on the Open LLM Leaderboard.
The dataset is composed of 61 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 agregated 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 2023-08-29T18:53:57.396321(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 nathan0/mpt_delta_tuned_model_v3",
"## 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 nathan0/mpt_delta_tuned_model_v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T18:53:57.396321(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 nathan0/mpt_delta_tuned_model_v3",
"## 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 nathan0/mpt_delta_tuned_model_v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T18:53:57.396321(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 nathan0/mpt_delta_tuned_model_v3## 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 nathan0/mpt_delta_tuned_model_v3 on the Open LLM Leaderboard.\n\nThe dataset is composed of 61 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 agregated 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 2023-08-29T18:53:57.396321(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"
] |
2d9e37e4089fa03821a131d40c27eeadc41d35ca
|
# Dataset Card for "eu_test3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KatMarie/eu_test3
|
[
"region:us"
] |
2023-08-29T09:15:17+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 302617, "num_examples": 5172}], "download_size": 207896, "dataset_size": 302617}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T09:21:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eu_test3"
More Information needed
|
[
"# Dataset Card for \"eu_test3\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eu_test3\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eu_test3\"\n\nMore Information needed"
] |
b10eedfc23530ebe257a149b00db73c4162fbfc3
|
This is a repository containing a subset of the DISC-Med-SFT Dataset.
Check [DISC-MedLLM](https://github.com/FudanDISC/DISC-MedLLM) for more information.
|
Flmc/DISC-Med-SFT
|
[
"task_categories:question-answering",
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"medical",
"region:us"
] |
2023-08-29T09:20:50+00:00
|
{"language": ["zh"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering", "conversational"], "tags": ["medical"]}
|
2023-08-29T11:54:14+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-question-answering #task_categories-conversational #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #medical #region-us
|
This is a repository containing a subset of the DISC-Med-SFT Dataset.
Check DISC-MedLLM for more information.
|
[] |
[
"TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #medical #region-us \n"
] |
[
56
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-conversational #size_categories-100K<n<1M #language-Chinese #license-apache-2.0 #medical #region-us \n"
] |
be5d85dcd48d7155cf677bbb7f1770eddf2d7f9b
|
# Dataset Card for "eu_test4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KatMarie/eu_test4
|
[
"region:us"
] |
2023-08-29T09:23:20+00:00
|
{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 302617, "num_examples": 5172}], "download_size": 207896, "dataset_size": 302617}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-08-29T09:23:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eu_test4"
More Information needed
|
[
"# Dataset Card for \"eu_test4\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eu_test4\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eu_test4\"\n\nMore Information needed"
] |
709fc736113a755e644727f788e30adc7b96bb11
|
# Dataset Card for "maestro-preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
SneakyInsect/maestro-preprocessed
|
[
"region:us"
] |
2023-08-29T09:31:27+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "start", "sequence": "float64"}, {"name": "duration", "sequence": "float64"}, {"name": "pitch", "sequence": "int64"}, {"name": "velocity", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 559075406, "num_examples": 280573}, {"name": "validation", "num_bytes": 63039151, "num_examples": 31635}, {"name": "test", "num_bytes": 73078316, "num_examples": 36635}], "download_size": 57694069, "dataset_size": 695192873}}
|
2023-09-01T08:28:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "maestro-preprocessed"
More Information needed
|
[
"# Dataset Card for \"maestro-preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"maestro-preprocessed\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"maestro-preprocessed\"\n\nMore Information needed"
] |
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