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77277a5a17faf2acf4790ecaf113508df0e917af
|
# Dataset Card for "deduped-embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
usvsnsp/deduped-embeddings
|
[
"region:us"
] |
2023-09-17T12:22:58+00:00
|
{"dataset_info": {"features": [{"name": "sequence_id", "dtype": "int64"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 11138657220, "num_examples": 7195515}], "download_size": 15591208109, "dataset_size": 11138657220}}
|
2023-09-17T12:33:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "deduped-embeddings"
More Information needed
|
[
"# Dataset Card for \"deduped-embeddings\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"deduped-embeddings\"\n\nMore Information needed"
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6,
17
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"passage: TAGS\n#region-us \n# Dataset Card for \"deduped-embeddings\"\n\nMore Information needed"
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1d6c0d574d919765a27be374cba4ae19d1f1c841
|
# Dataset Card for Evaluation run of Dampish/Dante-2.8B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Dampish/Dante-2.8B
- **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 [Dampish/Dante-2.8B](https://huggingface.co/Dampish/Dante-2.8B) 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 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_Dampish__Dante-2.8B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T13:26:29.842810](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__Dante-2.8B/blob/main/results_2023-09-17T13-26-29.842810.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": 0.00037786091964607033,
"f1": 0.0017051174496644293,
"f1_stderr": 0.00040455681041866965,
"acc": 0.255327545382794,
"acc_stderr": 0.007024647268145198
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.00037786091964607033,
"f1": 0.0017051174496644293,
"f1_stderr": 0.00040455681041866965
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.510655090765588,
"acc_stderr": 0.014049294536290396
}
}
```
### 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_Dampish__Dante-2.8B
|
[
"region:us"
] |
2023-09-17T12:26:33+00:00
|
{"pretty_name": "Evaluation run of Dampish/Dante-2.8B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Dampish/Dante-2.8B](https://huggingface.co/Dampish/Dante-2.8B) 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 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_Dampish__Dante-2.8B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T13:26:29.842810](https://huggingface.co/datasets/open-llm-leaderboard/details_Dampish__Dante-2.8B/blob/main/results_2023-09-17T13-26-29.842810.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\": 0.00037786091964607033,\n \"f1\": 0.0017051174496644293,\n \"f1_stderr\": 0.00040455681041866965,\n \"acc\": 0.255327545382794,\n \"acc_stderr\": 0.007024647268145198\n },\n \"harness|drop|3\": {\n \"em\": 0.001363255033557047,\n \"em_stderr\": 0.00037786091964607033,\n \"f1\": 0.0017051174496644293,\n \"f1_stderr\": 0.00040455681041866965\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.510655090765588,\n \"acc_stderr\": 0.014049294536290396\n }\n}\n```", "repo_url": "https://huggingface.co/Dampish/Dante-2.8B", "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_09_17T13_26_29.842810", "path": ["**/details_harness|drop|3_2023-09-17T13-26-29.842810.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T13-26-29.842810.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T13_26_29.842810", "path": ["**/details_harness|gsm8k|5_2023-09-17T13-26-29.842810.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T13-26-29.842810.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T13_26_29.842810", "path": ["**/details_harness|winogrande|5_2023-09-17T13-26-29.842810.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T13-26-29.842810.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T13_26_29.842810", "path": ["results_2023-09-17T13-26-29.842810.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T13-26-29.842810.parquet"]}]}]}
|
2023-09-17T12:26:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Dampish/Dante-2.8B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Dampish/Dante-2.8B 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 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 2023-09-17T13:26:29.842810(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"## Latest results\n\nThese are the latest results from run 2023-09-17T13:26:29.842810(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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"### 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 Dampish/Dante-2.8B 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 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 2023-09-17T13:26:29.842810(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 Dampish/Dante-2.8B## 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 Dampish/Dante-2.8B 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 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 2023-09-17T13:26:29.842810(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"
] |
168c0db2c0bbaaeae6ff24c3f7e00412833b04fa
|
# Dataset Card for "mailgen"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ecomoptimizer/mailgen
|
[
"region:us"
] |
2023-09-17T12:50:24+00:00
|
{"dataset_info": {"features": [{"name": "product", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "marketing_email", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 201959, "num_examples": 100}], "download_size": 125030, "dataset_size": 201959}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T12:50:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mailgen"
More Information needed
|
[
"# Dataset Card for \"mailgen\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mailgen\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mailgen\"\n\nMore Information needed"
] |
41b6d9288f7595ca79c319d589d3dbbadc8900ae
|
# Dataset Card for "b73eb60b"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/b73eb60b
|
[
"region:us"
] |
2023-09-17T13:04:08+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 206, "num_examples": 10}], "download_size": 1380, "dataset_size": 206}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T13:04:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "b73eb60b"
More Information needed
|
[
"# Dataset Card for \"b73eb60b\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"b73eb60b\"\n\nMore Information needed"
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[
6,
15
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"b73eb60b\"\n\nMore Information needed"
] |
5f86cb8018e008bb941ea95ec21ee50539de54cb
|
# Dataset Card for "qa_wikipedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
legacy107/qa_wikipedia
|
[
"region:us"
] |
2023-09-17T13:24:24+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer_start", "dtype": "int64"}, {"name": "answer", "dtype": "string"}, {"name": "article", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7477859892, "num_examples": 138712}, {"name": "test", "num_bytes": 898641134, "num_examples": 17341}, {"name": "validation", "num_bytes": 926495549, "num_examples": 17291}], "download_size": 498772569, "dataset_size": 9302996575}}
|
2023-09-18T03:37:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "qa_wikipedia"
More Information needed
|
[
"# Dataset Card for \"qa_wikipedia\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"qa_wikipedia\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"qa_wikipedia\"\n\nMore Information needed"
] |
89c0241a92a43f19feee3975818ac3af473caa30
|
# Dataset Card for "PokemonCardsPlus"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
bhavnicksm/PokemonCardsPlus
|
[
"region:us"
] |
2023-09-17T13:35:37+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "card_image", "dtype": "string"}, {"name": "pokemon_image", "dtype": "string"}, {"name": "caption", "dtype": "string"}, {"name": "pokemon_intro", "dtype": "string"}, {"name": "pokedex_text", "dtype": "string"}, {"name": "hp", "dtype": "int64"}, {"name": "set_name", "dtype": "string"}, {"name": "blip_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39075923, "num_examples": 13139}], "download_size": 8210056, "dataset_size": 39075923}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T14:22:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "PokemonCardsPlus"
More Information needed
|
[
"# Dataset Card for \"PokemonCardsPlus\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"PokemonCardsPlus\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"PokemonCardsPlus\"\n\nMore Information needed"
] |
6bcb499cd046a1dc0a43452485de575bb9c74761
|
# Dataset Card for "00dbfb2c"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/00dbfb2c
|
[
"region:us"
] |
2023-09-17T13:41:51+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 240, "num_examples": 10}], "download_size": 1450, "dataset_size": 240}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T13:41:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "00dbfb2c"
More Information needed
|
[
"# Dataset Card for \"00dbfb2c\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"00dbfb2c\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"00dbfb2c\"\n\nMore Information needed"
] |
ab5483ef6cfcf8f9d627db1a7c981c5a81daba02
|
# Dataset Card for Evaluation run of FabbriSimo01/Bloom_1b_Quantized
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized
- **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 [FabbriSimo01/Bloom_1b_Quantized](https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized) 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 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_FabbriSimo01__Bloom_1b_Quantized",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T14:41:55.154995](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Bloom_1b_Quantized/blob/main/results_2023-09-17T14-41-55.154995.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": 0.0016778523489932886,
"em_stderr": 0.00041913301788268413,
"f1": 0.047125629194631036,
"f1_stderr": 0.0012660847237774002,
"acc": 0.27897440899296483,
"acc_stderr": 0.007517237128084831
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788268413,
"f1": 0.047125629194631036,
"f1_stderr": 0.0012660847237774002
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492627
},
"harness|winogrande|5": {
"acc": 0.5564325177584846,
"acc_stderr": 0.0139626949076204
}
}
```
### 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_FabbriSimo01__Bloom_1b_Quantized
|
[
"region:us"
] |
2023-09-17T13:41:58+00:00
|
{"pretty_name": "Evaluation run of FabbriSimo01/Bloom_1b_Quantized", "dataset_summary": "Dataset automatically created during the evaluation run of model [FabbriSimo01/Bloom_1b_Quantized](https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized) 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 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_FabbriSimo01__Bloom_1b_Quantized\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T14:41:55.154995](https://huggingface.co/datasets/open-llm-leaderboard/details_FabbriSimo01__Bloom_1b_Quantized/blob/main/results_2023-09-17T14-41-55.154995.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\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268413,\n \"f1\": 0.047125629194631036,\n \"f1_stderr\": 0.0012660847237774002,\n \"acc\": 0.27897440899296483,\n \"acc_stderr\": 0.007517237128084831\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788268413,\n \"f1\": 0.047125629194631036,\n \"f1_stderr\": 0.0012660847237774002\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \"acc_stderr\": 0.0010717793485492627\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5564325177584846,\n \"acc_stderr\": 0.0139626949076204\n }\n}\n```", "repo_url": "https://huggingface.co/FabbriSimo01/Bloom_1b_Quantized", "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_09_17T14_41_55.154995", "path": ["**/details_harness|drop|3_2023-09-17T14-41-55.154995.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T14-41-55.154995.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T14_41_55.154995", "path": ["**/details_harness|gsm8k|5_2023-09-17T14-41-55.154995.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T14-41-55.154995.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T14_41_55.154995", "path": ["**/details_harness|winogrande|5_2023-09-17T14-41-55.154995.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T14-41-55.154995.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T14_41_55.154995", "path": ["results_2023-09-17T14-41-55.154995.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T14-41-55.154995.parquet"]}]}]}
|
2023-09-17T13:42:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of FabbriSimo01/Bloom_1b_Quantized
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model FabbriSimo01/Bloom_1b_Quantized 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 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 2023-09-17T14:41:55.154995(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 FabbriSimo01/Bloom_1b_Quantized",
"## 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 FabbriSimo01/Bloom_1b_Quantized 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 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 2023-09-17T14:41:55.154995(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 FabbriSimo01/Bloom_1b_Quantized",
"## 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 FabbriSimo01/Bloom_1b_Quantized 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 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 2023-09-17T14:41:55.154995(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 FabbriSimo01/Bloom_1b_Quantized## 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 FabbriSimo01/Bloom_1b_Quantized 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 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 2023-09-17T14:41:55.154995(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"
] |
4e83ec9607b3cf720831e4a5b56575fb43527549
|
# Persian-Text-QA: Lazy Llama 2 Formatting
This is a subset (1k samples) of the [`SeyedAli/Persian-Text-QA`](https://huggingface.co/datasets/SeyedAli/Persian-Text-QA) dataset, processed to match Llama 2's prompt format as described [in this article](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). It was created using the following [colab notebook](https://colab.research.google.com/drive/1Ad7a9zMmkxuXTOh1Z7-rNSICA4dybpM2?usp=sharing).
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for [this article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) about fine-tuning a Llama 2 (chat) model in a Google Colab.
|
hdeldar/Persian-Text-llama2-1k
|
[
"region:us"
] |
2023-09-17T13:47:31+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1830325, "num_examples": 1000}], "download_size": 1841325, "dataset_size": 1830325, "dataset_name": "json"}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/data-*"}]}]}
|
2023-09-17T13:53:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Persian-Text-QA: Lazy Llama 2 Formatting
This is a subset (1k samples) of the 'SeyedAli/Persian-Text-QA' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.
Useful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab.
|
[
"# Persian-Text-QA: Lazy Llama 2 Formatting\n\nThis is a subset (1k samples) of the 'SeyedAli/Persian-Text-QA' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab."
] |
[
"TAGS\n#region-us \n",
"# Persian-Text-QA: Lazy Llama 2 Formatting\n\nThis is a subset (1k samples) of the 'SeyedAli/Persian-Text-QA' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab."
] |
[
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"passage: TAGS\n#region-us \n# Persian-Text-QA: Lazy Llama 2 Formatting\n\nThis is a subset (1k samples) of the 'SeyedAli/Persian-Text-QA' dataset, processed to match Llama 2's prompt format as described in this article. It was created using the following colab notebook.\n\nUseful if you don't want to reformat it by yourself (e.g., using a script). It was designed for this article about fine-tuning a Llama 2 (chat) model in a Google Colab."
] |
5494a4e5f4598cce4cc296975dab2979cdab6f4f
|
# Dataset Card for "department_college_ForFineTune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vincenttttt/department_college_ForFineTune
|
[
"region:us"
] |
2023-09-17T13:52:45+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1719829, "num_examples": 3673}], "download_size": 312305, "dataset_size": 1719829}}
|
2023-09-17T14:23:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "department_college_ForFineTune"
More Information needed
|
[
"# Dataset Card for \"department_college_ForFineTune\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"department_college_ForFineTune\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"department_college_ForFineTune\"\n\nMore Information needed"
] |
63f8831b23522ae6357ac960fd3388f6f23c0d1c
|
# Dataset Card for Evaluation run of jphme/Llama-2-13b-chat-german
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/jphme/Llama-2-13b-chat-german
- **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 [jphme/Llama-2-13b-chat-german](https://huggingface.co/jphme/Llama-2-13b-chat-german) 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 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_jphme__Llama-2-13b-chat-german",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T15:03:11.382260](https://huggingface.co/datasets/open-llm-leaderboard/details_jphme__Llama-2-13b-chat-german/blob/main/results_2023-09-17T15-03-11.382260.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": 0.006606543624161074,
"em_stderr": 0.000829635738992222,
"f1": 0.06547399328859073,
"f1_stderr": 0.0015176277275461638,
"acc": 0.45063287882224046,
"acc_stderr": 0.01068787508123321
},
"harness|drop|3": {
"em": 0.006606543624161074,
"em_stderr": 0.000829635738992222,
"f1": 0.06547399328859073,
"f1_stderr": 0.0015176277275461638
},
"harness|gsm8k|5": {
"acc": 0.13646702047005307,
"acc_stderr": 0.00945574199881554
},
"harness|winogrande|5": {
"acc": 0.7647987371744278,
"acc_stderr": 0.01192000816365088
}
}
```
### 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_jphme__Llama-2-13b-chat-german
|
[
"region:us"
] |
2023-09-17T14:03:15+00:00
|
{"pretty_name": "Evaluation run of jphme/Llama-2-13b-chat-german", "dataset_summary": "Dataset automatically created during the evaluation run of model [jphme/Llama-2-13b-chat-german](https://huggingface.co/jphme/Llama-2-13b-chat-german) 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 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_jphme__Llama-2-13b-chat-german\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T15:03:11.382260](https://huggingface.co/datasets/open-llm-leaderboard/details_jphme__Llama-2-13b-chat-german/blob/main/results_2023-09-17T15-03-11.382260.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\": 0.006606543624161074,\n \"em_stderr\": 0.000829635738992222,\n \"f1\": 0.06547399328859073,\n \"f1_stderr\": 0.0015176277275461638,\n \"acc\": 0.45063287882224046,\n \"acc_stderr\": 0.01068787508123321\n },\n \"harness|drop|3\": {\n \"em\": 0.006606543624161074,\n \"em_stderr\": 0.000829635738992222,\n \"f1\": 0.06547399328859073,\n \"f1_stderr\": 0.0015176277275461638\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.13646702047005307,\n \"acc_stderr\": 0.00945574199881554\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7647987371744278,\n \"acc_stderr\": 0.01192000816365088\n }\n}\n```", "repo_url": "https://huggingface.co/jphme/Llama-2-13b-chat-german", "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_09_17T15_03_11.382260", "path": ["**/details_harness|drop|3_2023-09-17T15-03-11.382260.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T15-03-11.382260.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T15_03_11.382260", "path": ["**/details_harness|gsm8k|5_2023-09-17T15-03-11.382260.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T15-03-11.382260.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T15_03_11.382260", "path": ["**/details_harness|winogrande|5_2023-09-17T15-03-11.382260.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T15-03-11.382260.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T15_03_11.382260", "path": ["results_2023-09-17T15-03-11.382260.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T15-03-11.382260.parquet"]}]}]}
|
2023-09-17T14:03:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of jphme/Llama-2-13b-chat-german
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model jphme/Llama-2-13b-chat-german 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 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 2023-09-17T15:03:11.382260(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 jphme/Llama-2-13b-chat-german",
"## 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 jphme/Llama-2-13b-chat-german 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 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 2023-09-17T15:03:11.382260(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"## 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 jphme/Llama-2-13b-chat-german",
"## 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 jphme/Llama-2-13b-chat-german 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 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 2023-09-17T15:03:11.382260(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of jphme/Llama-2-13b-chat-german## 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 jphme/Llama-2-13b-chat-german 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 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 2023-09-17T15:03:11.382260(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"
] |
2113d0425bbbf57364a4483c28ad02540178931b
|
# Dataset Card for Evaluation run of chavinlo/alpaca-native
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/chavinlo/alpaca-native
- **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 [chavinlo/alpaca-native](https://huggingface.co/chavinlo/alpaca-native) 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_chavinlo__alpaca-native",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-21T20:23:20.255556](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__alpaca-native/blob/main/results_2023-09-21T20-23-20.255556.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": 0.035302205782678654,
"acc_norm": 0.42235476219088836,
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"mc1_stderr": 0.015127427096520674,
"mc2": 0.3759916250814691,
"mc2_stderr": 0.015396201572279763
},
"harness|arc:challenge|25": {
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"acc_stderr": 0.014606603181012538,
"acc_norm": 0.5204778156996587,
"acc_norm_stderr": 0.01459913135303501
},
"harness|hellaswag|10": {
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"acc_norm": 0.7699661422027485,
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},
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},
"harness|hendrycksTest-anatomy|5": {
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},
"harness|hendrycksTest-astronomy|5": {
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"acc_stderr": 0.03910525752849724,
"acc_norm": 0.3618421052631579,
"acc_norm_stderr": 0.03910525752849724
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.46,
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"acc_norm_stderr": 0.014333522059217889
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.028180596328259293,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.028180596328259293
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.4662379421221865,
"acc_stderr": 0.028333277109562793,
"acc_norm": 0.4662379421221865,
"acc_norm_stderr": 0.028333277109562793
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.4722222222222222,
"acc_stderr": 0.027777777777777804,
"acc_norm": 0.4722222222222222,
"acc_norm_stderr": 0.027777777777777804
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.30851063829787234,
"acc_stderr": 0.027553366165101362,
"acc_norm": 0.30851063829787234,
"acc_norm_stderr": 0.027553366165101362
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.3213820078226858,
"acc_stderr": 0.011927581352265076,
"acc_norm": 0.3213820078226858,
"acc_norm_stderr": 0.011927581352265076
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.40441176470588236,
"acc_stderr": 0.029812630701569743,
"acc_norm": 0.40441176470588236,
"acc_norm_stderr": 0.029812630701569743
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.3790849673202614,
"acc_stderr": 0.019627444748412232,
"acc_norm": 0.3790849673202614,
"acc_norm_stderr": 0.019627444748412232
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.44545454545454544,
"acc_stderr": 0.047605488214603246,
"acc_norm": 0.44545454545454544,
"acc_norm_stderr": 0.047605488214603246
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.40408163265306124,
"acc_stderr": 0.031414708025865885,
"acc_norm": 0.40408163265306124,
"acc_norm_stderr": 0.031414708025865885
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.472636815920398,
"acc_stderr": 0.03530235517334682,
"acc_norm": 0.472636815920398,
"acc_norm_stderr": 0.03530235517334682
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-virology|5": {
"acc": 0.4036144578313253,
"acc_stderr": 0.038194861407583984,
"acc_norm": 0.4036144578313253,
"acc_norm_stderr": 0.038194861407583984
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.03829509868994727,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.03829509868994727
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2484700122399021,
"mc1_stderr": 0.015127427096520674,
"mc2": 0.3759916250814691,
"mc2_stderr": 0.015396201572279763
}
}
```
### 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_chavinlo__alpaca-native
|
[
"region:us"
] |
2023-09-17T14:14:52+00:00
|
{"pretty_name": "Evaluation run of chavinlo/alpaca-native", "dataset_summary": "Dataset automatically created during the evaluation run of model [chavinlo/alpaca-native](https://huggingface.co/chavinlo/alpaca-native) 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_chavinlo__alpaca-native\",\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-21T20:23:20.255556](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__alpaca-native/blob/main/results_2023-09-21T20-23-20.255556.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\": 0.41927597389078103,\n \"acc_stderr\": 0.035302205782678654,\n \"acc_norm\": 0.42235476219088836,\n \"acc_norm_stderr\": 0.035290265393035695,\n \"mc1\": 0.2484700122399021,\n \"mc1_stderr\": 0.015127427096520674,\n \"mc2\": 0.3759916250814691,\n \"mc2_stderr\": 0.015396201572279763\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5127986348122867,\n \"acc_stderr\": 0.014606603181012538,\n \"acc_norm\": 0.5204778156996587,\n \"acc_norm_stderr\": 0.01459913135303501\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5959968133837881,\n \"acc_stderr\": 0.004896952378506926,\n \"acc_norm\": 0.7699661422027485,\n \"acc_norm_stderr\": 0.004199941217549452\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542129,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542129\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n \"acc_stderr\": 0.04304979692464242,\n \"acc_norm\": 0.45925925925925926,\n \"acc_norm_stderr\": 0.04304979692464242\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.3618421052631579,\n \"acc_stderr\": 0.03910525752849724,\n \"acc_norm\": 0.3618421052631579,\n \"acc_norm_stderr\": 0.03910525752849724\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.44150943396226416,\n \"acc_stderr\": 0.030561590426731837,\n \"acc_norm\": 0.44150943396226416,\n \"acc_norm_stderr\": 0.030561590426731837\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3819444444444444,\n \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.3819444444444444,\n \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3815028901734104,\n \"acc_stderr\": 0.03703851193099521,\n \"acc_norm\": 0.3815028901734104,\n \"acc_norm_stderr\": 0.03703851193099521\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n \"acc_stderr\": 0.04092563958237656,\n \"acc_norm\": 0.21568627450980393,\n \"acc_norm_stderr\": 0.04092563958237656\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.37446808510638296,\n \"acc_stderr\": 0.03163910665367291,\n \"acc_norm\": 0.37446808510638296,\n \"acc_norm_stderr\": 0.03163910665367291\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2543859649122807,\n \"acc_stderr\": 0.040969851398436716,\n \"acc_norm\": 0.2543859649122807,\n \"acc_norm_stderr\": 0.040969851398436716\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.36551724137931035,\n \"acc_stderr\": 0.040131241954243856,\n \"acc_norm\": 0.36551724137931035,\n \"acc_norm_stderr\": 0.040131241954243856\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.28835978835978837,\n \"acc_stderr\": 0.023330654054535903,\n \"acc_norm\": 0.28835978835978837,\n \"acc_norm_stderr\": 0.023330654054535903\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.24603174603174602,\n \"acc_stderr\": 0.03852273364924314,\n \"acc_norm\": 0.24603174603174602,\n \"acc_norm_stderr\": 0.03852273364924314\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\": 0.4290322580645161,\n \"acc_stderr\": 0.02815603653823321,\n \"acc_norm\": 0.4290322580645161,\n \"acc_norm_stderr\": 0.02815603653823321\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3103448275862069,\n \"acc_stderr\": 0.03255086769970103,\n \"acc_norm\": 0.3103448275862069,\n \"acc_norm_stderr\": 0.03255086769970103\n },\n \"harness|hendrycksTest-high_school_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-high_school_european_history|5\": {\n \"acc\": 0.5333333333333333,\n \"acc_stderr\": 0.038956580652718446,\n \"acc_norm\": 0.5333333333333333,\n \"acc_norm_stderr\": 0.038956580652718446\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.4797979797979798,\n \"acc_stderr\": 0.035594435655639196,\n \"acc_norm\": 0.4797979797979798,\n \"acc_norm_stderr\": 0.035594435655639196\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6062176165803109,\n \"acc_stderr\": 0.035260770955482405,\n \"acc_norm\": 0.6062176165803109,\n \"acc_norm_stderr\": 0.035260770955482405\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.3871794871794872,\n \"acc_stderr\": 0.024697216930878948,\n \"acc_norm\": 0.3871794871794872,\n \"acc_norm_stderr\": 0.024697216930878948\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.031041941304059295,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.031041941304059295\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.31125827814569534,\n \"acc_stderr\": 0.03780445850526733,\n \"acc_norm\": 0.31125827814569534,\n \"acc_norm_stderr\": 0.03780445850526733\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.544954128440367,\n \"acc_stderr\": 0.021350503090925173,\n \"acc_norm\": 0.544954128440367,\n \"acc_norm_stderr\": 0.021350503090925173\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5343137254901961,\n \"acc_stderr\": 0.03501038327635897,\n \"acc_norm\": 0.5343137254901961,\n \"acc_norm_stderr\": 0.03501038327635897\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5654008438818565,\n \"acc_stderr\": 0.03226759995510145,\n \"acc_norm\": 0.5654008438818565,\n \"acc_norm_stderr\": 0.03226759995510145\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5022421524663677,\n \"acc_stderr\": 0.03355746535223263,\n \"acc_norm\": 0.5022421524663677,\n \"acc_norm_stderr\": 0.03355746535223263\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.4122137404580153,\n \"acc_stderr\": 0.04317171194870254,\n \"acc_norm\": 0.4122137404580153,\n \"acc_norm_stderr\": 0.04317171194870254\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.5454545454545454,\n \"acc_stderr\": 0.045454545454545484,\n \"acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.045454545454545484\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.4444444444444444,\n \"acc_stderr\": 0.04803752235190192,\n \"acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.04803752235190192\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.3987730061349693,\n \"acc_stderr\": 0.03847021420456025,\n \"acc_norm\": 0.3987730061349693,\n \"acc_norm_stderr\": 0.03847021420456025\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\": 0.47572815533980584,\n \"acc_stderr\": 0.049449010929737795,\n \"acc_norm\": 0.47572815533980584,\n \"acc_norm_stderr\": 0.049449010929737795\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6068376068376068,\n \"acc_stderr\": 0.03199957924651047,\n \"acc_norm\": 0.6068376068376068,\n \"acc_norm_stderr\": 0.03199957924651047\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.5504469987228607,\n \"acc_stderr\": 0.017788725283507337,\n \"acc_norm\": 0.5504469987228607,\n \"acc_norm_stderr\": 0.017788725283507337\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.42485549132947975,\n \"acc_stderr\": 0.026613350840261736,\n \"acc_norm\": 0.42485549132947975,\n \"acc_norm_stderr\": 0.026613350840261736\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2424581005586592,\n \"acc_stderr\": 0.014333522059217889,\n \"acc_norm\": 0.2424581005586592,\n \"acc_norm_stderr\": 0.014333522059217889\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.028180596328259293,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.028180596328259293\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.4662379421221865,\n \"acc_stderr\": 0.028333277109562793,\n \"acc_norm\": 0.4662379421221865,\n \"acc_norm_stderr\": 0.028333277109562793\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4722222222222222,\n \"acc_stderr\": 0.027777777777777804,\n \"acc_norm\": 0.4722222222222222,\n \"acc_norm_stderr\": 0.027777777777777804\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.30851063829787234,\n \"acc_stderr\": 0.027553366165101362,\n \"acc_norm\": 0.30851063829787234,\n \"acc_norm_stderr\": 0.027553366165101362\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3213820078226858,\n \"acc_stderr\": 0.011927581352265076,\n \"acc_norm\": 0.3213820078226858,\n \"acc_norm_stderr\": 0.011927581352265076\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.40441176470588236,\n \"acc_stderr\": 0.029812630701569743,\n \"acc_norm\": 0.40441176470588236,\n \"acc_norm_stderr\": 0.029812630701569743\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.3790849673202614,\n \"acc_stderr\": 0.019627444748412232,\n \"acc_norm\": 0.3790849673202614,\n \"acc_norm_stderr\": 0.019627444748412232\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.44545454545454544,\n \"acc_stderr\": 0.047605488214603246,\n \"acc_norm\": 0.44545454545454544,\n \"acc_norm_stderr\": 0.047605488214603246\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.40408163265306124,\n \"acc_stderr\": 0.031414708025865885,\n \"acc_norm\": 0.40408163265306124,\n \"acc_norm_stderr\": 0.031414708025865885\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.472636815920398,\n \"acc_stderr\": 0.03530235517334682,\n \"acc_norm\": 0.472636815920398,\n \"acc_norm_stderr\": 0.03530235517334682\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.4036144578313253,\n \"acc_stderr\": 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|
2023-09-21T19:24:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of chavinlo/alpaca-native
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model chavinlo/alpaca-native 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 2023-09-21T20:23:20.255556(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 chavinlo/alpaca-native",
"## 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 chavinlo/alpaca-native 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 2023-09-21T20:23:20.255556(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 chavinlo/alpaca-native",
"## 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 chavinlo/alpaca-native 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 2023-09-21T20:23:20.255556(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,
18,
31,
166,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of chavinlo/alpaca-native## 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 chavinlo/alpaca-native 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 2023-09-21T20:23:20.255556(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"
] |
888cfce3d8049fdc3c0dded9f2a819bc09e05f64
|
# Dataset Card for ANTCorpus v2.1
## Dataset Description
- **Homepage:** https://antcorpus.github.io/
- **Point of Contact:** [email protected]
### Dataset Summary
ANTCorpus v2.1 (31 525 articles with multi-source Arabic news websites)
### Supported Tasks and Leaderboards
Text classification and summarization.
### Languages
Arabic
### Licensing Information
By downloading ANT Corpus, you agree to cite at least one of our papers describing ANT Corpus and/or refer the project's main page in any kind of material you produce where ANT Corpus was used to conduct search or experimentation, whether be it a research paper, dissertation, article, poster, presentation, or documentation.
📄 A. Chouigui, O. Ben Khiroun, and B. Elayeb. An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization. In Arabian Journal for Science and Engineering (AJSE 2021), 46(08), 1-14, DOI : 10.1007/s13369-020-05258-z , February 2021.
📄 A. Chouigui, O. Ben Khiroun, and B. Elayeb. ANT Corpus : An Arabic News Text Collection for Textual Classification. In proceedings of the 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), pp. 135-142, Hammamet, Tunisia, October 30 - November 3, 2017.
📄 A. Chouigui, O. Ben Khiroun, and B. Elayeb. A TF-IDF and Co-occurrence Based Approach for Events Extraction from Arabic News Corpus. In proceedings of the 23rd International Conference on Natural Language & Information Systems (NLDB 2018), pp. 272-280, Paris, France, 13-15 June 2018.
📄 A. Chouigui, O. Ben Khiroun and B. Elayeb. Related Terms Extraction from Arabic News Corpus using Word Embedding. In: OTM Conferences & Workshops: Proceedings of the 7th International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS'18), Springer, LNCS, pp. 1-11, Valletta, Malta, 22-26 October 2018.
|
Amina-Chouigui/ANTCorpusv2.1
|
[
"task_categories:text-classification",
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ar",
"region:us"
] |
2023-09-17T14:38:28+00:00
|
{"language": ["ar"], "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "summarization"]}
|
2023-09-17T15:19:02+00:00
|
[] |
[
"ar"
] |
TAGS
#task_categories-text-classification #task_categories-summarization #size_categories-10K<n<100K #language-Arabic #region-us
|
# Dataset Card for ANTCorpus v2.1
## Dataset Description
- Homepage: URL
- Point of Contact: aminachouigui@URL
### Dataset Summary
ANTCorpus v2.1 (31 525 articles with multi-source Arabic news websites)
### Supported Tasks and Leaderboards
Text classification and summarization.
### Languages
Arabic
### Licensing Information
By downloading ANT Corpus, you agree to cite at least one of our papers describing ANT Corpus and/or refer the project's main page in any kind of material you produce where ANT Corpus was used to conduct search or experimentation, whether be it a research paper, dissertation, article, poster, presentation, or documentation.
A. Chouigui, O. Ben Khiroun, and B. Elayeb. An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization. In Arabian Journal for Science and Engineering (AJSE 2021), 46(08), 1-14, DOI : 10.1007/s13369-020-05258-z , February 2021.
A. Chouigui, O. Ben Khiroun, and B. Elayeb. ANT Corpus : An Arabic News Text Collection for Textual Classification. In proceedings of the 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), pp. 135-142, Hammamet, Tunisia, October 30 - November 3, 2017.
A. Chouigui, O. Ben Khiroun, and B. Elayeb. A TF-IDF and Co-occurrence Based Approach for Events Extraction from Arabic News Corpus. In proceedings of the 23rd International Conference on Natural Language & Information Systems (NLDB 2018), pp. 272-280, Paris, France, 13-15 June 2018.
A. Chouigui, O. Ben Khiroun and B. Elayeb. Related Terms Extraction from Arabic News Corpus using Word Embedding. In: OTM Conferences & Workshops: Proceedings of the 7th International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS'18), Springer, LNCS, pp. 1-11, Valletta, Malta, 22-26 October 2018.
|
[
"# Dataset Card for ANTCorpus v2.1",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: aminachouigui@URL",
"### Dataset Summary\n\nANTCorpus v2.1 (31 525 articles with multi-source Arabic news websites)",
"### Supported Tasks and Leaderboards\n\nText classification and summarization.",
"### Languages\n\nArabic",
"### Licensing Information\n\nBy downloading ANT Corpus, you agree to cite at least one of our papers describing ANT Corpus and/or refer the project's main page in any kind of material you produce where ANT Corpus was used to conduct search or experimentation, whether be it a research paper, dissertation, article, poster, presentation, or documentation.\n\n A. Chouigui, O. Ben Khiroun, and B. Elayeb. An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization. In Arabian Journal for Science and Engineering (AJSE 2021), 46(08), 1-14, DOI : 10.1007/s13369-020-05258-z , February 2021.\n\n A. Chouigui, O. Ben Khiroun, and B. Elayeb. ANT Corpus : An Arabic News Text Collection for Textual Classification. In proceedings of the 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), pp. 135-142, Hammamet, Tunisia, October 30 - November 3, 2017.\n\n A. Chouigui, O. Ben Khiroun, and B. Elayeb. A TF-IDF and Co-occurrence Based Approach for Events Extraction from Arabic News Corpus. In proceedings of the 23rd International Conference on Natural Language & Information Systems (NLDB 2018), pp. 272-280, Paris, France, 13-15 June 2018.\n\n A. Chouigui, O. Ben Khiroun and B. Elayeb. Related Terms Extraction from Arabic News Corpus using Word Embedding. In: OTM Conferences & Workshops: Proceedings of the 7th International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS'18), Springer, LNCS, pp. 1-11, Valletta, Malta, 22-26 October 2018."
] |
[
"TAGS\n#task_categories-text-classification #task_categories-summarization #size_categories-10K<n<100K #language-Arabic #region-us \n",
"# Dataset Card for ANTCorpus v2.1",
"## Dataset Description\n\n- Homepage: URL\n- Point of Contact: aminachouigui@URL",
"### Dataset Summary\n\nANTCorpus v2.1 (31 525 articles with multi-source Arabic news websites)",
"### Supported Tasks and Leaderboards\n\nText classification and summarization.",
"### Languages\n\nArabic",
"### Licensing Information\n\nBy downloading ANT Corpus, you agree to cite at least one of our papers describing ANT Corpus and/or refer the project's main page in any kind of material you produce where ANT Corpus was used to conduct search or experimentation, whether be it a research paper, dissertation, article, poster, presentation, or documentation.\n\n A. Chouigui, O. Ben Khiroun, and B. Elayeb. An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization. In Arabian Journal for Science and Engineering (AJSE 2021), 46(08), 1-14, DOI : 10.1007/s13369-020-05258-z , February 2021.\n\n A. Chouigui, O. Ben Khiroun, and B. Elayeb. ANT Corpus : An Arabic News Text Collection for Textual Classification. In proceedings of the 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), pp. 135-142, Hammamet, Tunisia, October 30 - November 3, 2017.\n\n A. Chouigui, O. Ben Khiroun, and B. Elayeb. A TF-IDF and Co-occurrence Based Approach for Events Extraction from Arabic News Corpus. In proceedings of the 23rd International Conference on Natural Language & Information Systems (NLDB 2018), pp. 272-280, Paris, France, 13-15 June 2018.\n\n A. Chouigui, O. Ben Khiroun and B. Elayeb. Related Terms Extraction from Arabic News Corpus using Word Embedding. In: OTM Conferences & Workshops: Proceedings of the 7th International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS'18), Springer, LNCS, pp. 1-11, Valletta, Malta, 22-26 October 2018."
] |
[
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18,
5,
433
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-summarization #size_categories-10K<n<100K #language-Arabic #region-us \n# Dataset Card for ANTCorpus v2.1## Dataset Description\n\n- Homepage: URL\n- Point of Contact: aminachouigui@URL### Dataset Summary\n\nANTCorpus v2.1 (31 525 articles with multi-source Arabic news websites)### Supported Tasks and Leaderboards\n\nText classification and summarization.### Languages\n\nArabic"
] |
328f4a060d13425cfb09eb7a3e05b0982189a70b
|
# Dataset Card for "python_codestyles-random-500"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains `500` completely
different code styles. The code styles differ in at least one codestyle rule, which is called a `random` codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|:-----------------------------------------------------------------------:|:----------------------------------------:|
| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
You can find the corresponding code styles of the examples in the file [additional_data.json](additional_data.json).
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns `code_codestyle` and `style_context_codestyle` in the dataset.
There are 182.198 samples in total and 91.098 positive and 91.100 negative samples.
|
infinityofspace/python_codestyles-random-500
|
[
"size_categories:100K<n<1M",
"license:mit",
"python",
"code-style",
"random",
"doi:10.57967/hf/1229",
"region:us"
] |
2023-09-17T15:03:23+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "code_codestyle", "dtype": "int64"}, {"name": "style_context", "dtype": "string"}, {"name": "style_context_codestyle", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1805574493, "num_examples": 153999}, {"name": "test", "num_bytes": 329414314, "num_examples": 28199}], "download_size": 334063771, "dataset_size": 2134988807}, "tags": ["python", "code-style", "random"]}
|
2023-10-18T19:38:17+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #license-mit #python #code-style #random #doi-10.57967/hf/1229 #region-us
|
Dataset Card for "python\_codestyles-random-500"
================================================
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains '500' completely
different code styles. The code styles differ in at least one codestyle rule, which is called a 'random' codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
You can find the corresponding code styles of the examples in the file additional\_data.json.
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns 'code\_codestyle' and 'style\_context\_codestyle' in the dataset.
There are 182.198 samples in total and 91.098 positive and 91.100 negative samples.
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #random #doi-10.57967/hf/1229 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #random #doi-10.57967/hf/1229 #region-us \n"
] |
eddfdcabb0d028fc893cea8f3882607d3a599d5a
|
# Dataset Card for Evaluation run of rinna/bilingual-gpt-neox-4b-8k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k
- **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 [rinna/bilingual-gpt-neox-4b-8k](https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k) 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 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_rinna__bilingual-gpt-neox-4b-8k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T16:04:27.636437](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__bilingual-gpt-neox-4b-8k/blob/main/results_2023-09-17T16-04-27.636437.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0030935402684563753,
"f1_stderr": 0.00040549722152617045,
"acc": 0.23993685872138912,
"acc_stderr": 0.007020548332172165
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0030935402684563753,
"f1_stderr": 0.00040549722152617045
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.47987371744277824,
"acc_stderr": 0.01404109666434433
}
}
```
### 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_rinna__bilingual-gpt-neox-4b-8k
|
[
"region:us"
] |
2023-09-17T15:04:32+00:00
|
{"pretty_name": "Evaluation run of rinna/bilingual-gpt-neox-4b-8k", "dataset_summary": "Dataset automatically created during the evaluation run of model [rinna/bilingual-gpt-neox-4b-8k](https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k) 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 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_rinna__bilingual-gpt-neox-4b-8k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T16:04:27.636437](https://huggingface.co/datasets/open-llm-leaderboard/details_rinna__bilingual-gpt-neox-4b-8k/blob/main/results_2023-09-17T16-04-27.636437.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\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0030935402684563753,\n \"f1_stderr\": 0.00040549722152617045,\n \"acc\": 0.23993685872138912,\n \"acc_stderr\": 0.007020548332172165\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0030935402684563753,\n \"f1_stderr\": 0.00040549722152617045\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.47987371744277824,\n \"acc_stderr\": 0.01404109666434433\n }\n}\n```", "repo_url": "https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k", "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_09_17T16_04_27.636437", "path": ["**/details_harness|drop|3_2023-09-17T16-04-27.636437.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T16-04-27.636437.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T16_04_27.636437", "path": ["**/details_harness|gsm8k|5_2023-09-17T16-04-27.636437.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T16-04-27.636437.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T16_04_27.636437", "path": ["**/details_harness|winogrande|5_2023-09-17T16-04-27.636437.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T16-04-27.636437.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T16_04_27.636437", "path": ["results_2023-09-17T16-04-27.636437.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T16-04-27.636437.parquet"]}]}]}
|
2023-09-17T15:04:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of rinna/bilingual-gpt-neox-4b-8k
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model rinna/bilingual-gpt-neox-4b-8k 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 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 2023-09-17T16:04:27.636437(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 rinna/bilingual-gpt-neox-4b-8k",
"## 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 rinna/bilingual-gpt-neox-4b-8k 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 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 2023-09-17T16:04:27.636437(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 rinna/bilingual-gpt-neox-4b-8k",
"## 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 rinna/bilingual-gpt-neox-4b-8k 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 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 2023-09-17T16:04:27.636437(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,
24,
31,
172,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of rinna/bilingual-gpt-neox-4b-8k## 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 rinna/bilingual-gpt-neox-4b-8k 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 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 2023-09-17T16:04:27.636437(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"
] |
fc92b6c1bd1da7350f23d22581b4684c646c32bb
|
# Dataset Card for "imdb-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
asun17904/imdb-test
|
[
"region:us"
] |
2023-09-17T15:15:03+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}], "splits": [{"name": "test", "num_bytes": 19590411.0, "num_examples": 15000}], "download_size": 12828803, "dataset_size": 19590411.0}}
|
2023-09-17T15:15:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "imdb-test"
More Information needed
|
[
"# Dataset Card for \"imdb-test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"imdb-test\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"imdb-test\"\n\nMore Information needed"
] |
cd20e5e223219f1cd7a11e7a0d75df08613a64bb
|
# Dataset Card for "marques"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
juanluisrto/marques
|
[
"region:us"
] |
2023-09-17T15:27:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 607598, "num_examples": 289}], "download_size": 283004, "dataset_size": 607598}}
|
2023-09-17T21:02:47+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "marques"
More Information needed
|
[
"# Dataset Card for \"marques\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"marques\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"marques\"\n\nMore Information needed"
] |
f4b5f62b10a4ac281352ce36d363887351055cd8
|
# Dataset Card for "python_codestyles-single-500"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains `500` completely
different code styles. The code styles differ in exactly one codestyle rule, which is called a `single` codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|:-----------------------------------------------------------------------:|:----------------------------------------:|
| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
You can find the corresponding code styles of the examples in the file [additional_data.json](additional_data.json).
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns `code_codestyle` and `style_context_codestyle` in the dataset.
There are 182.184 samples in total and 91.084 positive and 91.100 negative samples.
|
infinityofspace/python_codestyles-single-500
|
[
"size_categories:100K<n<1M",
"license:mit",
"python",
"code-style",
"single",
"doi:10.57967/hf/1230",
"region:us"
] |
2023-09-17T15:33:51+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "code_codestyle", "dtype": "int64"}, {"name": "style_context", "dtype": "string"}, {"name": "style_context_codestyle", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1784386100, "num_examples": 153991}, {"name": "test", "num_bytes": 323920285, "num_examples": 28193}], "download_size": 320183832, "dataset_size": 2108306385}, "tags": ["python", "code-style", "single"]}
|
2023-10-18T19:41:06+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #license-mit #python #code-style #single #doi-10.57967/hf/1230 #region-us
|
Dataset Card for "python\_codestyles-single-500"
================================================
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains '500' completely
different code styles. The code styles differ in exactly one codestyle rule, which is called a 'single' codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
You can find the corresponding code styles of the examples in the file additional\_data.json.
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns 'code\_codestyle' and 'style\_context\_codestyle' in the dataset.
There are 182.184 samples in total and 91.084 positive and 91.100 negative samples.
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #single #doi-10.57967/hf/1230 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #single #doi-10.57967/hf/1230 #region-us \n"
] |
8fccf5e633b59b28e71bad0a0ce6aaca3aceb0f8
|
# Dataset Card for "lens_vqa_sample_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
llm-lens/lens_vqa_sample_test
|
[
"region:us"
] |
2023-09-17T15:37:44+00:00
|
{"dataset_info": {"features": [{"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "id_image", "dtype": "int64"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "intensive_captions_Salesforce-blip-image-captioning-large", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 1601792.0, "num_examples": 10}], "download_size": 1595850, "dataset_size": 1601792.0}}
|
2023-09-17T16:14:49+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "lens_vqa_sample_test"
More Information needed
|
[
"# Dataset Card for \"lens_vqa_sample_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"lens_vqa_sample_test\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"lens_vqa_sample_test\"\n\nMore Information needed"
] |
c6288c22af69dd286002c773fcde57c9c8689ab4
|
[](https://doi.org/10.5281/zenodo.1214456)
# 100,000 histological images of human colorectal cancer and healthy tissue
**Homepage**: https://zenodo.org/record/1214456 \
**Publication Date**: 2018-04-07 \
**License**: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) \
**Citation**:
```bibtex
@dataset{kather_jakob_nikolas_2018_1214456,
author = {Kather, Jakob Nikolas and Halama, Niels and Marx, Alexander},
title = {{100,000 histological images of human colorectal cancer and healthy tissue}},
month = apr,
year = 2018,
publisher = {Zenodo},
version = {v0.1},
doi = {10.5281/zenodo.1214456},
url = {https://doi.org/10.5281/zenodo.1214456}
}
```
## Data Description "NCT-CRC-HE-100K"
* This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.
* All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (http://ieeexplore.ieee.org/abstract/document/5193250/, DOI 10.1109/ISBI.2009.5193250).
* Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).
* These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.
## Ethics statement "NCT-CRC-HE-100K"
All experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).
## Data set "CRC-VAL-HE-7K"
This is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.
## Data set "NCT-CRC-HE-100K-NONORM"
This is a slightly different version of the "NCT-CRC-HE-100K" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as "NCT-CRC-HE-100K". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as "NCT-CRC-HE-100K", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.
## General comments
Please note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended.
|
1aurent/NCT-CRC-HE
|
[
"task_categories:image-classification",
"size_categories:100K<n<1M",
"license:cc-by-4.0",
"biology",
"Colorectal Pancer",
"Histopathology",
"Histology",
"Digital Pathology",
"region:us"
] |
2023-09-17T15:54:47+00:00
|
{"license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["image-classification"], "tags": ["biology", "Colorectal Pancer", "Histopathology", "Histology", "Digital Pathology"], "configs": [{"config_name": "default", "data_files": [{"split": "CRC_VAL_HE_7K", "path": "data/CRC_VAL_HE_7K-*"}, {"split": "NCT_CRC_HE_100K", "path": "data/NCT_CRC_HE_100K-*"}, {"split": "NCT_CRC_HE_100K_NONORM", "path": "data/NCT_CRC_HE_100K_NONORM-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADI", "1": "BACK", "2": "DEB", "3": "LYM", "4": "MUC", "5": "MUS", "6": "NORM", "7": "STR", "8": "TUM"}}}}], "splits": [{"name": "CRC_VAL_HE_7K", "num_bytes": 1093021734.96, "num_examples": 7180}, {"name": "NCT_CRC_HE_100K", "num_bytes": 15223287558.0, "num_examples": 100000}, {"name": "NCT_CRC_HE_100K_NONORM", "num_bytes": 15219740158.0, "num_examples": 100000}], "download_size": 27708267639, "dataset_size": 31536049450.96}}
|
2023-10-01T16:59:08+00:00
|
[] |
[] |
TAGS
#task_categories-image-classification #size_categories-100K<n<1M #license-cc-by-4.0 #biology #Colorectal Pancer #Histopathology #Histology #Digital Pathology #region-us
|
![DOI](URL
# 100,000 histological images of human colorectal cancer and healthy tissue
Homepage: URL \
Publication Date: 2018-04-07 \
License: Creative Commons Attribution 4.0 International \
Citation:
## Data Description "NCT-CRC-HE-100K"
* This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.
* All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (URL DOI 10.1109/ISBI.2009.5193250).
* Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).
* These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.
## Ethics statement "NCT-CRC-HE-100K"
All experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).
## Data set "CRC-VAL-HE-7K"
This is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.
## Data set "NCT-CRC-HE-100K-NONORM"
This is a slightly different version of the "NCT-CRC-HE-100K" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as "NCT-CRC-HE-100K". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as "NCT-CRC-HE-100K", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.
## General comments
Please note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended.
|
[
"# 100,000 histological images of human colorectal cancer and healthy tissue\n\nHomepage: URL \\\nPublication Date: 2018-04-07 \\\nLicense: Creative Commons Attribution 4.0 International \\\nCitation:",
"## Data Description \"NCT-CRC-HE-100K\"\n\n* This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.\n* All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (URL DOI 10.1109/ISBI.2009.5193250).\n* Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).\n* These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.",
"## Ethics statement \"NCT-CRC-HE-100K\"\n\nAll experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).",
"## Data set \"CRC-VAL-HE-7K\"\n\nThis is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.",
"## Data set \"NCT-CRC-HE-100K-NONORM\"\n\nThis is a slightly different version of the \"NCT-CRC-HE-100K\" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as \"NCT-CRC-HE-100K\". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as \"NCT-CRC-HE-100K\", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.",
"## General comments\n\nPlease note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended."
] |
[
"TAGS\n#task_categories-image-classification #size_categories-100K<n<1M #license-cc-by-4.0 #biology #Colorectal Pancer #Histopathology #Histology #Digital Pathology #region-us \n",
"# 100,000 histological images of human colorectal cancer and healthy tissue\n\nHomepage: URL \\\nPublication Date: 2018-04-07 \\\nLicense: Creative Commons Attribution 4.0 International \\\nCitation:",
"## Data Description \"NCT-CRC-HE-100K\"\n\n* This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.\n* All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (URL DOI 10.1109/ISBI.2009.5193250).\n* Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).\n* These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability.",
"## Ethics statement \"NCT-CRC-HE-100K\"\n\nAll experiments were conducted in accordance with the Declaration of Helsinki, the International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS), the Belmont Report and the U.S. Common Rule. Anonymized archival tissue samples were retrieved from the tissue bank of the National Center for Tumor diseases (NCT, Heidelberg, Germany) in accordance with the regulations of the tissue bank and the approval of the ethics committee of Heidelberg University (tissue bank decision numbers 2152 and 2154, granted to Niels Halama and Jakob Nikolas Kather; informed consent was obtained from all patients as part of the NCT tissue bank protocol, ethics board approval S-207/2005, renewed on 20 Dec 2017). Another set of tissue samples was provided by the pathology archive at UMM (University Medical Center Mannheim, Heidelberg University, Mannheim, Germany) after approval by the institutional ethics board (Ethics Board II at University Medical Center Mannheim, decision number 2017-806R-MA, granted to Alexander Marx and waiving the need for informed consent for this retrospective and fully anonymized analysis of archival samples).",
"## Data set \"CRC-VAL-HE-7K\"\n\nThis is a set of 7180 image patches from N=50 patients with colorectal adenocarcinoma (no overlap with patients in NCT-CRC-HE-100K). It can be used as a validation set for models trained on the larger data set. Like in the larger data set, images are 224x224 px at 0.5 MPP. All tissue samples were provided by the NCT tissue bank, see above for further details and ethics statement.",
"## Data set \"NCT-CRC-HE-100K-NONORM\"\n\nThis is a slightly different version of the \"NCT-CRC-HE-100K\" image set: This set contains 100,000 images in 9 tissue classes at 0.5 MPP and was created from the same raw data as \"NCT-CRC-HE-100K\". However, no color normalization was applied to these images. Consequently, staining intensity and color slightly varies between the images. Please note that although this image set was created from the same data as \"NCT-CRC-HE-100K\", the image regions are not completely identical because the selection of non-overlapping tiles from raw images was a stochastic process.",
"## General comments\n\nPlease note that the classes are only roughly balanced. Classifiers should never be evaluated based on accuracy in the full set alone. Also, if a high risk of training bias is excepted, balancing the number of cases per class is recommended."
] |
[
61,
44,
322,
284,
119,
163,
59
] |
[
"passage: TAGS\n#task_categories-image-classification #size_categories-100K<n<1M #license-cc-by-4.0 #biology #Colorectal Pancer #Histopathology #Histology #Digital Pathology #region-us \n# 100,000 histological images of human colorectal cancer and healthy tissue\n\nHomepage: URL \\\nPublication Date: 2018-04-07 \\\nLicense: Creative Commons Attribution 4.0 International \\\nCitation:## Data Description \"NCT-CRC-HE-100K\"\n\n* This is a set of 100,000 non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue.\n* All images are 224x224 pixels (px) at 0.5 microns per pixel (MPP). All images are color-normalized using Macenko's method (URL DOI 10.1109/ISBI.2009.5193250).\n* Tissue classes are: Adipose (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal colon mucosa (NORM), cancer-associated stroma (STR), colorectal adenocarcinoma epithelium (TUM).\n* These images were manually extracted from N=86 H&E stained human cancer tissue slides from formalin-fixed paraffin-embedded (FFPE) samples from the NCT Biobank (National Center for Tumor Diseases, Heidelberg, Germany) and the UMM pathology archive (University Medical Center Mannheim, Mannheim, Germany). Tissue samples contained CRC primary tumor slides and tumor tissue from CRC liver metastases; normal tissue classes were augmented with non-tumorous regions from gastrectomy specimen to increase variability."
] |
447200053c42004083970f68cd5b43c2ac9e1799
|
# Dataset Card for "med_en2es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ayoubkirouane/med_en2es
|
[
"region:us"
] |
2023-09-17T15:59:12+00:00
|
{"dataset_info": {"features": [{"name": "translation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 49128890, "num_examples": 285584}], "download_size": 27861710, "dataset_size": 49128890}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T15:59:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "med_en2es"
More Information needed
|
[
"# Dataset Card for \"med_en2es\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"med_en2es\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"med_en2es\"\n\nMore Information needed"
] |
52f9f2f85cb2e1677dc63b8ed6408a9fa10b0d81
|
### How to install?
```python
!pip install datasets -q
from huggingface_hub import snapshot_download
import pandas as pd
import matplotlib.pyplot as plt
# First step: download an entire datatset
snapshot_download(repo_id="Aborevsky01/CLEVR-BT-DB", repo_type="dataset", local_dir='path-to-your-local-dir')
# Second step: unarchive the images for VQA
!unzip [path-to-your-local-dir]/[type-of-task]/images.zip
# Example of the triplet (image - question - answer)
plt.imshow(plt.imread('[path-to-your-local-dir]/images/test/Reason_0.png'))
print(pd.read_csv('[path-to-your-local-dir]/[type-of-task]/Reason_test_questions.csv').iloc[0].question)
print([str(line) for line in open('[path-to-your-local-dir]/[type-of-task]/correct_answ.txt', 'rb')][0])
```
### Output of code
**Q**: There is an object to the left of a cylinder to the right of a cylinder, what color is it?
**A**: b'blue\n'
|
Aborevsky01/CLEVR-BT-DB
|
[
"task_categories:visual-question-answering",
"language:en",
"region:us"
] |
2023-09-17T16:03:32+00:00
|
{"language": ["en"], "task_categories": ["visual-question-answering"]}
|
2023-09-20T15:44:56+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-visual-question-answering #language-English #region-us
|
### How to install?
### Output of code
!Sample image
Q: There is an object to the left of a cylinder to the right of a cylinder, what color is it?
A: b'blue\n'
|
[
"### How to install?",
"### Output of code\n\n!Sample image\n\nQ: There is an object to the left of a cylinder to the right of a cylinder, what color is it? \n\nA: b'blue\\n'"
] |
[
"TAGS\n#task_categories-visual-question-answering #language-English #region-us \n",
"### How to install?",
"### Output of code\n\n!Sample image\n\nQ: There is an object to the left of a cylinder to the right of a cylinder, what color is it? \n\nA: b'blue\\n'"
] |
[
25,
6,
44
] |
[
"passage: TAGS\n#task_categories-visual-question-answering #language-English #region-us \n### How to install?### Output of code\n\n!Sample image\n\nQ: There is an object to the left of a cylinder to the right of a cylinder, what color is it? \n\nA: b'blue\\n'"
] |
3c68f3824a134b15e094594cf186c9bf919285f4
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DaisyStar004/guanaco-llama2-1k
|
[
"region:us"
] |
2023-09-17T16:04:28+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T16:04:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
5bab3985825d2ca4f2a2eee07022329807262a71
|
# Dataset Card for Evaluation run of Corianas/256_5epoch
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Corianas/256_5epoch
- **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 [Corianas/256_5epoch](https://huggingface.co/Corianas/256_5epoch) 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 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_Corianas__256_5epoch",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T17:10:44.545164](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__256_5epoch/blob/main/results_2023-09-17T17-10-44.545164.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": 0.006082214765100671,
"em_stderr": 0.0007962432393028846,
"f1": 0.04929320469798652,
"f1_stderr": 0.0015028533751229739,
"acc": 0.26475206337105733,
"acc_stderr": 0.0076718947223475545
},
"harness|drop|3": {
"em": 0.006082214765100671,
"em_stderr": 0.0007962432393028846,
"f1": 0.04929320469798652,
"f1_stderr": 0.0015028533751229739
},
"harness|gsm8k|5": {
"acc": 0.002274450341167551,
"acc_stderr": 0.0013121578148674133
},
"harness|winogrande|5": {
"acc": 0.5272296764009471,
"acc_stderr": 0.014031631629827696
}
}
```
### 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_Corianas__256_5epoch
|
[
"region:us"
] |
2023-09-17T16:10:48+00:00
|
{"pretty_name": "Evaluation run of Corianas/256_5epoch", "dataset_summary": "Dataset automatically created during the evaluation run of model [Corianas/256_5epoch](https://huggingface.co/Corianas/256_5epoch) 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 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_Corianas__256_5epoch\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T17:10:44.545164](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__256_5epoch/blob/main/results_2023-09-17T17-10-44.545164.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\": 0.006082214765100671,\n \"em_stderr\": 0.0007962432393028846,\n \"f1\": 0.04929320469798652,\n \"f1_stderr\": 0.0015028533751229739,\n \"acc\": 0.26475206337105733,\n \"acc_stderr\": 0.0076718947223475545\n },\n \"harness|drop|3\": {\n \"em\": 0.006082214765100671,\n \"em_stderr\": 0.0007962432393028846,\n \"f1\": 0.04929320469798652,\n \"f1_stderr\": 0.0015028533751229739\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.002274450341167551,\n \"acc_stderr\": 0.0013121578148674133\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5272296764009471,\n \"acc_stderr\": 0.014031631629827696\n }\n}\n```", "repo_url": "https://huggingface.co/Corianas/256_5epoch", "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_09_17T17_10_44.545164", "path": ["**/details_harness|drop|3_2023-09-17T17-10-44.545164.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T17-10-44.545164.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T17_10_44.545164", "path": ["**/details_harness|gsm8k|5_2023-09-17T17-10-44.545164.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T17-10-44.545164.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T17_10_44.545164", "path": ["**/details_harness|winogrande|5_2023-09-17T17-10-44.545164.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T17-10-44.545164.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T17_10_44.545164", "path": ["results_2023-09-17T17-10-44.545164.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T17-10-44.545164.parquet"]}]}]}
|
2023-09-17T16:10:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Corianas/256_5epoch
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Corianas/256_5epoch 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 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 2023-09-17T17:10:44.545164(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 Corianas/256_5epoch",
"## 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 Corianas/256_5epoch 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 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 2023-09-17T17:10:44.545164(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 Corianas/256_5epoch",
"## 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 Corianas/256_5epoch 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 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 2023-09-17T17:10:44.545164(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,
18,
31,
166,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
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8,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Corianas/256_5epoch## 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 Corianas/256_5epoch 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 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 2023-09-17T17:10:44.545164(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"
] |
60e33257f58036a622db21ba93ce062898d9db33
|
# Dataset Card for Evaluation run of bofenghuang/vigogne-2-13b-instruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bofenghuang/vigogne-2-13b-instruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bofenghuang/vigogne-2-13b-instruct](https://huggingface.co/bofenghuang/vigogne-2-13b-instruct) 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 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_bofenghuang__vigogne-2-13b-instruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T17:11:41.679174](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-2-13b-instruct/blob/main/results_2023-09-17T17-11-41.679174.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": 0.32791526845637586,
"em_stderr": 0.004807646038011016,
"f1": 0.3836671560402693,
"f1_stderr": 0.00469015048706981,
"acc": 0.3969753580269667,
"acc_stderr": 0.007832281220307026
},
"harness|drop|3": {
"em": 0.32791526845637586,
"em_stderr": 0.004807646038011016,
"f1": 0.3836671560402693,
"f1_stderr": 0.00469015048706981
},
"harness|gsm8k|5": {
"acc": 0.02047005307050796,
"acc_stderr": 0.003900413385915718
},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
"acc_stderr": 0.011764149054698334
}
}
```
### 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_bofenghuang__vigogne-2-13b-instruct
|
[
"region:us"
] |
2023-09-17T16:11:45+00:00
|
{"pretty_name": "Evaluation run of bofenghuang/vigogne-2-13b-instruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [bofenghuang/vigogne-2-13b-instruct](https://huggingface.co/bofenghuang/vigogne-2-13b-instruct) 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 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_bofenghuang__vigogne-2-13b-instruct\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T17:11:41.679174](https://huggingface.co/datasets/open-llm-leaderboard/details_bofenghuang__vigogne-2-13b-instruct/blob/main/results_2023-09-17T17-11-41.679174.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\": 0.32791526845637586,\n \"em_stderr\": 0.004807646038011016,\n \"f1\": 0.3836671560402693,\n \"f1_stderr\": 0.00469015048706981,\n \"acc\": 0.3969753580269667,\n \"acc_stderr\": 0.007832281220307026\n },\n \"harness|drop|3\": {\n \"em\": 0.32791526845637586,\n \"em_stderr\": 0.004807646038011016,\n \"f1\": 0.3836671560402693,\n \"f1_stderr\": 0.00469015048706981\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.02047005307050796,\n \"acc_stderr\": 0.003900413385915718\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7734806629834254,\n \"acc_stderr\": 0.011764149054698334\n }\n}\n```", "repo_url": "https://huggingface.co/bofenghuang/vigogne-2-13b-instruct", "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_09_17T17_11_41.679174", "path": ["**/details_harness|drop|3_2023-09-17T17-11-41.679174.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T17-11-41.679174.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T17_11_41.679174", "path": ["**/details_harness|gsm8k|5_2023-09-17T17-11-41.679174.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T17-11-41.679174.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T17_11_41.679174", "path": ["**/details_harness|winogrande|5_2023-09-17T17-11-41.679174.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T17-11-41.679174.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T17_11_41.679174", "path": ["results_2023-09-17T17-11-41.679174.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T17-11-41.679174.parquet"]}]}]}
|
2023-09-17T16:11:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of bofenghuang/vigogne-2-13b-instruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model bofenghuang/vigogne-2-13b-instruct 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 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 2023-09-17T17:11:41.679174(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 bofenghuang/vigogne-2-13b-instruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model bofenghuang/vigogne-2-13b-instruct 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 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 2023-09-17T17:11:41.679174(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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"## 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 bofenghuang/vigogne-2-13b-instruct 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 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 2023-09-17T17:11:41.679174(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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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of bofenghuang/vigogne-2-13b-instruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model bofenghuang/vigogne-2-13b-instruct 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 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 2023-09-17T17:11:41.679174(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"
] |
b44bb09ed19f9a4c94154763f7b47015fb1e81fa
|
# Dataset Card for "Transformed_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DaisyStar004/Transformed_data
|
[
"region:us"
] |
2023-09-17T16:19:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 385155, "num_examples": 607}], "download_size": 211261, "dataset_size": 385155}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-18T00:50:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "Transformed_data"
More Information needed
|
[
"# Dataset Card for \"Transformed_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"Transformed_data\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"Transformed_data\"\n\nMore Information needed"
] |
07349a8f51699a812da256a6c253af9360fb4e04
|
# Dataset Card for "covid-llama2-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DaisyStar004/covid-llama2-100
|
[
"region:us"
] |
2023-09-17T16:22:56+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 74499, "num_examples": 100}], "download_size": 47718, "dataset_size": 74499}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T16:22:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "covid-llama2-100"
More Information needed
|
[
"# Dataset Card for \"covid-llama2-100\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"covid-llama2-100\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"covid-llama2-100\"\n\nMore Information needed"
] |
d6d0113c5559118089d6a7fbac5e509b90ae5825
|
# Dataset Card for "cireco_chat_abstracts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Harshithacj123/cireco_chat_abstracts
|
[
"region:us"
] |
2023-09-17T16:25:31+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22152, "num_examples": 50}], "download_size": 9554, "dataset_size": 22152}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-10T01:28:46+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cireco_chat_abstracts"
More Information needed
|
[
"# Dataset Card for \"cireco_chat_abstracts\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cireco_chat_abstracts\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cireco_chat_abstracts\"\n\nMore Information needed"
] |
f2b87099bd4ae9bb2007137b2507117e242d15ea
|
# Dataset Card for "xlsum-vietnamese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pengold/xlsum-vietnamese
|
[
"region:us"
] |
2023-09-17T16:41:01+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "prefix_text", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 528740548, "num_examples": 32111}, {"name": "test", "num_bytes": 40990709, "num_examples": 4013}, {"name": "validation", "num_bytes": 40943030, "num_examples": 4013}], "download_size": 304960271, "dataset_size": 610674287}}
|
2023-09-17T16:41:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xlsum-vietnamese"
More Information needed
|
[
"# Dataset Card for \"xlsum-vietnamese\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xlsum-vietnamese\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xlsum-vietnamese\"\n\nMore Information needed"
] |
61ae74fda8933510d3340e41caaf9a38b0e5a4b5
|
# Dataset Card for "real-estate-instructions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jmelsbach/real-estate-instructions
|
[
"region:us"
] |
2023-09-17T16:44:58+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16409348, "num_examples": 8586}], "download_size": 6691825, "dataset_size": 16409348}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T16:48:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "real-estate-instructions"
More Information needed
|
[
"# Dataset Card for \"real-estate-instructions\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"real-estate-instructions\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"real-estate-instructions\"\n\nMore Information needed"
] |
1c0495807daff95e319fb3190010d8a8a89dbaf0
|
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jpbello/gtzan_all_preprocessed
|
[
"region:us"
] |
2023-09-17T16:46:48+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "blues", "1": "classical", "2": "country", "3": "disco", "4": "hiphop", "5": "jazz", "6": "metal", "7": "pop", "8": "reggae", "9": "rock"}}}}, {"name": "input_values", "sequence": "float32"}, {"name": "attention_mask", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3452159816, "num_examples": 899}, {"name": "test", "num_bytes": 384000696, "num_examples": 100}], "download_size": 1923103923, "dataset_size": 3836160512}}
|
2023-09-17T16:55:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gtzan_all_preprocessed"
More Information needed
|
[
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
bf45786ec01e59030c192d6a36a5d6be38fb4422
|
# Dataset Card for "real-estate-instructions-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jmelsbach/real-estate-instructions-small
|
[
"region:us"
] |
2023-09-17T16:55:53+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 951120, "num_examples": 500}], "download_size": 469994, "dataset_size": 951120}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T16:57:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "real-estate-instructions-small"
More Information needed
|
[
"# Dataset Card for \"real-estate-instructions-small\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"real-estate-instructions-small\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"real-estate-instructions-small\"\n\nMore Information needed"
] |
9d2e166e5a1ff2f3310e7c889ac0926e7806ebf6
|
# pseudo's BG20K-COCO dataset
## Dataset Description
- **Homepage:** https://paperswithcode.com/dataset/bg-20k
- **Repository:** https://github.com/JizhiziLi/GFM
- **Paper:** https://paperswithcode.com/dataset/bg-20k
### Dataset Summary
This is the BG20K dataset, captioned using the BLIP2 model `git-coco-large`.
BG20K is a dataset of non-salient objects, though some animals and silhouettes may have slipped through (see `/train/s` directory).
The captions have been partially validated as being highly accurate. Locations tend to be named correctly.
|
ptx0/BG20K
|
[
"region:us"
] |
2023-09-17T16:56:18+00:00
|
{}
|
2023-09-17T23:59:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# pseudo's BG20K-COCO dataset
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
### Dataset Summary
This is the BG20K dataset, captioned using the BLIP2 model 'git-coco-large'.
BG20K is a dataset of non-salient objects, though some animals and silhouettes may have slipped through (see '/train/s' directory).
The captions have been partially validated as being highly accurate. Locations tend to be named correctly.
|
[
"# pseudo's BG20K-COCO dataset",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"### Dataset Summary\n\nThis is the BG20K dataset, captioned using the BLIP2 model 'git-coco-large'.\n\nBG20K is a dataset of non-salient objects, though some animals and silhouettes may have slipped through (see '/train/s' directory).\n\nThe captions have been partially validated as being highly accurate. Locations tend to be named correctly."
] |
[
"TAGS\n#region-us \n",
"# pseudo's BG20K-COCO dataset",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL",
"### Dataset Summary\n\nThis is the BG20K dataset, captioned using the BLIP2 model 'git-coco-large'.\n\nBG20K is a dataset of non-salient objects, though some animals and silhouettes may have slipped through (see '/train/s' directory).\n\nThe captions have been partially validated as being highly accurate. Locations tend to be named correctly."
] |
[
6,
12,
18,
96
] |
[
"passage: TAGS\n#region-us \n# pseudo's BG20K-COCO dataset## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL### Dataset Summary\n\nThis is the BG20K dataset, captioned using the BLIP2 model 'git-coco-large'.\n\nBG20K is a dataset of non-salient objects, though some animals and silhouettes may have slipped through (see '/train/s' directory).\n\nThe captions have been partially validated as being highly accurate. Locations tend to be named correctly."
] |
1d9d40d5074e5e85f50b7b582dbb06153465b222
|
# Dataset Card for Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [KnutJaegersberg/gpt-2-xl-EvolInstruct](https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct) 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 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_KnutJaegersberg__gpt-2-xl-EvolInstruct",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:02:57.671011](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct/blob/main/results_2023-09-17T18-02-57.671011.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": 0.0045092281879194635,
"em_stderr": 0.000686134689909505,
"f1": 0.039052013422818846,
"f1_stderr": 0.0012293007940162644,
"acc": 0.26831931822737687,
"acc_stderr": 0.007544776234715419
},
"harness|drop|3": {
"em": 0.0045092281879194635,
"em_stderr": 0.000686134689909505,
"f1": 0.039052013422818846,
"f1_stderr": 0.0012293007940162644
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492619
},
"harness|winogrande|5": {
"acc": 0.5351223362273086,
"acc_stderr": 0.014017773120881576
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct
|
[
"region:us"
] |
2023-09-17T17:03:00+00:00
|
{"pretty_name": "Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct", "dataset_summary": "Dataset automatically created during the evaluation run of model [KnutJaegersberg/gpt-2-xl-EvolInstruct](https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct) 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 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_KnutJaegersberg__gpt-2-xl-EvolInstruct\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T18:02:57.671011](https://huggingface.co/datasets/open-llm-leaderboard/details_KnutJaegersberg__gpt-2-xl-EvolInstruct/blob/main/results_2023-09-17T18-02-57.671011.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\": 0.0045092281879194635,\n \"em_stderr\": 0.000686134689909505,\n \"f1\": 0.039052013422818846,\n \"f1_stderr\": 0.0012293007940162644,\n \"acc\": 0.26831931822737687,\n \"acc_stderr\": 0.007544776234715419\n },\n \"harness|drop|3\": {\n \"em\": 0.0045092281879194635,\n \"em_stderr\": 0.000686134689909505,\n \"f1\": 0.039052013422818846,\n \"f1_stderr\": 0.0012293007940162644\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \"acc_stderr\": 0.0010717793485492619\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5351223362273086,\n \"acc_stderr\": 0.014017773120881576\n }\n}\n```", "repo_url": "https://huggingface.co/KnutJaegersberg/gpt-2-xl-EvolInstruct", "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_09_17T18_02_57.671011", "path": ["**/details_harness|drop|3_2023-09-17T18-02-57.671011.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T18-02-57.671011.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T18_02_57.671011", "path": ["**/details_harness|gsm8k|5_2023-09-17T18-02-57.671011.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T18-02-57.671011.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T18_02_57.671011", "path": ["**/details_harness|winogrande|5_2023-09-17T18-02-57.671011.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T18-02-57.671011.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T18_02_57.671011", "path": ["results_2023-09-17T18-02-57.671011.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T18-02-57.671011.parquet"]}]}]}
|
2023-09-17T17:03:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model KnutJaegersberg/gpt-2-xl-EvolInstruct 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 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 2023-09-17T18:02:57.671011(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
[
"# Dataset Card for Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/gpt-2-xl-EvolInstruct 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 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 2023-09-17T18:02:57.671011(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 KnutJaegersberg/gpt-2-xl-EvolInstruct",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/gpt-2-xl-EvolInstruct 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 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 2023-09-17T18:02:57.671011(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",
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"### Curation Rationale",
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"#### 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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9,
8,
8,
7,
8,
7,
5,
6,
6,
5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KnutJaegersberg/gpt-2-xl-EvolInstruct## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model KnutJaegersberg/gpt-2-xl-EvolInstruct 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 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 2023-09-17T18:02:57.671011(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"
] |
15618fc253962b01def615873488ac7b77fa8831
|
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ardneebwar/gtzan_all_preprocessed
|
[
"region:us"
] |
2023-09-17T17:18:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "blues", "1": "classical", "2": "country", "3": "disco", "4": "hiphop", "5": "jazz", "6": "metal", "7": "pop", "8": "reggae", "9": "rock"}}}}, {"name": "input_values", "sequence": "float32"}, {"name": "attention_mask", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3452159816, "num_examples": 899}, {"name": "test", "num_bytes": 384000696, "num_examples": 100}], "download_size": 0, "dataset_size": 3836160512}}
|
2023-09-18T09:41:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gtzan_all_preprocessed"
More Information needed
|
[
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
224cc881d30735ccf19915872c26c1edfe553b10
|
# Dataset Card for "python_codestyles-mixed1-500"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style.
The dataset combines both
datasets [infinityofspace/python_codestyles-random-500](https://huggingface.co/datasets/infinityofspace/python_codestyles-random-500)
and [infinityofspace/python_codestyles-single-500](https://huggingface.co/datasets/infinityofspace/python_codestyles-single-500)
by randomly selecting half of the examples from each of the two datasets.
The code styles in the combined dataset differ in at least one and exactly one codestyle rule, which is called a
`mixed` codestyle dataset variant. The dataset consists of a training and test group, with none of the code styles
overlapping between groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|:-----------------------------------------------------------------------:|:----------------------------------------:|
| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
|
infinityofspace/python_codestyles-mixed1-500
|
[
"size_categories:100K<n<1M",
"license:mit",
"python",
"code-style",
"mixed",
"doi:10.57967/hf/1231",
"region:us"
] |
2023-09-17T17:21:31+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "code_codestyle", "dtype": "int64"}, {"name": "style_context", "dtype": "string"}, {"name": "style_context_codestyle", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1794945328.216033, "num_examples": 153992}, {"name": "test", "num_bytes": 326644128.3197262, "num_examples": 28194}], "download_size": 645473358, "dataset_size": 2121589456.5357592}, "tags": ["python", "code-style", "mixed"]}
|
2023-10-18T19:56:48+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #license-mit #python #code-style #mixed #doi-10.57967/hf/1231 #region-us
|
Dataset Card for "python\_codestyles-mixed1-500"
================================================
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style.
The dataset combines both
datasets infinityofspace/python\_codestyles-random-500
and infinityofspace/python\_codestyles-single-500
by randomly selecting half of the examples from each of the two datasets.
The code styles in the combined dataset differ in at least one and exactly one codestyle rule, which is called a
'mixed' codestyle dataset variant. The dataset consists of a training and test group, with none of the code styles
overlapping between groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #mixed #doi-10.57967/hf/1231 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #mixed #doi-10.57967/hf/1231 #region-us \n"
] |
f5d96e8e77aa881daf5c0b9f0b46daca760689d1
|
# Dataset Card for "python_codestyles-random-1k"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains `1.000` completely
different code styles. The code styles differ in at least one codestyle rule, which is called a `random` codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|:-----------------------------------------------------------------------:|:----------------------------------------:|
| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
You can find the corresponding code styles of the examples in the file [additional_data.json](additional_data.json).
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns `code_codestyle` and `style_context_codestyle` in the dataset.
There are 364.400 samples in total and 182.200 positive and 182.200 negative samples.
|
infinityofspace/python_codestyles-random-1k
|
[
"size_categories:100K<n<1M",
"license:mit",
"python",
"code-style",
"random",
"doi:10.57967/hf/1232",
"region:us"
] |
2023-09-17T17:24:57+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "code_codestyle", "dtype": "int64"}, {"name": "style_context", "dtype": "string"}, {"name": "style_context_codestyle", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3604934957, "num_examples": 308000}, {"name": "test", "num_bytes": 645620388, "num_examples": 56400}], "download_size": 671035436, "dataset_size": 4250555345}, "tags": ["python", "code-style", "random"]}
|
2023-10-18T19:42:59+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #license-mit #python #code-style #random #doi-10.57967/hf/1232 #region-us
|
Dataset Card for "python\_codestyles-random-1k"
===============================================
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains '1.000' completely
different code styles. The code styles differ in at least one codestyle rule, which is called a 'random' codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
You can find the corresponding code styles of the examples in the file additional\_data.json.
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns 'code\_codestyle' and 'style\_context\_codestyle' in the dataset.
There are 364.400 samples in total and 182.200 positive and 182.200 negative samples.
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #random #doi-10.57967/hf/1232 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #random #doi-10.57967/hf/1232 #region-us \n"
] |
2643dae8a0e395ae3e6589ead77c9d52a19bbcb9
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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]
|
uellaaaa/praci
|
[
"language:it",
"region:us"
] |
2023-09-17T17:29:38+00:00
|
{"language": ["it"]}
|
2023-09-17T17:30:28+00:00
|
[] |
[
"it"
] |
TAGS
#language-Italian #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"### Supported Tasks and Leaderboards",
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] |
6226196b2bdda77aca57ba9ebe145f9983ad039d
|
# Lambada (Vietnamese)
## Install
To install `lm-eval` from the github repository main branch, run:
```bash
git clone https://github.com/hieunguyen1053/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```
## Basic Usage
> **Note**: When reporting results from eval harness, please include the task versions (shown in `results["versions"]`) for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the [Task Versioning](#task-versioning) section for more info.
### Hugging Face `transformers`
To evaluate a model hosted on the [HuggingFace Hub](https://huggingface.co/models) (e.g. vlsp-2023-vllm/hoa-1b4) on `lambada_vi` you can use the following command:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4 \
--tasks lambada_vi \
--device cuda:0
```
Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
```bash
python main.py \
--model hf-causal \
--model_args pretrained=vlsp-2023-vllm/hoa-1b4,revision=step100000,dtype="float" \
--tasks lambada_vi \
--device cuda:0
```
To evaluate models that are loaded via `AutoSeq2SeqLM` in Huggingface, you instead use `hf-seq2seq`. *To evaluate (causal) models across multiple GPUs, use `--model hf-causal-experimental`*
> **Warning**: Choosing the wrong model may result in erroneous outputs despite not erroring.
|
vlsp-2023-vllm/lambada_vi
|
[
"region:us"
] |
2023-09-17T17:31:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "target_word", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "num_sents", "dtype": "int64"}, {"name": "target_word", "struct": [{"name": "appeared_in_prev_sents", "dtype": "bool"}, {"name": "pos_tag", "dtype": "string"}]}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "word_type", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 18460415.77200859, "num_examples": 10000}, {"name": "validation", "num_bytes": 454126.2279914113, "num_examples": 246}], "download_size": 10704436, "dataset_size": 18914542.0}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
|
2023-11-19T08:53:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Lambada (Vietnamese)
## Install
To install 'lm-eval' from the github repository main branch, run:
## Basic Usage
> Note: When reporting results from eval harness, please include the task versions (shown in 'results["versions"]') for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the Task Versioning section for more info.
### Hugging Face 'transformers'
To evaluate a model hosted on the HuggingFace Hub (e.g. vlsp-2023-vllm/hoa-1b4) on 'lambada_vi' you can use the following command:
Additional arguments can be provided to the model constructor using the '--model_args' flag. Most notably, this supports the common practice of using the 'revisions' feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:
To evaluate models that are loaded via 'AutoSeq2SeqLM' in Huggingface, you instead use 'hf-seq2seq'. *To evaluate (causal) models across multiple GPUs, use '--model hf-causal-experimental'*
> Warning: Choosing the wrong model may result in erroneous outputs despite not erroring.
|
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"### Hugging Face 'transformers'\n\nTo evaluate a model hosted on the HuggingFace Hub (e.g. vlsp-2023-vllm/hoa-1b4) on 'lambada_vi' you can use the following command:\n\n\n\n\nAdditional arguments can be provided to the model constructor using the '--model_args' flag. Most notably, this supports the common practice of using the 'revisions' feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:\n\n\n\nTo evaluate models that are loaded via 'AutoSeq2SeqLM' in Huggingface, you instead use 'hf-seq2seq'. *To evaluate (causal) models across multiple GPUs, use '--model hf-causal-experimental'*\n\n> Warning: Choosing the wrong model may result in erroneous outputs despite not erroring."
] |
[
"TAGS\n#region-us \n",
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"## Install\n\nTo install 'lm-eval' from the github repository main branch, run:",
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"### Hugging Face 'transformers'\n\nTo evaluate a model hosted on the HuggingFace Hub (e.g. vlsp-2023-vllm/hoa-1b4) on 'lambada_vi' you can use the following command:\n\n\n\n\nAdditional arguments can be provided to the model constructor using the '--model_args' flag. Most notably, this supports the common practice of using the 'revisions' feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:\n\n\n\nTo evaluate models that are loaded via 'AutoSeq2SeqLM' in Huggingface, you instead use 'hf-seq2seq'. *To evaluate (causal) models across multiple GPUs, use '--model hf-causal-experimental'*\n\n> Warning: Choosing the wrong model may result in erroneous outputs despite not erroring."
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[
6,
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[
"passage: TAGS\n#region-us \n# Lambada (Vietnamese)## Install\n\nTo install 'lm-eval' from the github repository main branch, run:## Basic Usage\n\n> Note: When reporting results from eval harness, please include the task versions (shown in 'results[\"versions\"]') for reproducibility. This allows bug fixes to tasks while also ensuring that previously reported scores are reproducible. See the Task Versioning section for more info.### Hugging Face 'transformers'\n\nTo evaluate a model hosted on the HuggingFace Hub (e.g. vlsp-2023-vllm/hoa-1b4) on 'lambada_vi' you can use the following command:\n\n\n\n\nAdditional arguments can be provided to the model constructor using the '--model_args' flag. Most notably, this supports the common practice of using the 'revisions' feature on the Hub to store partially trained checkpoints, or to specify the datatype for running a model:\n\n\n\nTo evaluate models that are loaded via 'AutoSeq2SeqLM' in Huggingface, you instead use 'hf-seq2seq'. *To evaluate (causal) models across multiple GPUs, use '--model hf-causal-experimental'*\n\n> Warning: Choosing the wrong model may result in erroneous outputs despite not erroring."
] |
596fa5b0df0a4a0af6bc269545e7a282b3cfceb7
|
# Dataset Card for Evaluation run of ikala/bloom-zh-3b-chat
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ikala/bloom-zh-3b-chat
- **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 [ikala/bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) 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 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_ikala__bloom-zh-3b-chat",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T18:43:41.397434](https://huggingface.co/datasets/open-llm-leaderboard/details_ikala__bloom-zh-3b-chat/blob/main/results_2023-09-17T18-43-41.397434.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": 0.08022231543624161,
"em_stderr": 0.0027818178017908015,
"f1": 0.1465918624161071,
"f1_stderr": 0.003030605237968897,
"acc": 0.2954867628904967,
"acc_stderr": 0.007847263403599461
},
"harness|drop|3": {
"em": 0.08022231543624161,
"em_stderr": 0.0027818178017908015,
"f1": 0.1465918624161071,
"f1_stderr": 0.003030605237968897
},
"harness|gsm8k|5": {
"acc": 0.004548900682335102,
"acc_stderr": 0.0018535550440036198
},
"harness|winogrande|5": {
"acc": 0.5864246250986582,
"acc_stderr": 0.013840971763195304
}
}
```
### 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_ikala__bloom-zh-3b-chat
|
[
"region:us"
] |
2023-09-17T17:43:44+00:00
|
{"pretty_name": "Evaluation run of ikala/bloom-zh-3b-chat", "dataset_summary": "Dataset automatically created during the evaluation run of model [ikala/bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) 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 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_ikala__bloom-zh-3b-chat\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T18:43:41.397434](https://huggingface.co/datasets/open-llm-leaderboard/details_ikala__bloom-zh-3b-chat/blob/main/results_2023-09-17T18-43-41.397434.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\": 0.08022231543624161,\n \"em_stderr\": 0.0027818178017908015,\n \"f1\": 0.1465918624161071,\n \"f1_stderr\": 0.003030605237968897,\n \"acc\": 0.2954867628904967,\n \"acc_stderr\": 0.007847263403599461\n },\n \"harness|drop|3\": {\n \"em\": 0.08022231543624161,\n \"em_stderr\": 0.0027818178017908015,\n \"f1\": 0.1465918624161071,\n \"f1_stderr\": 0.003030605237968897\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.004548900682335102,\n \"acc_stderr\": 0.0018535550440036198\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5864246250986582,\n \"acc_stderr\": 0.013840971763195304\n }\n}\n```", "repo_url": "https://huggingface.co/ikala/bloom-zh-3b-chat", "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_09_17T18_43_41.397434", "path": ["**/details_harness|drop|3_2023-09-17T18-43-41.397434.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T18-43-41.397434.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T18_43_41.397434", "path": ["**/details_harness|gsm8k|5_2023-09-17T18-43-41.397434.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T18-43-41.397434.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T18_43_41.397434", "path": ["**/details_harness|winogrande|5_2023-09-17T18-43-41.397434.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T18-43-41.397434.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T18_43_41.397434", "path": ["results_2023-09-17T18-43-41.397434.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T18-43-41.397434.parquet"]}]}]}
|
2023-09-17T17:43:53+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of ikala/bloom-zh-3b-chat
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model ikala/bloom-zh-3b-chat 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 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 2023-09-17T18:43:41.397434(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 ikala/bloom-zh-3b-chat",
"## 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 ikala/bloom-zh-3b-chat 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 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 2023-09-17T18:43:41.397434(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of ikala/bloom-zh-3b-chat",
"## 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 ikala/bloom-zh-3b-chat 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 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 2023-09-17T18:43:41.397434(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"## 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 ikala/bloom-zh-3b-chat## 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 ikala/bloom-zh-3b-chat 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 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 2023-09-17T18:43:41.397434(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"
] |
7d5a8a9a6a3fc75a3ed66cff16314db8442d1a26
|
# Dataset Card for "MetalDam_Base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ironchanchellor/MetalDam_Base
|
[
"region:us"
] |
2023-09-17T17:48:12+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 27150608.0, "num_examples": 33}, {"name": "validation", "num_bytes": 7208635.0, "num_examples": 9}], "download_size": 34361273, "dataset_size": 34359243.0}}
|
2023-09-18T19:35:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "MetalDam_Base"
More Information needed
|
[
"# Dataset Card for \"MetalDam_Base\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"MetalDam_Base\"\n\nMore Information needed"
] |
[
6,
17
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[
"passage: TAGS\n#region-us \n# Dataset Card for \"MetalDam_Base\"\n\nMore Information needed"
] |
f0deb37b562a5f30520e9d445ad2eb0666f926b1
|
# Dataset Card for Evaluation run of Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
- **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 [Harshvir/LaMini-Neo-1.3B-Mental-Health_lora](https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora) 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 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_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T19:00:53.771505](https://huggingface.co/datasets/open-llm-leaderboard/details_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora/blob/main/results_2023-09-17T19-00-53.771505.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0,
"acc": 0.24585635359116023,
"acc_stderr": 0.007025277661412099
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.49171270718232046,
"acc_stderr": 0.014050555322824197
}
}
```
### 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_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora
|
[
"region:us"
] |
2023-09-17T18:00:57+00:00
|
{"pretty_name": "Evaluation run of Harshvir/LaMini-Neo-1.3B-Mental-Health_lora", "dataset_summary": "Dataset automatically created during the evaluation run of model [Harshvir/LaMini-Neo-1.3B-Mental-Health_lora](https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora) 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 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_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T19:00:53.771505](https://huggingface.co/datasets/open-llm-leaderboard/details_Harshvir__LaMini-Neo-1.3B-Mental-Health_lora/blob/main/results_2023-09-17T19-00-53.771505.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\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0,\n \"acc\": 0.24585635359116023,\n \"acc_stderr\": 0.007025277661412099\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.49171270718232046,\n \"acc_stderr\": 0.014050555322824197\n }\n}\n```", "repo_url": "https://huggingface.co/Harshvir/LaMini-Neo-1.3B-Mental-Health_lora", "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_09_17T19_00_53.771505", "path": ["**/details_harness|drop|3_2023-09-17T19-00-53.771505.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T19-00-53.771505.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T19_00_53.771505", "path": ["**/details_harness|gsm8k|5_2023-09-17T19-00-53.771505.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T19-00-53.771505.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T19_00_53.771505", "path": ["**/details_harness|winogrande|5_2023-09-17T19-00-53.771505.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T19-00-53.771505.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T19_00_53.771505", "path": ["results_2023-09-17T19-00-53.771505.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T19-00-53.771505.parquet"]}]}]}
|
2023-09-17T18:01:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Harshvir/LaMini-Neo-1.3B-Mental-Health_lora
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Harshvir/LaMini-Neo-1.3B-Mental-Health_lora 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 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 2023-09-17T19:00:53.771505(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 Harshvir/LaMini-Neo-1.3B-Mental-Health_lora",
"## 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 Harshvir/LaMini-Neo-1.3B-Mental-Health_lora 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 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 2023-09-17T19:00:53.771505(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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"## 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 Harshvir/LaMini-Neo-1.3B-Mental-Health_lora",
"## 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 Harshvir/LaMini-Neo-1.3B-Mental-Health_lora 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 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 2023-09-17T19:00:53.771505(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",
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Harshvir/LaMini-Neo-1.3B-Mental-Health_lora## 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 Harshvir/LaMini-Neo-1.3B-Mental-Health_lora 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 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 2023-09-17T19:00:53.771505(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"
] |
9fc7b0ff841f8e59a71972f588ee0b1fe2e07440
|
# Dataset Card for "news-articles-ptbr-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
iara-project/news-articles-ptbr-dataset
|
[
"region:us"
] |
2023-09-17T18:11:32+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "category_natural_language", "dtype": "string"}, {"name": "link", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 628987914, "num_examples": 176114}, {"name": "test", "num_bytes": 627415372, "num_examples": 176114}], "download_size": 770300096, "dataset_size": 1256403286}}
|
2023-09-21T02:12:30+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "news-articles-ptbr-dataset"
More Information needed
|
[
"# Dataset Card for \"news-articles-ptbr-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"news-articles-ptbr-dataset\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"news-articles-ptbr-dataset\"\n\nMore Information needed"
] |
ca4da23979509bedc954eb1d761f59594424b258
|
# Dataset Card for "rw_processed_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
qazisaad/rw_processed_ds
|
[
"region:us"
] |
2023-09-17T18:26:04+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 79056000, "num_examples": 16200}, {"name": "test", "num_bytes": 8784000, "num_examples": 1800}], "download_size": 16937368, "dataset_size": 87840000}}
|
2023-09-17T18:26:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "rw_processed_ds"
More Information needed
|
[
"# Dataset Card for \"rw_processed_ds\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"rw_processed_ds\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"rw_processed_ds\"\n\nMore Information needed"
] |
423ff7bd9111327f298d391c71d950eacefbbc1d
|
# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [lvkaokao/llama2-7b-hf-chat-lora-v2](https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2) 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 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_lvkaokao__llama2-7b-hf-chat-lora-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T19:43:28.899115](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora-v2/blob/main/results_2023-09-17T19-43-28.899115.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": 0.25723573825503354,
"em_stderr": 0.004476419757548592,
"f1": 0.31864408557046997,
"f1_stderr": 0.004427420085857621,
"acc": 0.42871444189201235,
"acc_stderr": 0.010374814363571815
},
"harness|drop|3": {
"em": 0.25723573825503354,
"em_stderr": 0.004476419757548592,
"f1": 0.31864408557046997,
"f1_stderr": 0.004427420085857621
},
"harness|gsm8k|5": {
"acc": 0.10841546626231995,
"acc_stderr": 0.008563852506627476
},
"harness|winogrande|5": {
"acc": 0.7490134175217048,
"acc_stderr": 0.012185776220516155
}
}
```
### 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_lvkaokao__llama2-7b-hf-chat-lora-v2
|
[
"region:us"
] |
2023-09-17T18:43:32+00:00
|
{"pretty_name": "Evaluation run of lvkaokao/llama2-7b-hf-chat-lora-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [lvkaokao/llama2-7b-hf-chat-lora-v2](https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2) 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 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_lvkaokao__llama2-7b-hf-chat-lora-v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T19:43:28.899115](https://huggingface.co/datasets/open-llm-leaderboard/details_lvkaokao__llama2-7b-hf-chat-lora-v2/blob/main/results_2023-09-17T19-43-28.899115.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\": 0.25723573825503354,\n \"em_stderr\": 0.004476419757548592,\n \"f1\": 0.31864408557046997,\n \"f1_stderr\": 0.004427420085857621,\n \"acc\": 0.42871444189201235,\n \"acc_stderr\": 0.010374814363571815\n },\n \"harness|drop|3\": {\n \"em\": 0.25723573825503354,\n \"em_stderr\": 0.004476419757548592,\n \"f1\": 0.31864408557046997,\n \"f1_stderr\": 0.004427420085857621\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.10841546626231995,\n \"acc_stderr\": 0.008563852506627476\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7490134175217048,\n \"acc_stderr\": 0.012185776220516155\n }\n}\n```", "repo_url": "https://huggingface.co/lvkaokao/llama2-7b-hf-chat-lora-v2", "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_09_17T19_43_28.899115", "path": ["**/details_harness|drop|3_2023-09-17T19-43-28.899115.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T19-43-28.899115.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T19_43_28.899115", "path": ["**/details_harness|gsm8k|5_2023-09-17T19-43-28.899115.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T19-43-28.899115.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T19_43_28.899115", "path": ["**/details_harness|winogrande|5_2023-09-17T19-43-28.899115.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T19-43-28.899115.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T19_43_28.899115", "path": ["results_2023-09-17T19-43-28.899115.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T19-43-28.899115.parquet"]}]}]}
|
2023-09-17T18:43:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora-v2 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 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 2023-09-17T19:43:28.899115(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 lvkaokao/llama2-7b-hf-chat-lora-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora-v2 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 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 2023-09-17T19:43:28.899115(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 lvkaokao/llama2-7b-hf-chat-lora-v2",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora-v2 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 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 2023-09-17T19:43:28.899115(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,
30,
31,
178,
66,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of lvkaokao/llama2-7b-hf-chat-lora-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model lvkaokao/llama2-7b-hf-chat-lora-v2 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 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 2023-09-17T19:43:28.899115(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"
] |
4348a826a77a367fc535a32635deaa315ce3d47d
|
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AescF/gtzan_all_preprocessed
|
[
"region:us"
] |
2023-09-17T18:45:21+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "blues", "1": "classical", "2": "country", "3": "disco", "4": "hiphop", "5": "jazz", "6": "metal", "7": "pop", "8": "reggae", "9": "rock"}}}}, {"name": "input_values", "sequence": "float32"}, {"name": "attention_mask", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3452159816, "num_examples": 899}, {"name": "test", "num_bytes": 384000696, "num_examples": 100}], "download_size": 1923103923, "dataset_size": 3836160512}}
|
2023-09-17T18:46:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gtzan_all_preprocessed"
More Information needed
|
[
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
f4d469a02778a87959a4ad1c5dc30d0396d83bcb
|
# Dataset Card for "python_codestyles-single-1k"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains `1.000` completely
different code styles. The code styles differ in exactly one codestyle rule, which is called a `single` codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|:-----------------------------------------------------------------------:|:----------------------------------------:|
| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
You can find the corresponding code styles of the examples in the file [additional_data.json](additional_data.json).
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns `code_codestyle` and `style_context_codestyle` in the dataset.
There are 364.381 samples in total and 182.181 positive and 182.200 negative samples.
|
infinityofspace/python_codestyles-single-1k
|
[
"size_categories:100K<n<1M",
"license:mit",
"python",
"code-style",
"single",
"doi:10.57967/hf/1233",
"region:us"
] |
2023-09-17T18:47:13+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "code_codestyle", "dtype": "int64"}, {"name": "style_context", "dtype": "string"}, {"name": "style_context_codestyle", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3579272804, "num_examples": 307987}, {"name": "test", "num_bytes": 643911672, "num_examples": 56394}], "download_size": 639857749, "dataset_size": 4223184476}, "tags": ["python", "code-style", "single"]}
|
2023-10-18T19:45:51+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #license-mit #python #code-style #single #doi-10.57967/hf/1233 #region-us
|
Dataset Card for "python\_codestyles-single-1k"
===============================================
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style. In total, the dataset contains '1.000' completely
different code styles. The code styles differ in exactly one codestyle rule, which is called a 'single' codestyle
dataset variant. The dataset consists of a training and test group, with none of the code styles overlapping between
groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
You can find the corresponding code styles of the examples in the file additional\_data.json.
The code styles in the file are split by training and test group and the index corresponds to the class for the
columns 'code\_codestyle' and 'style\_context\_codestyle' in the dataset.
There are 364.381 samples in total and 182.181 positive and 182.200 negative samples.
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #single #doi-10.57967/hf/1233 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #single #doi-10.57967/hf/1233 #region-us \n"
] |
575b8eca6fdedb16470c91e9c1ecec95d015cd21
|
# Dataset Card for "xbsd-guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xbsd/xbsd-guanaco-llama2-1k
|
[
"region:us"
] |
2023-09-17T18:48:26+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 966693, "dataset_size": 1654448}}
|
2023-09-17T18:48:28+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "xbsd-guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"xbsd-guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"xbsd-guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"xbsd-guanaco-llama2-1k\"\n\nMore Information needed"
] |
003d4c1117d7b59629bfecff9d9ee1b14bd8c630
|
# Dataset Card for "python_codestyles-mixed1-1k"
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style.
The dataset combines both
datasets [infinityofspace/python_codestyles-random-1k](https://huggingface.co/datasets/infinityofspace/python_codestyles-random-1k)
and [infinityofspace/python_codestyles-single-1k](https://huggingface.co/datasets/infinityofspace/python_codestyles-single-1k)
by randomly selecting half of the examples from each of the two datasets.
The code styles in the combined dataset differ in at least one and exactly one codestyle rule, which is called a
`mixed` codestyle dataset variant. The dataset consists of a training and test group, with none of the code styles
overlapping between groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
| repository | tag or commit |
|:-----------------------------------------------------------------------:|:----------------------------------------:|
| [TheAlgorithms/Python](https://github.com/TheAlgorithms/Python) | f614ed72170011d2d439f7901e1c8daa7deac8c4 |
| [huggingface/transformers](https://github.com/huggingface/transformers) | v4.31.0 |
| [huggingface/datasets](https://github.com/huggingface/datasets) | 2.13.1 |
| [huggingface/diffusers](https://github.com/huggingface/diffusers) | v0.18.2 |
| [huggingface/accelerate](https://github.com/huggingface/accelerate) | v0.21.0 |
|
infinityofspace/python_codestyles-mixed1-1k
|
[
"size_categories:100K<n<1M",
"license:mit",
"python",
"code-style",
"mixed",
"doi:10.57967/hf/1234",
"region:us"
] |
2023-09-17T18:56:12+00:00
|
{"license": "mit", "size_categories": ["100K<n<1M"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "code", "dtype": "string"}, {"name": "code_codestyle", "dtype": "int64"}, {"name": "style_context", "dtype": "string"}, {"name": "style_context_codestyle", "dtype": "int64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3592039734.4341164, "num_examples": 307988}, {"name": "test", "num_bytes": 644731732.1102186, "num_examples": 56394}], "download_size": 0, "dataset_size": 4236771466.544335}, "tags": ["python", "code-style", "mixed"]}
|
2023-10-18T19:58:15+00:00
|
[] |
[] |
TAGS
#size_categories-100K<n<1M #license-mit #python #code-style #mixed #doi-10.57967/hf/1234 #region-us
|
Dataset Card for "python\_codestyles-mixed1-1k"
===============================================
This dataset contains negative and positive examples with python code of compliance with a code style. A positive
example represents compliance with the code style (label is 1). Each example is composed of two components, the first
component consists of a code that either conforms to the code style or violates it and the second component
corresponding to an example code that already conforms to a code style.
The dataset combines both
datasets infinityofspace/python\_codestyles-random-1k
and infinityofspace/python\_codestyles-single-1k
by randomly selecting half of the examples from each of the two datasets.
The code styles in the combined dataset differ in at least one and exactly one codestyle rule, which is called a
'mixed' codestyle dataset variant. The dataset consists of a training and test group, with none of the code styles
overlapping between groups. In addition, both groups contain completely different underlying codes.
The examples contain source code from the following repositories:
|
[] |
[
"TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #mixed #doi-10.57967/hf/1234 #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#size_categories-100K<n<1M #license-mit #python #code-style #mixed #doi-10.57967/hf/1234 #region-us \n"
] |
8f2aa6380ba15b616f528cc22c52884f8215c1d0
|
# Dataset Card for Evaluation run of nkpz/llama2-22b-chat-wizard-uncensored
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored
- **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 [nkpz/llama2-22b-chat-wizard-uncensored](https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored) 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 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_nkpz__llama2-22b-chat-wizard-uncensored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T20:13:04.484783](https://huggingface.co/datasets/open-llm-leaderboard/details_nkpz__llama2-22b-chat-wizard-uncensored/blob/main/results_2023-09-17T20-13-04.484783.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": 0.047399328859060404,
"em_stderr": 0.002176111725660241,
"f1": 0.10403313758389295,
"f1_stderr": 0.0024782296933352054,
"acc": 0.406947395631691,
"acc_stderr": 0.010758553304204539
},
"harness|drop|3": {
"em": 0.047399328859060404,
"em_stderr": 0.002176111725660241,
"f1": 0.10403313758389295,
"f1_stderr": 0.0024782296933352054
},
"harness|gsm8k|5": {
"acc": 0.11144806671721001,
"acc_stderr": 0.008668021353794427
},
"harness|winogrande|5": {
"acc": 0.7024467245461721,
"acc_stderr": 0.01284908525461465
}
}
```
### 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_nkpz__llama2-22b-chat-wizard-uncensored
|
[
"region:us"
] |
2023-09-17T19:13:08+00:00
|
{"pretty_name": "Evaluation run of nkpz/llama2-22b-chat-wizard-uncensored", "dataset_summary": "Dataset automatically created during the evaluation run of model [nkpz/llama2-22b-chat-wizard-uncensored](https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored) 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 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_nkpz__llama2-22b-chat-wizard-uncensored\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T20:13:04.484783](https://huggingface.co/datasets/open-llm-leaderboard/details_nkpz__llama2-22b-chat-wizard-uncensored/blob/main/results_2023-09-17T20-13-04.484783.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\": 0.047399328859060404,\n \"em_stderr\": 0.002176111725660241,\n \"f1\": 0.10403313758389295,\n \"f1_stderr\": 0.0024782296933352054,\n \"acc\": 0.406947395631691,\n \"acc_stderr\": 0.010758553304204539\n },\n \"harness|drop|3\": {\n \"em\": 0.047399328859060404,\n \"em_stderr\": 0.002176111725660241,\n \"f1\": 0.10403313758389295,\n \"f1_stderr\": 0.0024782296933352054\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11144806671721001,\n \"acc_stderr\": 0.008668021353794427\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7024467245461721,\n \"acc_stderr\": 0.01284908525461465\n }\n}\n```", "repo_url": "https://huggingface.co/nkpz/llama2-22b-chat-wizard-uncensored", "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_09_17T20_13_04.484783", "path": ["**/details_harness|drop|3_2023-09-17T20-13-04.484783.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T20-13-04.484783.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T20_13_04.484783", "path": ["**/details_harness|gsm8k|5_2023-09-17T20-13-04.484783.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T20-13-04.484783.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T20_13_04.484783", "path": ["**/details_harness|winogrande|5_2023-09-17T20-13-04.484783.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T20-13-04.484783.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T20_13_04.484783", "path": ["results_2023-09-17T20-13-04.484783.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T20-13-04.484783.parquet"]}]}]}
|
2023-09-17T19:13:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of nkpz/llama2-22b-chat-wizard-uncensored
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model nkpz/llama2-22b-chat-wizard-uncensored 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 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 2023-09-17T20:13:04.484783(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 nkpz/llama2-22b-chat-wizard-uncensored",
"## 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 nkpz/llama2-22b-chat-wizard-uncensored 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 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 2023-09-17T20:13:04.484783(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 nkpz/llama2-22b-chat-wizard-uncensored",
"## 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 nkpz/llama2-22b-chat-wizard-uncensored 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 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 2023-09-17T20:13:04.484783(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
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"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
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"#### 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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6,
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175,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of nkpz/llama2-22b-chat-wizard-uncensored## 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 nkpz/llama2-22b-chat-wizard-uncensored 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 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 2023-09-17T20:13:04.484783(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"
] |
57d5819747bb474cd600a340c89ab124977a9167
|
# Dataset Card for "bees-internal"
Full length OCR of Bee material. Documents are split into multiple chunks if they contain more than 0.5 MB of text, to avoid destroying the CPU during tokenization.
Tokens (tiktoken):
<pre> "metadata": {
"model": "gpt-3.5-turbo",
"clean_text": true,
"extension": "mmd",
"recursive": true,
"global_token_count": 31652105
}
</pre>
Files:
<pre>INFO:__main__:Found 984 text files.
INFO:__main__:Performing train-test split...
INFO:__main__:Performing validation-test split...
INFO:__main__:Train size: 934
INFO:__main__:Validation size: 25
INFO:__main__:Test size: 25
</pre>
|
BEE-spoke-data/bees-internal
|
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-09-17T19:59:41+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation", "fill-mask"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}, {"config_name": "embeddings-jina-sm", "data_files": [{"split": "train", "path": "embeddings-jina-sm/train-*"}, {"split": "validation", "path": "embeddings-jina-sm/validation-*"}, {"split": "test", "path": "embeddings-jina-sm/test-*"}]}, {"config_name": "embeddings-text-nomic_text_v1", "data_files": [{"split": "train", "path": "embeddings-text-nomic_text_v1/train-*"}]}], "dataset_info": [{"config_name": "default", "features": [{"name": "relative_path", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 128834247.15, "num_examples": 1254}, {"name": "validation", "num_bytes": 3390374.925, "num_examples": 33}, {"name": "test", "num_bytes": 3390374.925, "num_examples": 33}], "download_size": 80463216, "dataset_size": 135614997.0}, {"config_name": "embeddings-jina-sm", "features": [{"name": "relative_path", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "embedding", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 133288341, "num_examples": 1254}, {"name": "validation", "num_bytes": 4916417, "num_examples": 33}, {"name": "test", "num_bytes": 2822239, "num_examples": 33}], "download_size": 84812247, "dataset_size": 141026997}, {"config_name": "embeddings-text-nomic_text_v1", "features": [{"name": "relative_path", "dtype": "string"}, {"name": "section", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "text-embedding", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 135856533, "num_examples": 1254}], "download_size": 82483500, "dataset_size": 135856533}], "thumbnail": "https://i.ibb.co/DCjs6R2/bessinternal.png"}
|
2024-02-03T08:29:44+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-fill-mask #size_categories-n<1K #language-English #license-apache-2.0 #region-us
|
# Dataset Card for "bees-internal"
Full length OCR of Bee material. Documents are split into multiple chunks if they contain more than 0.5 MB of text, to avoid destroying the CPU during tokenization.
Tokens (tiktoken):
<pre> "metadata": {
"model": "gpt-3.5-turbo",
"clean_text": true,
"extension": "mmd",
"recursive": true,
"global_token_count": 31652105
}
</pre>
Files:
<pre>INFO:__main__:Found 984 text files.
INFO:__main__:Performing train-test split...
INFO:__main__:Performing validation-test split...
INFO:__main__:Train size: 934
INFO:__main__:Validation size: 25
INFO:__main__:Test size: 25
</pre>
|
[
"# Dataset Card for \"bees-internal\"\n\n\nFull length OCR of Bee material. Documents are split into multiple chunks if they contain more than 0.5 MB of text, to avoid destroying the CPU during tokenization.\n\nTokens (tiktoken):\n\n<pre> "metadata": {\n "model": "gpt-3.5-turbo",\n "clean_text": true,\n "extension": "mmd",\n "recursive": true,\n "global_token_count": 31652105\n }\n</pre>\n\nFiles:\n\n<pre>INFO:__main__:Found 984 text files.\nINFO:__main__:Performing train-test split...\nINFO:__main__:Performing validation-test split...\nINFO:__main__:Train size: 934\nINFO:__main__:Validation size: 25\nINFO:__main__:Test size: 25\n</pre>"
] |
[
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for \"bees-internal\"\n\n\nFull length OCR of Bee material. Documents are split into multiple chunks if they contain more than 0.5 MB of text, to avoid destroying the CPU during tokenization.\n\nTokens (tiktoken):\n\n<pre> "metadata": {\n "model": "gpt-3.5-turbo",\n "clean_text": true,\n "extension": "mmd",\n "recursive": true,\n "global_token_count": 31652105\n }\n</pre>\n\nFiles:\n\n<pre>INFO:__main__:Found 984 text files.\nINFO:__main__:Performing train-test split...\nINFO:__main__:Performing validation-test split...\nINFO:__main__:Train size: 934\nINFO:__main__:Validation size: 25\nINFO:__main__:Test size: 25\n</pre>"
] |
[
50,
241
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #size_categories-n<1K #language-English #license-apache-2.0 #region-us \n# Dataset Card for \"bees-internal\"\n\n\nFull length OCR of Bee material. Documents are split into multiple chunks if they contain more than 0.5 MB of text, to avoid destroying the CPU during tokenization.\n\nTokens (tiktoken):\n\n<pre> "metadata": {\n "model": "gpt-3.5-turbo",\n "clean_text": true,\n "extension": "mmd",\n "recursive": true,\n "global_token_count": 31652105\n }\n</pre>\n\nFiles:\n\n<pre>INFO:__main__:Found 984 text files.\nINFO:__main__:Performing train-test split...\nINFO:__main__:Performing validation-test split...\nINFO:__main__:Train size: 934\nINFO:__main__:Validation size: 25\nINFO:__main__:Test size: 25\n</pre>"
] |
1d007a5fde738596155fc1fcac93f2d73b1c645d
|
# Dataset Card for "gtzan_all_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
barto17/gtzan_all_preprocessed
|
[
"region:us"
] |
2023-09-17T20:20:15+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "blues", "1": "classical", "2": "country", "3": "disco", "4": "hiphop", "5": "jazz", "6": "metal", "7": "pop", "8": "reggae", "9": "rock"}}}}, {"name": "input_values", "sequence": "float32"}, {"name": "attention_mask", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 3452159816, "num_examples": 899}, {"name": "test", "num_bytes": 384000696, "num_examples": 100}], "download_size": 1923103923, "dataset_size": 3836160512}}
|
2023-09-17T20:21:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gtzan_all_preprocessed"
More Information needed
|
[
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gtzan_all_preprocessed\"\n\nMore Information needed"
] |
ec50b53b8a8682587a8c83a7a538265d53a68b32
|
# Набор данных CarveSet V2.0
Мы собрали обширный набор данных, охватывающий наиболее распространенные классы объектов,
предназначенных для удаления фона.
## Характеристики:
Он включает фотографии объектов, принадлежащих 9 различным классам.
### Распределение классов объектов в наборе данных CarveSet V2.0:
| Класс объектов | Кол-во изображений |
|-------------------------------|--------------------|
| 🚗 автомобили | 1878 |
| 👗 одежда | 1840 |
| 🏠 предметы быта | 1878 |
| 📱 электроника | 1806 |
| 🧸 детские игрушки | 1785 |
| 🍳 кухонные принадлежности | 1878 |
| 👨👩👧👦 люди | 1777 |
| 🏡 объекты в жилых помещениях | 1777 |
| 🐾 животные | 1878 |
Общее количество изображений в наборе данных: **16 497**.
### Разбивка на выборки:
- Тестовая выборка: **2000** пар изображений.
- Валидационная выборка: **2000** пар изображений.
- Тренировочная выборка: **12 497** пар изображений.
### Информация о базе изображений в наборе данных
1. **CarveSet** - содержит 3 172 изображения высокого качества размером примерно 2500x2500 пикселей, собранных вручную из [Pexels](https://www.pexels.com/), [Unsplash](https://unsplash.com/).
2. **SOPG** - состоит из 13 325 изображений, увеличенных в 4 раза из набора данных [SOPG](https://huggingface.co/datasets/absinc/sopg),
размером примерно 2048x1536 пикселей.
## Файловая структура набора данных
- `carveset2` - База изображений.
- `carveset2/train` - Тренировочная выборка.
- `carveset2/train/images` - RGB изображения.
- `carveset2/train/masks` - Маски.
- `carveset2/train/trimaps` - Тримапы.
- `carveset2/val` - Валидационная выборка.
- `carveset2/val/images` - RGB изображения.
- `carveset2/val/masks` - Маски.
- `carveset2/val/trimaps` - Тримапы.
- `carveset2/test` - Тестовая выборка.
- `carveset2/test/images` - RGB изображения.
- `carveset2/test/masks` - Маски.
- `carveset2/test/trimaps` - Тримапы.
- `carveset2/train.csv` - Таблица с путями и лицензиями для RGB изображений.
- `carveset2/val.csv` - Таблица с путями и лицензиями для RGB изображений.
- `carveset2/test.csv` - Таблица с путями и лицензиями для RGB изображений.
- `licenses/*.txt` - Текст лицензий
- `terms_of_use.pdf` - Условия использования
## Лицензии
1. RGB изображения
1. [Pexels License](https://www.pexels.com/ru-RU/license/) - `pexels` в таблицах
2. [Unsplash License](https://unsplash.com/license) - `unsplash` в таблицах
3. [SOPG MIT License](https://huggingface.co/datasets/absinc/sopg#license) - `mit` в таблицах
2. Разметка - [Apache License 2.0](https://github.com/OPHoperHPO/freezed_carvekit_2023/blob/master/LICENSE)
Подробнее о лицензиях можно прочитать в [terms_of_use.pdf](terms_of_use.pdf).
## Скачать:
Набор данных предоставляется на [специальных условиях использования.](terms_of_use.pdf)
Скачивая и используя набор данных, вы соглашаетесь с условиями использования.
[Скачать с HuggingFace](https://huggingface.co/datasets/Carve/carveset)
[Скачать файл (carveset2.zip)](https://huggingface.co/datasets/Carve/carveset/blob/main/carveset2.zip)
|
Carve/carveset
|
[
"task_categories:image-segmentation",
"size_categories:10K<n<100K",
"license:other",
"computer vision",
"background removal",
"SOD",
"Salient Object Detection",
"Segmentation",
"Image Segmentation",
"region:us"
] |
2023-09-17T20:25:06+00:00
|
{"license": "other", "size_categories": ["10K<n<100K"], "task_categories": ["image-segmentation"], "pretty_name": "CarveSet", "tags": ["computer vision", "background removal", "SOD", "Salient Object Detection", "Segmentation", "Image Segmentation"]}
|
2023-09-24T04:13:05+00:00
|
[] |
[] |
TAGS
#task_categories-image-segmentation #size_categories-10K<n<100K #license-other #computer vision #background removal #SOD #Salient Object Detection #Segmentation #Image Segmentation #region-us
|
Набор данных CarveSet V2.0
==========================
Мы собрали обширный набор данных, охватывающий наиболее распространенные классы объектов,
предназначенных для удаления фона.
Характеристики:
---------------
Он включает фотографии объектов, принадлежащих 9 различным классам.
### Распределение классов объектов в наборе данных CarveSet V2.0:
Общее количество изображений в наборе данных: 16 497.
### Разбивка на выборки:
* Тестовая выборка: 2000 пар изображений.
* Валидационная выборка: 2000 пар изображений.
* Тренировочная выборка: 12 497 пар изображений.
### Информация о базе изображений в наборе данных
1. CarveSet - содержит 3 172 изображения высокого качества размером примерно 2500x2500 пикселей, собранных вручную из Pexels, Unsplash.
2. SOPG - состоит из 13 325 изображений, увеличенных в 4 раза из набора данных SOPG,
размером примерно 2048x1536 пикселей.
Файловая структура набора данных
--------------------------------
* 'carveset2' - База изображений.
* 'carveset2/train' - Тренировочная выборка.
* 'carveset2/train/images' - RGB изображения.
* 'carveset2/train/masks' - Маски.
* 'carveset2/train/trimaps' - Тримапы.
* 'carveset2/val' - Валидационная выборка.
* 'carveset2/val/images' - RGB изображения.
* 'carveset2/val/masks' - Маски.
* 'carveset2/val/trimaps' - Тримапы.
* 'carveset2/test' - Тестовая выборка.
* 'carveset2/test/images' - RGB изображения.
* 'carveset2/test/masks' - Маски.
* 'carveset2/test/trimaps' - Тримапы.
* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.
* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.
* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.
* 'licenses/\*.txt' - Текст лицензий
* 'terms\_of\_use.pdf' - Условия использования
Лицензии
--------
1. RGB изображения
1. Pexels License - 'pexels' в таблицах
2. Unsplash License - 'unsplash' в таблицах
3. SOPG MIT License - 'mit' в таблицах
2. Разметка - Apache License 2.0
Подробнее о лицензиях можно прочитать в terms\_of\_use.pdf.
Скачать:
--------
Набор данных предоставляется на специальных условиях использования.
Скачивая и используя набор данных, вы соглашаетесь с условиями использования.
Скачать с HuggingFace
Скачать файл (URL)
|
[
"### Распределение классов объектов в наборе данных CarveSet V2.0:\n\n\n\nОбщее количество изображений в наборе данных: 16 497.",
"### Разбивка на выборки:\n\n\n* Тестовая выборка: 2000 пар изображений.\n* Валидационная выборка: 2000 пар изображений.\n* Тренировочная выборка: 12 497 пар изображений.",
"### Информация о базе изображений в наборе данных\n\n\n1. CarveSet - содержит 3 172 изображения высокого качества размером примерно 2500x2500 пикселей, собранных вручную из Pexels, Unsplash.\n2. SOPG - состоит из 13 325 изображений, увеличенных в 4 раза из набора данных SOPG,\nразмером примерно 2048x1536 пикселей.\n\n\nФайловая структура набора данных\n--------------------------------\n\n\n* 'carveset2' - База изображений.\n* 'carveset2/train' - Тренировочная выборка.\n* 'carveset2/train/images' - RGB изображения.\n* 'carveset2/train/masks' - Маски.\n* 'carveset2/train/trimaps' - Тримапы.\n* 'carveset2/val' - Валидационная выборка.\n* 'carveset2/val/images' - RGB изображения.\n* 'carveset2/val/masks' - Маски.\n* 'carveset2/val/trimaps' - Тримапы.\n* 'carveset2/test' - Тестовая выборка.\n* 'carveset2/test/images' - RGB изображения.\n* 'carveset2/test/masks' - Маски.\n* 'carveset2/test/trimaps' - Тримапы.\n* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.\n* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.\n* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.\n* 'licenses/\\*.txt' - Текст лицензий\n* 'terms\\_of\\_use.pdf' - Условия использования\n\n\nЛицензии\n--------\n\n\n1. RGB изображения\n\t1. Pexels License - 'pexels' в таблицах\n\t2. Unsplash License - 'unsplash' в таблицах\n\t3. SOPG MIT License - 'mit' в таблицах\n2. Разметка - Apache License 2.0\n\n\nПодробнее о лицензиях можно прочитать в terms\\_of\\_use.pdf.\n\n\nСкачать:\n--------\n\n\nНабор данных предоставляется на специальных условиях использования.\nСкачивая и используя набор данных, вы соглашаетесь с условиями использования.\n\n\nСкачать с HuggingFace\n\n\nСкачать файл (URL)"
] |
[
"TAGS\n#task_categories-image-segmentation #size_categories-10K<n<100K #license-other #computer vision #background removal #SOD #Salient Object Detection #Segmentation #Image Segmentation #region-us \n",
"### Распределение классов объектов в наборе данных CarveSet V2.0:\n\n\n\nОбщее количество изображений в наборе данных: 16 497.",
"### Разбивка на выборки:\n\n\n* Тестовая выборка: 2000 пар изображений.\n* Валидационная выборка: 2000 пар изображений.\n* Тренировочная выборка: 12 497 пар изображений.",
"### Информация о базе изображений в наборе данных\n\n\n1. CarveSet - содержит 3 172 изображения высокого качества размером примерно 2500x2500 пикселей, собранных вручную из Pexels, Unsplash.\n2. SOPG - состоит из 13 325 изображений, увеличенных в 4 раза из набора данных SOPG,\nразмером примерно 2048x1536 пикселей.\n\n\nФайловая структура набора данных\n--------------------------------\n\n\n* 'carveset2' - База изображений.\n* 'carveset2/train' - Тренировочная выборка.\n* 'carveset2/train/images' - RGB изображения.\n* 'carveset2/train/masks' - Маски.\n* 'carveset2/train/trimaps' - Тримапы.\n* 'carveset2/val' - Валидационная выборка.\n* 'carveset2/val/images' - RGB изображения.\n* 'carveset2/val/masks' - Маски.\n* 'carveset2/val/trimaps' - Тримапы.\n* 'carveset2/test' - Тестовая выборка.\n* 'carveset2/test/images' - RGB изображения.\n* 'carveset2/test/masks' - Маски.\n* 'carveset2/test/trimaps' - Тримапы.\n* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.\n* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.\n* 'carveset2/URL' - Таблица с путями и лицензиями для RGB изображений.\n* 'licenses/\\*.txt' - Текст лицензий\n* 'terms\\_of\\_use.pdf' - Условия использования\n\n\nЛицензии\n--------\n\n\n1. RGB изображения\n\t1. Pexels License - 'pexels' в таблицах\n\t2. Unsplash License - 'unsplash' в таблицах\n\t3. SOPG MIT License - 'mit' в таблицах\n2. Разметка - Apache License 2.0\n\n\nПодробнее о лицензиях можно прочитать в terms\\_of\\_use.pdf.\n\n\nСкачать:\n--------\n\n\nНабор данных предоставляется на специальных условиях использования.\nСкачивая и используя набор данных, вы соглашаетесь с условиями использования.\n\n\nСкачать с HuggingFace\n\n\nСкачать файл (URL)"
] |
[
62,
32,
48,
505
] |
[
"passage: TAGS\n#task_categories-image-segmentation #size_categories-10K<n<100K #license-other #computer vision #background removal #SOD #Salient Object Detection #Segmentation #Image Segmentation #region-us \n### Распределение классов объектов в наборе данных CarveSet V2.0:\n\n\n\nОбщее количество изображений в наборе данных: 16 497.### Разбивка на выборки:\n\n\n* Тестовая выборка: 2000 пар изображений.\n* Валидационная выборка: 2000 пар изображений.\n* Тренировочная выборка: 12 497 пар изображений."
] |
d796ebb26a00a62ecf7c42f15e376abd24371d54
|
# Dataset Card for "guanaco-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ameemazainab/guanaco-llama2-1k
|
[
"region:us"
] |
2023-09-17T20:31:30+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1654448, "num_examples": 1000}], "download_size": 0, "dataset_size": 1654448}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-19T16:53:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-1k\"\n\nMore Information needed"
] |
b7b6c4d4725bf1af215637261273d88687b27a8d
|
# Dataset Card for "SYSK_Transcripts"
Transcriptions + Summaries of _Stuff You Should Know_. DOI 10.17605/OSF.IO/VM9NT
```python
Dataset({
features: ['id', 'title', 'link', 'desc', 'summary', 'pubDate', 'pubFormatted', 'enc_len', 'enc_type', 'audio_url', 'transcript'],
num_rows: 1965
})
```
## citation
```
@article{https://doi.org/10.17605/osf.io/vm9nt,
doi = {10.17605/OSF.IO/VM9NT},
url = {https://osf.io/vm9nt/},
author = {Pierson, Britt},
keywords = {audio processing, audio timestamp, corpus, dataset, natural language processing, nlp, podcast, podcasts trancripts, podcasts transcript, podcast transcript, podcast transcripts, sentiment analysis, transcript, transcripts, transcripts of podcast, transcripts of podcasts},
title = {"Stuff You Should Know" Podcast Transcripts - Full Dataset with Transcript of All Episodes (SYSK_Transcripts)},
publisher = {Open Science Framework},
year = {2022},
copyright = {CC-By Attribution 4.0 International}
}
```
|
BEE-spoke-data/SYSK-Transcripts
|
[
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"region:us"
] |
2023-09-17T20:38:31+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "dataset_info": [{"config_name": "default", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "desc", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "pubDate", "dtype": "string"}, {"name": "pubFormatted", "dtype": "string"}, {"name": "enc_len", "dtype": "string"}, {"name": "enc_type", "dtype": "string"}, {"name": "audio_url", "dtype": "string"}, {"name": "transcript", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 72117648, "num_examples": 1965}], "download_size": 40830798, "dataset_size": 72117648}, {"config_name": "seg-no-ads", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "desc", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 69771386, "num_examples": 1911}], "download_size": 40026675, "dataset_size": 69771386}, {"config_name": "segmented", "features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "link", "dtype": "string"}, {"name": "desc", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 69521425.32519084, "num_examples": 1911}], "download_size": 40832338, "dataset_size": 69521425.32519084}], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}, {"config_name": "seg-no-ads", "data_files": [{"split": "train", "path": "seg-no-ads/train-*"}]}, {"config_name": "segmented", "data_files": [{"split": "train", "path": "segmented/train-*"}]}]}
|
2023-11-08T01:40:22+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-cc-by-4.0 #region-us
|
# Dataset Card for "SYSK_Transcripts"
Transcriptions + Summaries of _Stuff You Should Know_. DOI 10.17605/OSF.IO/VM9NT
## citation
|
[
"# Dataset Card for \"SYSK_Transcripts\"\n\nTranscriptions + Summaries of _Stuff You Should Know_. DOI 10.17605/OSF.IO/VM9NT",
"## citation"
] |
[
"TAGS\n#size_categories-1K<n<10K #license-cc-by-4.0 #region-us \n",
"# Dataset Card for \"SYSK_Transcripts\"\n\nTranscriptions + Summaries of _Stuff You Should Know_. DOI 10.17605/OSF.IO/VM9NT",
"## citation"
] |
[
27,
42,
3
] |
[
"passage: TAGS\n#size_categories-1K<n<10K #license-cc-by-4.0 #region-us \n# Dataset Card for \"SYSK_Transcripts\"\n\nTranscriptions + Summaries of _Stuff You Should Know_. DOI 10.17605/OSF.IO/VM9NT## citation"
] |
2bb03b482ee96d7e18ed889440e0e9dfa51c35cd
|
# Dataset Card for Evaluation run of deepse/CodeUp-Llama-2-13b-chat-hf
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf
- **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 [deepse/CodeUp-Llama-2-13b-chat-hf](https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf) 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 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_deepse__CodeUp-Llama-2-13b-chat-hf",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T21:42:48.305634](https://huggingface.co/datasets/open-llm-leaderboard/details_deepse__CodeUp-Llama-2-13b-chat-hf/blob/main/results_2023-09-17T21-42-48.305634.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": 0.1782718120805369,
"em_stderr": 0.003919630092588375,
"f1": 0.2387195889261742,
"f1_stderr": 0.003944947017182046,
"acc": 0.448727630233375,
"acc_stderr": 0.011074189612085313
},
"harness|drop|3": {
"em": 0.1782718120805369,
"em_stderr": 0.003919630092588375,
"f1": 0.2387195889261742,
"f1_stderr": 0.003944947017182046
},
"harness|gsm8k|5": {
"acc": 0.15238817285822592,
"acc_stderr": 0.009899572254794204
},
"harness|winogrande|5": {
"acc": 0.745067087608524,
"acc_stderr": 0.012248806969376422
}
}
```
### 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_deepse__CodeUp-Llama-2-13b-chat-hf
|
[
"region:us"
] |
2023-09-17T20:42:52+00:00
|
{"pretty_name": "Evaluation run of deepse/CodeUp-Llama-2-13b-chat-hf", "dataset_summary": "Dataset automatically created during the evaluation run of model [deepse/CodeUp-Llama-2-13b-chat-hf](https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf) 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 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_deepse__CodeUp-Llama-2-13b-chat-hf\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T21:42:48.305634](https://huggingface.co/datasets/open-llm-leaderboard/details_deepse__CodeUp-Llama-2-13b-chat-hf/blob/main/results_2023-09-17T21-42-48.305634.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\": 0.1782718120805369,\n \"em_stderr\": 0.003919630092588375,\n \"f1\": 0.2387195889261742,\n \"f1_stderr\": 0.003944947017182046,\n \"acc\": 0.448727630233375,\n \"acc_stderr\": 0.011074189612085313\n },\n \"harness|drop|3\": {\n \"em\": 0.1782718120805369,\n \"em_stderr\": 0.003919630092588375,\n \"f1\": 0.2387195889261742,\n \"f1_stderr\": 0.003944947017182046\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.15238817285822592,\n \"acc_stderr\": 0.009899572254794204\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.745067087608524,\n \"acc_stderr\": 0.012248806969376422\n }\n}\n```", "repo_url": "https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf", "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_09_17T21_42_48.305634", "path": ["**/details_harness|drop|3_2023-09-17T21-42-48.305634.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T21-42-48.305634.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T21_42_48.305634", "path": ["**/details_harness|gsm8k|5_2023-09-17T21-42-48.305634.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T21-42-48.305634.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T21_42_48.305634", "path": ["**/details_harness|winogrande|5_2023-09-17T21-42-48.305634.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T21-42-48.305634.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T21_42_48.305634", "path": ["results_2023-09-17T21-42-48.305634.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T21-42-48.305634.parquet"]}]}]}
|
2023-09-17T20:43:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of deepse/CodeUp-Llama-2-13b-chat-hf
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model deepse/CodeUp-Llama-2-13b-chat-hf 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 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 2023-09-17T21:42:48.305634(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 deepse/CodeUp-Llama-2-13b-chat-hf",
"## 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 deepse/CodeUp-Llama-2-13b-chat-hf 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 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 2023-09-17T21:42:48.305634(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 deepse/CodeUp-Llama-2-13b-chat-hf",
"## 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 deepse/CodeUp-Llama-2-13b-chat-hf 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 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 2023-09-17T21:42:48.305634(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",
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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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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of deepse/CodeUp-Llama-2-13b-chat-hf## 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 deepse/CodeUp-Llama-2-13b-chat-hf 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 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 2023-09-17T21:42:48.305634(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"
] |
f9bd53ae4231dde7fdc782d31050e20b71f923d0
|
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains the German and corresponding Czech names for almost 5k places in Czech Republic. It has been generated using [this code](https://github.com/DebasishDhal/Minor-Stuff/blob/main/paired-placenames-scrapping/german-czech.py).
Many of these names are related to each other. Some German names are literal translation of the Czech names (or maybe the other way around), some are phonetic modifications while some are unrelated.
## Dataset Creation
### Source Data
[English wiki page containing German exonyms for places in Czech Republic](https://en.wikipedia.org/wiki/List_of_German_names_for_places_in_the_Czech_Republic)
|
DebasishDhal99/german-czech-paired-placenames
|
[
"task_categories:translation",
"size_categories:1K<n<10K",
"language:de",
"language:cs",
"license:mit",
"history",
"region:us"
] |
2023-09-17T20:57:51+00:00
|
{"language": ["de", "cs"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "tags": ["history"]}
|
2023-12-17T09:44:51+00:00
|
[] |
[
"de",
"cs"
] |
TAGS
#task_categories-translation #size_categories-1K<n<10K #language-German #language-Czech #license-mit #history #region-us
|
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset contains the German and corresponding Czech names for almost 5k places in Czech Republic. It has been generated using this code.
Many of these names are related to each other. Some German names are literal translation of the Czech names (or maybe the other way around), some are phonetic modifications while some are unrelated.
## Dataset Creation
### Source Data
English wiki page containing German exonyms for places in Czech Republic
|
[
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains the German and corresponding Czech names for almost 5k places in Czech Republic. It has been generated using this code.\nMany of these names are related to each other. Some German names are literal translation of the Czech names (or maybe the other way around), some are phonetic modifications while some are unrelated.",
"## Dataset Creation",
"### Source Data\nEnglish wiki page containing German exonyms for places in Czech Republic"
] |
[
"TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-German #language-Czech #license-mit #history #region-us \n",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains the German and corresponding Czech names for almost 5k places in Czech Republic. It has been generated using this code.\nMany of these names are related to each other. Some German names are literal translation of the Czech names (or maybe the other way around), some are phonetic modifications while some are unrelated.",
"## Dataset Creation",
"### Source Data\nEnglish wiki page containing German exonyms for places in Czech Republic"
] |
[
45,
24,
77,
5,
18
] |
[
"passage: TAGS\n#task_categories-translation #size_categories-1K<n<10K #language-German #language-Czech #license-mit #history #region-us \n## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset contains the German and corresponding Czech names for almost 5k places in Czech Republic. It has been generated using this code.\nMany of these names are related to each other. Some German names are literal translation of the Czech names (or maybe the other way around), some are phonetic modifications while some are unrelated.## Dataset Creation### Source Data\nEnglish wiki page containing German exonyms for places in Czech Republic"
] |
7d4ec813ad18a8f18b1a9b11a09dd1f334f60056
|
# Dataset Card for "llama2-chinese-couplet-770k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
chenqile09/llama2-chinese-couplet-770k
|
[
"region:us"
] |
2023-09-17T21:02:09+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 261365259, "num_examples": 770491}, {"name": "validation", "num_bytes": 1358512, "num_examples": 4000}], "download_size": 101554099, "dataset_size": 262723771}}
|
2023-09-17T21:02:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "llama2-chinese-couplet-770k"
More Information needed
|
[
"# Dataset Card for \"llama2-chinese-couplet-770k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"llama2-chinese-couplet-770k\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"llama2-chinese-couplet-770k\"\n\nMore Information needed"
] |
6c5fe323a28f91f5f5c909fc93d606b2b3904dc2
|
# Dataset Card for "corpus_1_clustered_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/corpus_1_clustered_formatted
|
[
"region:us"
] |
2023-09-17T21:24:57+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "0", "path": "data/0-*"}, {"split": "1", "path": "data/1-*"}, {"split": "2", "path": "data/2-*"}, {"split": "3", "path": "data/3-*"}, {"split": "4", "path": "data/4-*"}, {"split": "5", "path": "data/5-*"}, {"split": "6", "path": "data/6-*"}, {"split": "7", "path": "data/7-*"}, {"split": "8", "path": "data/8-*"}, {"split": "9", "path": "data/9-*"}, {"split": "10", "path": "data/10-*"}, {"split": "11", "path": "data/11-*"}, {"split": "12", "path": "data/12-*"}, {"split": "13", "path": "data/13-*"}, {"split": "14", "path": "data/14-*"}, {"split": "15", "path": "data/15-*"}, {"split": "16", "path": "data/16-*"}, {"split": "17", "path": "data/17-*"}, {"split": "18", "path": "data/18-*"}, {"split": "19", "path": "data/19-*"}, {"split": "20", "path": "data/20-*"}, {"split": "21", "path": "data/21-*"}, {"split": "22", "path": "data/22-*"}, {"split": "23", "path": "data/23-*"}, {"split": "24", "path": "data/24-*"}, {"split": "25", "path": "data/25-*"}, {"split": "26", "path": "data/26-*"}, {"split": "27", "path": "data/27-*"}, {"split": "28", "path": "data/28-*"}, {"split": "29", "path": "data/29-*"}, {"split": "30", "path": "data/30-*"}, {"split": "31", "path": "data/31-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "0", "num_bytes": 57988271, "num_examples": 45617}, {"name": "1", "num_bytes": 80924315, "num_examples": 57017}, {"name": "2", "num_bytes": 146972588, "num_examples": 59271}, {"name": "3", "num_bytes": 55446301, "num_examples": 41544}, {"name": "4", "num_bytes": 126072016, "num_examples": 72587}, {"name": "5", "num_bytes": 60462897, "num_examples": 34080}, {"name": "6", "num_bytes": 42695954, "num_examples": 30203}, {"name": "7", "num_bytes": 86334809, "num_examples": 36365}, {"name": "8", "num_bytes": 205182212, "num_examples": 82654}, {"name": "9", "num_bytes": 65097365, "num_examples": 34266}, {"name": "10", "num_bytes": 18143136, "num_examples": 22221}, {"name": "11", "num_bytes": 85400025, "num_examples": 43502}, {"name": "12", "num_bytes": 145547717, "num_examples": 90729}, {"name": "13", "num_bytes": 68582287, "num_examples": 77149}, {"name": "14", "num_bytes": 56976092, "num_examples": 53042}, {"name": "15", "num_bytes": 86545425, "num_examples": 49714}, {"name": "16", "num_bytes": 94867422, "num_examples": 51517}, {"name": "17", "num_bytes": 59847974, "num_examples": 39622}, {"name": "18", "num_bytes": 132858143, "num_examples": 54708}, {"name": "19", "num_bytes": 32550229, "num_examples": 21282}, {"name": "20", "num_bytes": 94382189, "num_examples": 42830}, {"name": "21", "num_bytes": 112712389, "num_examples": 41104}, {"name": "22", "num_bytes": 59089685, "num_examples": 42586}, {"name": "23", "num_bytes": 90127682, "num_examples": 35260}, {"name": "24", "num_bytes": 71313692, "num_examples": 45451}, {"name": "25", "num_bytes": 131908904, "num_examples": 55974}, {"name": "26", "num_bytes": 61742004, "num_examples": 60773}, {"name": "27", "num_bytes": 22254025, "num_examples": 29582}, {"name": "28", "num_bytes": 63023032, "num_examples": 47177}, {"name": "29", "num_bytes": 36460715, "num_examples": 32707}, {"name": "30", "num_bytes": 12331184, "num_examples": 15399}, {"name": "31", "num_bytes": 26522434, "num_examples": 26952}], "download_size": 1331217922, "dataset_size": 2490363113}}
|
2023-09-17T21:31:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "corpus_1_clustered_formatted"
More Information needed
|
[
"# Dataset Card for \"corpus_1_clustered_formatted\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"corpus_1_clustered_formatted\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_clustered_formatted\"\n\nMore Information needed"
] |
d565eb2ddab9706095137834195ab99015d26da8
|
# Dataset Card for "dw_communities_content"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jkruk/dw_communities_content
|
[
"region:us"
] |
2023-09-17T21:32:30+00:00
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 86184647.40351267, "num_examples": 579625}], "download_size": 50409061, "dataset_size": 86184647.40351267}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T21:52:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dw_communities_content"
More Information needed
|
[
"# Dataset Card for \"dw_communities_content\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dw_communities_content\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dw_communities_content\"\n\nMore Information needed"
] |
29f22436577594addf9e67eb2fc22bd1e28a7da1
|
# Dataset Card for "prm800k-train-direct-prediction-0-02validiation-seed42-encoded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tongyx361/prm800k-train-direct-prediction-0-02validiation-seed42-encoded
|
[
"region:us"
] |
2023-09-17T21:46:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 308232504, "num_examples": 85194}, {"name": "validation", "num_bytes": 5818260, "num_examples": 1818}], "download_size": 32445039, "dataset_size": 314050764}}
|
2023-09-17T21:46:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prm800k-train-direct-prediction-0-02validiation-seed42-encoded"
More Information needed
|
[
"# Dataset Card for \"prm800k-train-direct-prediction-0-02validiation-seed42-encoded\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prm800k-train-direct-prediction-0-02validiation-seed42-encoded\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prm800k-train-direct-prediction-0-02validiation-seed42-encoded\"\n\nMore Information needed"
] |
557caeb82c881f5dd50d2d2d342032dce59a0700
|
# Dataset Card for "neon_dreambooth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Navu45/neon_dreambooth
|
[
"region:us"
] |
2023-09-17T21:51:34+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 222317.0, "num_examples": 5}], "download_size": 223132, "dataset_size": 222317.0}}
|
2023-09-17T22:45:09+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "neon_dreambooth"
More Information needed
|
[
"# Dataset Card for \"neon_dreambooth\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"neon_dreambooth\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"neon_dreambooth\"\n\nMore Information needed"
] |
832a321e7e9633e8f4e2453b2d3d7325dd59c77d
|
# Dataset Card for "corpus_1_classifier_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HydraLM/corpus_1_classifier_data
|
[
"region:us"
] |
2023-09-17T22:05:48+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1103014425, "num_examples": 1472917}], "download_size": 669772750, "dataset_size": 1103014425}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-17T22:08:38+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "corpus_1_classifier_data"
More Information needed
|
[
"# Dataset Card for \"corpus_1_classifier_data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"corpus_1_classifier_data\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"corpus_1_classifier_data\"\n\nMore Information needed"
] |
93212730000a7191c501ebc672fee76bf578a22f
|
# Dataset Card for Evaluation run of w601sxs/b1ade-1b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/w601sxs/b1ade-1b
- **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 [w601sxs/b1ade-1b](https://huggingface.co/w601sxs/b1ade-1b) 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 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_w601sxs__b1ade-1b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-17T23:22:23.773676](https://huggingface.co/datasets/open-llm-leaderboard/details_w601sxs__b1ade-1b/blob/main/results_2023-09-17T23-22-23.773676.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": 0.002936241610738255,
"em_stderr": 0.0005541113054709937,
"f1": 0.042455956375839043,
"f1_stderr": 0.0012020859118158512,
"acc": 0.2721723005338167,
"acc_stderr": 0.00807495644821229
},
"harness|drop|3": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054709937,
"f1": 0.042455956375839043,
"f1_stderr": 0.0012020859118158512
},
"harness|gsm8k|5": {
"acc": 0.006065200909780136,
"acc_stderr": 0.0021386703014604556
},
"harness|winogrande|5": {
"acc": 0.5382794001578532,
"acc_stderr": 0.014011242594964125
}
}
```
### 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_w601sxs__b1ade-1b
|
[
"region:us"
] |
2023-09-17T22:22:27+00:00
|
{"pretty_name": "Evaluation run of w601sxs/b1ade-1b", "dataset_summary": "Dataset automatically created during the evaluation run of model [w601sxs/b1ade-1b](https://huggingface.co/w601sxs/b1ade-1b) 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 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_w601sxs__b1ade-1b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-17T23:22:23.773676](https://huggingface.co/datasets/open-llm-leaderboard/details_w601sxs__b1ade-1b/blob/main/results_2023-09-17T23-22-23.773676.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\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709937,\n \"f1\": 0.042455956375839043,\n \"f1_stderr\": 0.0012020859118158512,\n \"acc\": 0.2721723005338167,\n \"acc_stderr\": 0.00807495644821229\n },\n \"harness|drop|3\": {\n \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054709937,\n \"f1\": 0.042455956375839043,\n \"f1_stderr\": 0.0012020859118158512\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006065200909780136,\n \"acc_stderr\": 0.0021386703014604556\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5382794001578532,\n \"acc_stderr\": 0.014011242594964125\n }\n}\n```", "repo_url": "https://huggingface.co/w601sxs/b1ade-1b", "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_09_17T23_22_23.773676", "path": ["**/details_harness|drop|3_2023-09-17T23-22-23.773676.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-17T23-22-23.773676.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_17T23_22_23.773676", "path": ["**/details_harness|gsm8k|5_2023-09-17T23-22-23.773676.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-17T23-22-23.773676.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_17T23_22_23.773676", "path": ["**/details_harness|winogrande|5_2023-09-17T23-22-23.773676.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-17T23-22-23.773676.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_17T23_22_23.773676", "path": ["results_2023-09-17T23-22-23.773676.parquet"]}, {"split": "latest", "path": ["results_2023-09-17T23-22-23.773676.parquet"]}]}]}
|
2023-09-17T22:22:35+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of w601sxs/b1ade-1b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model w601sxs/b1ade-1b 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 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 2023-09-17T23:22:23.773676(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 w601sxs/b1ade-1b",
"## 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 w601sxs/b1ade-1b 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 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 2023-09-17T23:22:23.773676(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 w601sxs/b1ade-1b",
"## 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 w601sxs/b1ade-1b 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 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 2023-09-17T23:22:23.773676(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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"### Data Splits",
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"### Curation Rationale",
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"#### Initial Data Collection and Normalization",
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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 w601sxs/b1ade-1b## 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 w601sxs/b1ade-1b 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 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 2023-09-17T23:22:23.773676(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"
] |
6995b13cdd5fd9d053d9d9a12f19cd9bd590533b
|
# Dataset Card for Evaluation run of mncai/SGPT-1.3B-insurance-epoch10
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mncai/SGPT-1.3B-insurance-epoch10
- **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 [mncai/SGPT-1.3B-insurance-epoch10](https://huggingface.co/mncai/SGPT-1.3B-insurance-epoch10) 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 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_mncai__SGPT-1.3B-insurance-epoch10",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T00:09:04.877490](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__SGPT-1.3B-insurance-epoch10/blob/main/results_2023-09-18T00-09-04.877490.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": 0.0,
"em_stderr": 0.0,
"f1": 1.99244966442953e-05,
"f1_stderr": 5.6438034448796525e-06,
"acc": 0.25453827940015783,
"acc_stderr": 0.007025085047248852
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 1.99244966442953e-05,
"f1_stderr": 5.6438034448796525e-06
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5090765588003157,
"acc_stderr": 0.014050170094497704
}
}
```
### 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_mncai__SGPT-1.3B-insurance-epoch10
|
[
"region:us"
] |
2023-09-17T23:09:10+00:00
|
{"pretty_name": "Evaluation run of mncai/SGPT-1.3B-insurance-epoch10", "dataset_summary": "Dataset automatically created during the evaluation run of model [mncai/SGPT-1.3B-insurance-epoch10](https://huggingface.co/mncai/SGPT-1.3B-insurance-epoch10) 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 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_mncai__SGPT-1.3B-insurance-epoch10\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-18T00:09:04.877490](https://huggingface.co/datasets/open-llm-leaderboard/details_mncai__SGPT-1.3B-insurance-epoch10/blob/main/results_2023-09-18T00-09-04.877490.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\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 1.99244966442953e-05,\n \"f1_stderr\": 5.6438034448796525e-06,\n \"acc\": 0.25453827940015783,\n \"acc_stderr\": 0.007025085047248852\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 1.99244966442953e-05,\n \"f1_stderr\": 5.6438034448796525e-06\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5090765588003157,\n \"acc_stderr\": 0.014050170094497704\n }\n}\n```", "repo_url": "https://huggingface.co/mncai/SGPT-1.3B-insurance-epoch10", "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_09_18T00_09_04.877490", "path": ["**/details_harness|drop|3_2023-09-18T00-09-04.877490.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-18T00-09-04.877490.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_18T00_09_04.877490", "path": ["**/details_harness|gsm8k|5_2023-09-18T00-09-04.877490.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-18T00-09-04.877490.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_18T00_09_04.877490", "path": ["**/details_harness|winogrande|5_2023-09-18T00-09-04.877490.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-18T00-09-04.877490.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_18T00_09_04.877490", "path": ["results_2023-09-18T00-09-04.877490.parquet"]}, {"split": "latest", "path": ["results_2023-09-18T00-09-04.877490.parquet"]}]}]}
|
2023-09-17T23:09:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of mncai/SGPT-1.3B-insurance-epoch10
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model mncai/SGPT-1.3B-insurance-epoch10 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 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 2023-09-18T00:09:04.877490(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 mncai/SGPT-1.3B-insurance-epoch10",
"## 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 mncai/SGPT-1.3B-insurance-epoch10 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 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 2023-09-18T00:09:04.877490(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 mncai/SGPT-1.3B-insurance-epoch10",
"## 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 mncai/SGPT-1.3B-insurance-epoch10 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 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 2023-09-18T00:09:04.877490(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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"### 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 mncai/SGPT-1.3B-insurance-epoch10## 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 mncai/SGPT-1.3B-insurance-epoch10 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 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 2023-09-18T00:09:04.877490(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"
] |
07c79a3f2f91346f4c1ae12183dfea62aea73f76
|
# Dataset Card for Evaluation run of TheBloke/gpt4-alpaca-lora-30b-HF
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF
- **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 [TheBloke/gpt4-alpaca-lora-30b-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF) 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 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_TheBloke__gpt4-alpaca-lora-30b-HF",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T00:20:21.073173](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF/blob/main/results_2023-09-18T00-20-21.073173.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": 0.0016778523489932886,
"em_stderr": 0.00041913301788269584,
"f1": 0.06442533557047006,
"f1_stderr": 0.0013970563636897643,
"acc": 0.47865750583572136,
"acc_stderr": 0.0105907760769931
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.00041913301788269584,
"f1": 0.06442533557047006,
"f1_stderr": 0.0013970563636897643
},
"harness|gsm8k|5": {
"acc": 0.155420773313116,
"acc_stderr": 0.009979689409499152
},
"harness|winogrande|5": {
"acc": 0.8018942383583267,
"acc_stderr": 0.01120186274448705
}
}
```
### 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_TheBloke__gpt4-alpaca-lora-30b-HF
|
[
"region:us"
] |
2023-09-17T23:20:24+00:00
|
{"pretty_name": "Evaluation run of TheBloke/gpt4-alpaca-lora-30b-HF", "dataset_summary": "Dataset automatically created during the evaluation run of model [TheBloke/gpt4-alpaca-lora-30b-HF](https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF) 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 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_TheBloke__gpt4-alpaca-lora-30b-HF\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-18T00:20:21.073173](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__gpt4-alpaca-lora-30b-HF/blob/main/results_2023-09-18T00-20-21.073173.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\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788269584,\n \"f1\": 0.06442533557047006,\n \"f1_stderr\": 0.0013970563636897643,\n \"acc\": 0.47865750583572136,\n \"acc_stderr\": 0.0105907760769931\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.00041913301788269584,\n \"f1\": 0.06442533557047006,\n \"f1_stderr\": 0.0013970563636897643\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.155420773313116,\n \"acc_stderr\": 0.009979689409499152\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8018942383583267,\n \"acc_stderr\": 0.01120186274448705\n }\n}\n```", "repo_url": "https://huggingface.co/TheBloke/gpt4-alpaca-lora-30b-HF", "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_09_18T00_20_21.073173", "path": ["**/details_harness|drop|3_2023-09-18T00-20-21.073173.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-18T00-20-21.073173.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_18T00_20_21.073173", "path": ["**/details_harness|gsm8k|5_2023-09-18T00-20-21.073173.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-18T00-20-21.073173.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_18T00_20_21.073173", "path": ["**/details_harness|winogrande|5_2023-09-18T00-20-21.073173.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-18T00-20-21.073173.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_18T00_20_21.073173", "path": ["results_2023-09-18T00-20-21.073173.parquet"]}, {"split": "latest", "path": ["results_2023-09-18T00-20-21.073173.parquet"]}]}]}
|
2023-09-17T23:20:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TheBloke/gpt4-alpaca-lora-30b-HF
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TheBloke/gpt4-alpaca-lora-30b-HF 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 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 2023-09-18T00:20:21.073173(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 TheBloke/gpt4-alpaca-lora-30b-HF",
"## 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 TheBloke/gpt4-alpaca-lora-30b-HF 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 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 2023-09-18T00:20:21.073173(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 TheBloke/gpt4-alpaca-lora-30b-HF",
"## 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 TheBloke/gpt4-alpaca-lora-30b-HF 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 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 2023-09-18T00:20:21.073173(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 TheBloke/gpt4-alpaca-lora-30b-HF## 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 TheBloke/gpt4-alpaca-lora-30b-HF 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 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 2023-09-18T00:20:21.073173(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"
] |
20b2fd7c7f4f65e4a8f3d5aa7c3e75e00d6acc7a
|
# r/ChatGPT General Dump
From [r/ChatGPT Discord #general channel](https://discord.gg/aRpD4pCw33).
|
v2ray/r-chatgpt-general-dump
|
[
"task_categories:conversational",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"not-for-all-audiences",
"region:us"
] |
2023-09-17T23:30:33+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["conversational"], "tags": ["not-for-all-audiences"]}
|
2024-02-04T12:01:07+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-conversational #size_categories-100K<n<1M #language-English #license-mit #not-for-all-audiences #region-us
|
# r/ChatGPT General Dump
From r/ChatGPT Discord #general channel.
|
[
"# r/ChatGPT General Dump\nFrom r/ChatGPT Discord #general channel."
] |
[
"TAGS\n#task_categories-conversational #size_categories-100K<n<1M #language-English #license-mit #not-for-all-audiences #region-us \n",
"# r/ChatGPT General Dump\nFrom r/ChatGPT Discord #general channel."
] |
[
46,
21
] |
[
"passage: TAGS\n#task_categories-conversational #size_categories-100K<n<1M #language-English #license-mit #not-for-all-audiences #region-us \n# r/ChatGPT General Dump\nFrom r/ChatGPT Discord #general channel."
] |
7a11c26cc0bd37af0f8677c37d878ac7b6007e8d
|
# Dataset Card for "cv-as-nlp-vqa-example-flan-xxl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/cv-as-nlp-vqa-example-flan-xxl
|
[
"region:us"
] |
2023-09-17T23:47:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 585371.0, "num_examples": 10}], "download_size": 587773, "dataset_size": 585371.0}}
|
2023-09-18T00:35:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cv-as-nlp-vqa-example-flan-xxl"
More Information needed
|
[
"# Dataset Card for \"cv-as-nlp-vqa-example-flan-xxl\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cv-as-nlp-vqa-example-flan-xxl\"\n\nMore Information needed"
] |
[
6,
29
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cv-as-nlp-vqa-example-flan-xxl\"\n\nMore Information needed"
] |
b62776161ba48e5dd279ec561b9443463875440c
|
# Dataset Card for "communities_content"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jkruk/communities_content
|
[
"region:us"
] |
2023-09-17T23:56:25+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "author", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "subreddit_id", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1747840548, "num_examples": 853048}], "download_size": 1054847711, "dataset_size": 1747840548}}
|
2023-09-17T23:57:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "communities_content"
More Information needed
|
[
"# Dataset Card for \"communities_content\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"communities_content\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"communities_content\"\n\nMore Information needed"
] |
4d833c4e481d17412f82ee8fe553c94de5b9260c
|
DO NOT USE THIS PROJECT!!! This is only just in case the original is taken down, there will be a backup. The original is listed here: https://huggingface.co/lj1995/VoiceConversionWebUI. ONLY USE THIS IF IT'S DELETED!!!
|
PhoenixStormJr/EVC-just-in-case
|
[
"license:mit",
"region:us"
] |
2023-09-18T00:18:46+00:00
|
{"license": "mit"}
|
2023-09-18T00:27:46+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
DO NOT USE THIS PROJECT!!! This is only just in case the original is taken down, there will be a backup. The original is listed here: URL ONLY USE THIS IF IT'S DELETED!!!
|
[] |
[
"TAGS\n#license-mit #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#license-mit #region-us \n"
] |
f99d0f76fa8a571ddeaa04580c7a47793261ce85
|
# Dataset Card for Evaluation run of Andron00e/YetAnother_Open-Llama-3B-LoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA
- **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 [Andron00e/YetAnother_Open-Llama-3B-LoRA](https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA) 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 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_Andron00e__YetAnother_Open-Llama-3B-LoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T01:32:17.416050](https://huggingface.co/datasets/open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA/blob/main/results_2023-09-18T01-32-17.416050.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": 0.0,
"em_stderr": 0.0,
"f1": 0.0004886744966442953,
"f1_stderr": 8.997703088731367e-05,
"acc": 0.2569060773480663,
"acc_stderr": 0.007023561458220211
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0004886744966442953,
"f1_stderr": 8.997703088731367e-05
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5138121546961326,
"acc_stderr": 0.014047122916440422
}
}
```
### 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_Andron00e__YetAnother_Open-Llama-3B-LoRA
|
[
"region:us"
] |
2023-09-18T00:32:21+00:00
|
{"pretty_name": "Evaluation run of Andron00e/YetAnother_Open-Llama-3B-LoRA", "dataset_summary": "Dataset automatically created during the evaluation run of model [Andron00e/YetAnother_Open-Llama-3B-LoRA](https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA) 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 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_Andron00e__YetAnother_Open-Llama-3B-LoRA\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-18T01:32:17.416050](https://huggingface.co/datasets/open-llm-leaderboard/details_Andron00e__YetAnother_Open-Llama-3B-LoRA/blob/main/results_2023-09-18T01-32-17.416050.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\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0004886744966442953,\n \"f1_stderr\": 8.997703088731367e-05,\n \"acc\": 0.2569060773480663,\n \"acc_stderr\": 0.007023561458220211\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\": 0.0,\n \"f1\": 0.0004886744966442953,\n \"f1_stderr\": 8.997703088731367e-05\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5138121546961326,\n \"acc_stderr\": 0.014047122916440422\n }\n}\n```", "repo_url": "https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA", "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_09_18T01_32_17.416050", "path": ["**/details_harness|drop|3_2023-09-18T01-32-17.416050.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-18T01-32-17.416050.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_18T01_32_17.416050", "path": ["**/details_harness|gsm8k|5_2023-09-18T01-32-17.416050.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-18T01-32-17.416050.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_18T01_32_17.416050", "path": ["**/details_harness|winogrande|5_2023-09-18T01-32-17.416050.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-18T01-32-17.416050.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_18T01_32_17.416050", "path": ["results_2023-09-18T01-32-17.416050.parquet"]}, {"split": "latest", "path": ["results_2023-09-18T01-32-17.416050.parquet"]}]}]}
|
2023-09-18T00:32:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of Andron00e/YetAnother_Open-Llama-3B-LoRA
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Andron00e/YetAnother_Open-Llama-3B-LoRA 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 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 2023-09-18T01:32:17.416050(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 Andron00e/YetAnother_Open-Llama-3B-LoRA",
"## 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 Andron00e/YetAnother_Open-Llama-3B-LoRA 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 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 2023-09-18T01:32:17.416050(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 Andron00e/YetAnother_Open-Llama-3B-LoRA",
"## 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 Andron00e/YetAnother_Open-Llama-3B-LoRA 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 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 2023-09-18T01:32:17.416050(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,
28,
31,
176,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Andron00e/YetAnother_Open-Llama-3B-LoRA## 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 Andron00e/YetAnother_Open-Llama-3B-LoRA 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 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 2023-09-18T01:32:17.416050(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"
] |
e95df2e69572002af7e7d27b327121d84ab61c18
|
# Dataset Card for "covid-llama2-500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
DaisyStar004/covid-llama2-500
|
[
"region:us"
] |
2023-09-18T00:55:35+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 317407, "num_examples": 500}], "download_size": 181582, "dataset_size": 317407}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-18T00:55:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "covid-llama2-500"
More Information needed
|
[
"# Dataset Card for \"covid-llama2-500\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"covid-llama2-500\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"covid-llama2-500\"\n\nMore Information needed"
] |
b43f1eea18fe40621cdd7a9e6cba50e28458b00d
|
# Dataset Card for "articles_for_qa_wikipedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
legacy107/articles_for_qa_wikipedia
|
[
"region:us"
] |
2023-09-18T01:43:39+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "article", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 307503219, "num_examples": 8886}], "download_size": 174908641, "dataset_size": 307503219}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-18T01:43:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "articles_for_qa_wikipedia"
More Information needed
|
[
"# Dataset Card for \"articles_for_qa_wikipedia\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"articles_for_qa_wikipedia\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"articles_for_qa_wikipedia\"\n\nMore Information needed"
] |
72db7504469786f94d9bf7d54cdbc1ff3148cf5f
|
## Real Language Model
---
license: other
---
|
marketspace/real
|
[
"task_categories:text-generation",
"task_categories:translation",
"size_categories:1K<n<10K",
"license:openrail",
"code",
"region:us"
] |
2023-09-18T01:50:35+00:00
|
{"license": "openrail", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation", "translation"], "tags": ["code"]}
|
2024-01-30T01:29:35+00:00
|
[] |
[] |
TAGS
#task_categories-text-generation #task_categories-translation #size_categories-1K<n<10K #license-openrail #code #region-us
|
## Real Language Model
---
license: other
---
|
[
"## Real Language Model\n\n---\nlicense: other\n---"
] |
[
"TAGS\n#task_categories-text-generation #task_categories-translation #size_categories-1K<n<10K #license-openrail #code #region-us \n",
"## Real Language Model\n\n---\nlicense: other\n---"
] |
[
46,
9
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-translation #size_categories-1K<n<10K #license-openrail #code #region-us \n## Real Language Model\n\n---\nlicense: other\n---"
] |
c3a8b852af6de1468c7d62e2df2e271d9b0a664d
|
# Dataset Card for "cv-as-nlp-vision-example-flan-xxl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/cv-as-nlp-vision-example-flan-xxl
|
[
"region:us"
] |
2023-09-18T02:00:14+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 119377.0, "num_examples": 10}], "download_size": 119894, "dataset_size": 119377.0}}
|
2023-09-18T02:00:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cv-as-nlp-vision-example-flan-xxl"
More Information needed
|
[
"# Dataset Card for \"cv-as-nlp-vision-example-flan-xxl\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cv-as-nlp-vision-example-flan-xxl\"\n\nMore Information needed"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cv-as-nlp-vision-example-flan-xxl\"\n\nMore Information needed"
] |
ada13831e478fa4ebcc6989194d76c4035c2b33c
|
# Dataset Card for Evaluation run of clibrain/Llama-2-13b-ft-instruct-es
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/clibrain/Llama-2-13b-ft-instruct-es
- **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 [clibrain/Llama-2-13b-ft-instruct-es](https://huggingface.co/clibrain/Llama-2-13b-ft-instruct-es) 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 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_clibrain__Llama-2-13b-ft-instruct-es",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T03:06:46.998156](https://huggingface.co/datasets/open-llm-leaderboard/details_clibrain__Llama-2-13b-ft-instruct-es/blob/main/results_2023-09-18T03-06-46.998156.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": 0.0024119127516778523,
"em_stderr": 0.0005023380498893348,
"f1": 0.0655463506711411,
"f1_stderr": 0.0014039891922809947,
"acc": 0.42168315309067345,
"acc_stderr": 0.009875785691028784
},
"harness|drop|3": {
"em": 0.0024119127516778523,
"em_stderr": 0.0005023380498893348,
"f1": 0.0655463506711411,
"f1_stderr": 0.0014039891922809947
},
"harness|gsm8k|5": {
"acc": 0.08567096285064443,
"acc_stderr": 0.007709218855882782
},
"harness|winogrande|5": {
"acc": 0.7576953433307024,
"acc_stderr": 0.012042352526174787
}
}
```
### 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_clibrain__Llama-2-13b-ft-instruct-es
|
[
"region:us"
] |
2023-09-18T02:06:50+00:00
|
{"pretty_name": "Evaluation run of clibrain/Llama-2-13b-ft-instruct-es", "dataset_summary": "Dataset automatically created during the evaluation run of model [clibrain/Llama-2-13b-ft-instruct-es](https://huggingface.co/clibrain/Llama-2-13b-ft-instruct-es) 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 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_clibrain__Llama-2-13b-ft-instruct-es\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-09-18T03:06:46.998156](https://huggingface.co/datasets/open-llm-leaderboard/details_clibrain__Llama-2-13b-ft-instruct-es/blob/main/results_2023-09-18T03-06-46.998156.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\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893348,\n \"f1\": 0.0655463506711411,\n \"f1_stderr\": 0.0014039891922809947,\n \"acc\": 0.42168315309067345,\n \"acc_stderr\": 0.009875785691028784\n },\n \"harness|drop|3\": {\n \"em\": 0.0024119127516778523,\n \"em_stderr\": 0.0005023380498893348,\n \"f1\": 0.0655463506711411,\n \"f1_stderr\": 0.0014039891922809947\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.08567096285064443,\n \"acc_stderr\": 0.007709218855882782\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7576953433307024,\n \"acc_stderr\": 0.012042352526174787\n }\n}\n```", "repo_url": "https://huggingface.co/clibrain/Llama-2-13b-ft-instruct-es", "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_09_18T03_06_46.998156", "path": ["**/details_harness|drop|3_2023-09-18T03-06-46.998156.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-09-18T03-06-46.998156.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_09_18T03_06_46.998156", "path": ["**/details_harness|gsm8k|5_2023-09-18T03-06-46.998156.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-09-18T03-06-46.998156.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_09_18T03_06_46.998156", "path": ["**/details_harness|winogrande|5_2023-09-18T03-06-46.998156.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-09-18T03-06-46.998156.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_09_18T03_06_46.998156", "path": ["results_2023-09-18T03-06-46.998156.parquet"]}, {"split": "latest", "path": ["results_2023-09-18T03-06-46.998156.parquet"]}]}]}
|
2023-09-18T02:06:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of clibrain/Llama-2-13b-ft-instruct-es
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model clibrain/Llama-2-13b-ft-instruct-es 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 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 2023-09-18T03:06:46.998156(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 clibrain/Llama-2-13b-ft-instruct-es",
"## 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 clibrain/Llama-2-13b-ft-instruct-es 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 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 2023-09-18T03:06:46.998156(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 clibrain/Llama-2-13b-ft-instruct-es",
"## 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 clibrain/Llama-2-13b-ft-instruct-es 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 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 2023-09-18T03:06:46.998156(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
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"### Curation Rationale",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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[
6,
25,
31,
173,
66,
10,
4,
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6,
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10,
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6,
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5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of clibrain/Llama-2-13b-ft-instruct-es## 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 clibrain/Llama-2-13b-ft-instruct-es 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 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 2023-09-18T03:06:46.998156(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"
] |
8e1e0ab4b9e3b0ecf0223ccdd39048631e1a3dbc
|
# Dataset Card for "se_cooking_preference_sft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andrewsiah/se_cooking_preference_sft
|
[
"region:us"
] |
2023-09-18T02:10:28+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11095448, "num_examples": 7262}], "download_size": 6879361, "dataset_size": 11095448}}
|
2023-09-18T02:30:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "se_cooking_preference_sft"
More Information needed
|
[
"# Dataset Card for \"se_cooking_preference_sft\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"se_cooking_preference_sft\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"se_cooking_preference_sft\"\n\nMore Information needed"
] |
361c4a7873f75e0619ff837a8a97de77daf23857
|
# Selenium-bot
use pip install -r requirements.txt
|
dangvinh77/Dataminer
|
[
"region:us"
] |
2023-09-18T02:33:23+00:00
|
{}
|
2023-09-18T02:48:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Selenium-bot
use pip install -r URL
|
[
"# Selenium-bot\n\nuse pip install -r URL"
] |
[
"TAGS\n#region-us \n",
"# Selenium-bot\n\nuse pip install -r URL"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Selenium-bot\n\nuse pip install -r URL"
] |
a771af9798ef51d4562db8088bd8b18621aa824b
|
# Dataset Card for "asr-slu_whisper"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Cherishh/asr-slu_whisper
|
[
"region:us"
] |
2023-09-18T02:34:54+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 5765085224, "num_examples": 6002}, {"name": "val", "num_bytes": 640671888, "num_examples": 667}, {"name": "test", "num_bytes": 711747832, "num_examples": 741}], "download_size": 1134615218, "dataset_size": 7117504944}}
|
2023-09-18T06:49:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "asr-slu_whisper"
More Information needed
|
[
"# Dataset Card for \"asr-slu_whisper\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"asr-slu_whisper\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"asr-slu_whisper\"\n\nMore Information needed"
] |
d13ab1d4469291d26910f0e1c791b650edcf290b
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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]
|
re2panda/grade_school_math_modified
|
[
"task_categories:text-generation",
"region:us"
] |
2023-09-18T03:07:49+00:00
|
{"task_categories": ["text-generation"]}
|
2023-09-18T04:09:25+00:00
|
[] |
[] |
TAGS
#task_categories-text-generation #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### 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 Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
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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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"### Supported Tasks and Leaderboards",
"### Languages",
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"### Data Fields",
"### Data Splits",
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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"
] |
[
17,
8,
24,
32,
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5,
5,
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4,
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6,
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5
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[
"passage: TAGS\n#task_categories-text-generation #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.### 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"
] |
e595391ad69b62e97d12434b1e21c5c732714068
|
# Bangumi Image Base of Goblin Slayer
This is the image base of bangumi Goblin Slayer, we detected 45 characters, 2771 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 | 103 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 686 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 41 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 29 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 22 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 11 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 38 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 33 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 89 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 38 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 47 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 41 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 21 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 74 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 13 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 30 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 398 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 46 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 131 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 56 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 11 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 36 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 17 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 17 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 22 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 75 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 27 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 18 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 21 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 20 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 6 | [Download](31/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 32 | 135 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 58 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 23 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 12 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 9 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 8 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 6 | [Download](39/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 40 | 9 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 7 | [Download](41/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 42 | 11 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 58 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 199 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/goblinslayer
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-09-18T03:11:50+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T07:35:36+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Goblin Slayer
===================================
This is the image base of bangumi Goblin Slayer, we detected 45 characters, 2771 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:
|
[] |
[
"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"
] |
eeb4f8795cb299bb1b108256b779d4da111c860b
|
# Dataset Card for "asr-slu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Cherishh/asr-slu
|
[
"region:us"
] |
2023-09-18T03:12:10+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "speech", "sequence": "float64"}, {"name": "sampling_rate", "dtype": "int64"}, {"name": "target_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3131199570, "num_examples": 6002}, {"name": "val", "num_bytes": 351773643, "num_examples": 667}, {"name": "test", "num_bytes": 380367632, "num_examples": 741}], "download_size": 916274597, "dataset_size": 3863340845}}
|
2023-09-18T03:14:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "asr-slu"
More Information needed
|
[
"# Dataset Card for \"asr-slu\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"asr-slu\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"asr-slu\"\n\nMore Information needed"
] |
f9f414d364b414723eb2db5946805d2fe17b9946
|
# Dataset Card for "anthropic_hh_sft"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
andrewsiah/anthropic_hh_sft
|
[
"region:us"
] |
2023-09-18T03:14:20+00:00
|
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13042104, "num_examples": 20159}], "download_size": 7382066, "dataset_size": 13042104}}
|
2023-10-06T03:23:06+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "anthropic_hh_sft"
More Information needed
|
[
"# Dataset Card for \"anthropic_hh_sft\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"anthropic_hh_sft\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"anthropic_hh_sft\"\n\nMore Information needed"
] |
cb8e159845f55b7948130acce82806862b5ceddb
|
# Dataset Card for **Data Provenance Initiative - Commercial-Licenses**
## Dataset Description
- **Homepage:** https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection
- **Repository:** https://github.com/Data-Provenance-Initiative/Data-Provenance-Collection
- **Paper:** https://arxiv.org/abs/2310.16787
- **Point of Contact:** [email protected]
- **NOTE:** Licenses for these datasets are "self-reported" and collected by best-effort volunteers on a per dataset basis. Please find more details in the paper linked above.
### Legal Disclaimer / Notice
Collected License Information is **NOT** Legal Advice.
It is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative.
The information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only.
Readers and users should seek their own legal advice from counsel in their relevant jurisdiction.
### Dataset Summary
A wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models.
### What does **Commercial** mean here?
- `Commercial` includes datasets that are compatible with commercial usage, meaning commercial usage of this dataset is permitted as per its license.
### Constituent Data Collections
- Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.
| # | Collection Name | Description | Source |
| --------------- | --------------- | --------------- | --------------- |
| 1 | Anthropic HH-RLHF | Human preference data about helpfulness and harmlessness & Human-generated and annotated red teaming dialogues. | https://huggingface.co/datasets/Anthropic/hh-rlhf |
| 2 | CommitPackFT | CommitPackFT is a 2GB filtered version of CommitPack to contain only high-quality commit messages that resemble natural language instructions. | https://huggingface.co/datasets/bigcode/commitpackft |
| 3 | Dolly 15k | Databricks Dolly 15k is a dataset containing 15,000 high-quality human-generated prompt / response pairs specifically designed for instruction tuning large language models. | https://huggingface.co/datasets/databricks/databricks-dolly-15k |
| 4 | Flan Collection (Chain-of-Thought) | Chain-of-Thought sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/cot_submix_original |
| 5 | Flan Collection (Dialog) | Chain-of-Thought sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/dialog_submix_original |
| 6 | Flan Collection (Flan 2021) | Flan 2021 sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/flan2021_submix_original |
| 7 | Flan Collection (P3) | P3 sub-mixture in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/t0_submix_original |
| 8 | Flan Collection (Super-NaturalInstructions) | Super-Natural Instructions in Flan collection dataset. | https://huggingface.co/datasets/conceptofmind/niv2_submix_original |
| 9 | Joke Explanation | Corpus for testing whether your LLM can explain the joke well. | https://huggingface.co/datasets/theblackcat102/joke_explaination |
| 10 | OIG | Open Instruction Generalist is a large instruction dataset of medium quality along with a smaller high quality instruction dataset (OIG-small-chip2). | https://huggingface.co/datasets/laion/OIG |
| 11 | Open Assistant | OpenAssistant Conversations (OASST1) is a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees. | https://huggingface.co/datasets/OpenAssistant/oasst1 |
| 12 | Open Assistant OctoPack | Filtered version of OpenAssistant Conversations (OASST1) to focus only on high-quality conversation trees as used in OctoPack paper. | https://huggingface.co/datasets/bigcode/oasst-octopack |
| 13 | Tasksource Symbol-Tuning | Tasksource datasets converted for symbol-tuning. | https://github.com/sileod/tasksource |
| 14 | Tasksource Instruct | Tasksource datasets as instructions for instruction-tuning. | https://github.com/sileod/tasksource |
| 15 | xp3x | xP3x is a collection of prompts & datasets across 277 languages & 16 NLP tasks. It contains all of xP3 + much more. | https://huggingface.co/datasets/Muennighoff/xP3x |
| 16 | StarCoder Self-Instruct | Dataset generated by prompting starcoder to generate new instructions based on some human-written seed instructions. | https://huggingface.co/datasets/codeparrot/self-instruct-starcoder |
### Data Instances
[More Information Needed]
### Data Fields
The following snippet shows the fields in a row in each data collection in this dataset:
```
[
{"from": "user", "text": input_text.strip(), "parent": dset},
{"from": "assistant", "text": target_text.strip(), "parent": 0},
...
]
```
with fields:
- from: indicates the originator of the text in this conversation. This can be either "user" or "assistant", where "assistant" indicates the model and text will be model's response to user's text.
- text: indicates text that originator wants to communicate to receiver.
- parent: field indicating the parent for tracing the conversation hierarchy.
Here each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager `parent` field in json object to follow the tree structure of interactions.
### Downloading Dataset
You can load the entire dataset by using the following code:
```python
import os
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("DataProvenanceInitiative/Commercially-Verified-Licenses")
```
You can load a specific dataset subset such as Dolly 15k using the following code:
```python
import os
from datasets import load_dataset
subset = load_dataset(
"DataProvenanceInitiative/Commercially-Verified-Licenses",
split="train",
num_proc = os.cpu_count(),
revision="main",
data_files="data/dolly_15k/*.jsonl"
)
```
### Data Splits
[More Information Needed]
[TODO: Add each dataset and add # of samples in train/dev]
## Dataset Creation
[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]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{longpre2023data,
title={The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing \& Attribution in AI},
author={Longpre, Shayne and Mahari, Robert and Chen, Anthony and Obeng-Marnu, Naana and Sileo, Damien and Brannon, William and Muennighoff, Niklas and Khazam, Nathan and Kabbara, Jad and Perisetla, Kartik and others},
journal={arXiv preprint arXiv:2310.16787},
year={2023}
}
```
### Contributions
Thanks to [[email protected]](mailto:[email protected]) for adding this dataset.
|
DataProvenanceInitiative/Commercially-Verified-Licenses
|
[
"arxiv:2310.16787",
"region:us"
] |
2023-09-18T03:31:20+00:00
|
{}
|
2023-11-03T19:23:40+00:00
|
[
"2310.16787"
] |
[] |
TAGS
#arxiv-2310.16787 #region-us
|
Dataset Card for Data Provenance Initiative - Commercial-Licenses
=================================================================
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Point of Contact: URL@URL
* NOTE: Licenses for these datasets are "self-reported" and collected by best-effort volunteers on a per dataset basis. Please find more details in the paper linked above.
### Legal Disclaimer / Notice
Collected License Information is NOT Legal Advice.
It is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative.
The information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only.
Readers and users should seek their own legal advice from counsel in their relevant jurisdiction.
### Dataset Summary
A wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models.
### What does Commercial mean here?
* 'Commercial' includes datasets that are compatible with commercial usage, meaning commercial usage of this dataset is permitted as per its license.
### Constituent Data Collections
* Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.
### Data Instances
### Data Fields
The following snippet shows the fields in a row in each data collection in this dataset:
with fields:
* from: indicates the originator of the text in this conversation. This can be either "user" or "assistant", where "assistant" indicates the model and text will be model's response to user's text.
* text: indicates text that originator wants to communicate to receiver.
* parent: field indicating the parent for tracing the conversation hierarchy.
Here each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager 'parent' field in json object to follow the tree structure of interactions.
### Downloading Dataset
You can load the entire dataset by using the following code:
You can load a specific dataset subset such as Dolly 15k using the following code:
### Data Splits
[TODO: Add each dataset and add # of samples in train/dev]
Dataset Creation
----------------
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
Thanks to URL@URL for adding this dataset.
|
[
"### Legal Disclaimer / Notice\n\n\nCollected License Information is NOT Legal Advice.\nIt is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative.\nThe information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only.\nReaders and users should seek their own legal advice from counsel in their relevant jurisdiction.",
"### Dataset Summary\n\n\nA wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models.",
"### What does Commercial mean here?\n\n\n* 'Commercial' includes datasets that are compatible with commercial usage, meaning commercial usage of this dataset is permitted as per its license.",
"### Constituent Data Collections\n\n\n* Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.",
"### Data Instances",
"### Data Fields\n\n\nThe following snippet shows the fields in a row in each data collection in this dataset:\n\n\nwith fields:\n\n\n* from: indicates the originator of the text in this conversation. This can be either \"user\" or \"assistant\", where \"assistant\" indicates the model and text will be model's response to user's text.\n* text: indicates text that originator wants to communicate to receiver.\n* parent: field indicating the parent for tracing the conversation hierarchy.\nHere each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager 'parent' field in json object to follow the tree structure of interactions.",
"### Downloading Dataset\n\n\nYou can load the entire dataset by using the following code:\n\n\nYou can load a specific dataset subset such as Dolly 15k using the following code:",
"### Data Splits\n\n\n[TODO: Add each dataset and add # of samples in train/dev]\n\n\nDataset Creation\n----------------",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to URL@URL for adding this dataset."
] |
[
"TAGS\n#arxiv-2310.16787 #region-us \n",
"### Legal Disclaimer / Notice\n\n\nCollected License Information is NOT Legal Advice.\nIt is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative.\nThe information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only.\nReaders and users should seek their own legal advice from counsel in their relevant jurisdiction.",
"### Dataset Summary\n\n\nA wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models.",
"### What does Commercial mean here?\n\n\n* 'Commercial' includes datasets that are compatible with commercial usage, meaning commercial usage of this dataset is permitted as per its license.",
"### Constituent Data Collections\n\n\n* Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.",
"### Data Instances",
"### Data Fields\n\n\nThe following snippet shows the fields in a row in each data collection in this dataset:\n\n\nwith fields:\n\n\n* from: indicates the originator of the text in this conversation. This can be either \"user\" or \"assistant\", where \"assistant\" indicates the model and text will be model's response to user's text.\n* text: indicates text that originator wants to communicate to receiver.\n* parent: field indicating the parent for tracing the conversation hierarchy.\nHere each row contains one or more json objects indicating user-assistant interaction dialogue with text messages exchanged between them. You can leverager 'parent' field in json object to follow the tree structure of interactions.",
"### Downloading Dataset\n\n\nYou can load the entire dataset by using the following code:\n\n\nYou can load a specific dataset subset such as Dolly 15k using the following code:",
"### Data Splits\n\n\n[TODO: Add each dataset and add # of samples in train/dev]\n\n\nDataset Creation\n----------------",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to URL@URL for adding this dataset."
] |
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15,
137,
249,
42,
34,
6,
166,
39,
30,
10,
5,
5,
9,
15,
6,
6,
16
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[
"passage: TAGS\n#arxiv-2310.16787 #region-us \n### Legal Disclaimer / Notice\n\n\nCollected License Information is NOT Legal Advice.\nIt is important to note we collect self-reported licenses, from the papers and repositories that released these datasets, and categorize them according to our best efforts, as a volunteer research and transparency initiative.\nThe information provided by any of our works and any outputs of the Data Provenance Initiative do not, and are not intended to, constitute legal advice; instead, all information, content, and materials are for general informational purposes only.\nReaders and users should seek their own legal advice from counsel in their relevant jurisdiction.### Dataset Summary\n\n\nA wave of recent language models have been powered by large collections of natural language datasets. The sudden race to train models on these disparate collections of incorrectly, ambiguously, or under-documented datasets has left practitioners unsure of the legal and qualitative characteristics of the models they train. To remedy this crisis in data transparency and understanding, in a joint effort between experts in machine learning and the law, we’ve compiled the most detailed and reliable metadata available for data licenses, sources, and provenance, as well as fine-grained characteristics like language, text domains, topics, usage, collection time, and task compositions. Beginning with nearly 40 popular instruction (or “alignment”) tuning collections, we release a suite of open source tools for downloading, filtering, and examining this training data. Our analysis sheds light on the fractured state of data transparency, particularly with data licensing, and we hope our tools will empower more informed and responsible data-centric development of future language models.### What does Commercial mean here?\n\n\n* 'Commercial' includes datasets that are compatible with commercial usage, meaning commercial usage of this dataset is permitted as per its license.### Constituent Data Collections\n\n\n* Following table shows each constituent data collection this Dataset along with original source from where each data collection is derived from.### Data Instances"
] |
96aeb219eb4f5f655caea21c0b19b34add82280c
|
# Dataset Card for "qa_wikipedia_no_article"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
legacy107/qa_wikipedia_no_article
|
[
"region:us"
] |
2023-09-18T03:38:43+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer_start", "dtype": "int64"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 74337363, "num_examples": 138712}, {"name": "test", "num_bytes": 9222514, "num_examples": 17341}, {"name": "validation", "num_bytes": 9271740, "num_examples": 17291}], "download_size": 25137600, "dataset_size": 92831617}}
|
2023-09-18T03:38:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "qa_wikipedia_no_article"
More Information needed
|
[
"# Dataset Card for \"qa_wikipedia_no_article\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"qa_wikipedia_no_article\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"qa_wikipedia_no_article\"\n\nMore Information needed"
] |
d834fbeec74134466465ccf738ac11d864f92958
|
# Dataset Card for "8edb1fe9"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/8edb1fe9
|
[
"region:us"
] |
2023-09-18T03:48:25+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 258, "num_examples": 10}], "download_size": 1429, "dataset_size": 258}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-18T03:48:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "8edb1fe9"
More Information needed
|
[
"# Dataset Card for \"8edb1fe9\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"8edb1fe9\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"8edb1fe9\"\n\nMore Information needed"
] |
91b7899aeee8ea884a013879cdb85ce95b090ce5
|
***This branch is unmaintained!***
https://github.com/toriato/stable-diffusion-webui-wd14-tagger/issues/108
---
https://github.com/picobyte/stable-diffusion-webui-wd14-tagger
# Tagger for [Automatic1111's WebUI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
Interrogate booru style tags for single or multiple image files using various models, such as DeepDanbooru.
[한국어를 사용하시나요? 여기에 한국어 설명서가 있습니다!](README.ko.md)
## Disclaimer
I didn't make any models, and most of the code was heavily borrowed from the [DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) and MrSmillingWolf's tagger.
## Installation
1. *Extensions* -> *Install from URL* -> Enter URL of this repository -> Press *Install* button
- or clone this repository under `extensions/`
```sh
$ git clone https://github.com/toriato/stable-diffusion-webui-wd14-tagger.git extensions/tagger
```
1. *(optional)* Add interrogate model
- #### [*Waifu Diffusion 1.4 Tagger by MrSmilingWolf*](docs/what-is-wd14-tagger.md)
Downloads automatically from the [HuggingFace repository](https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger) the first time you run it.
- #### *DeepDanbooru*
1. Various model files can be found below.
- [DeepDanbooru models](https://github.com/KichangKim/DeepDanbooru/releases)
- [e621 model by 🐾Zack🐾#1984](https://discord.gg/BDFpq9Yb7K)
*(link contains NSFW contents!)*
1. Move the project folder containing the model and config to `models/deepdanbooru`
1. The file structure should look like:
```
models/
└╴deepdanbooru/
├╴deepdanbooru-v3-20211112-sgd-e28/
│ ├╴project.json
│ └╴...
│
├╴deepdanbooru-v4-20200814-sgd-e30/
│ ├╴project.json
│ └╴...
│
├╴e621-v3-20221117-sgd-e32/
│ ├╴project.json
│ └╴...
│
...
```
1. Start or restart the WebUI.
- or you can press refresh button after *Interrogator* dropdown box.
- "You must close stable diffusion completely after installation and re-run it!"
## Model comparison
[Model comparison](docs/model-comparison.md)
## Screenshot
Artwork made by [hecattaart](https://vk.com/hecattaart?w=wall-89063929_3767)
## Copyright
Public domain, except borrowed parts (e.g. `dbimutils.py`)
|
eara4vu/zsxbtag
|
[
"region:us"
] |
2023-09-18T04:33:21+00:00
|
{}
|
2023-11-03T05:37:11+00:00
|
[] |
[] |
TAGS
#region-us
|
*This branch is unmaintained!*
URL
---
URL
# Tagger for Automatic1111's WebUI
Interrogate booru style tags for single or multiple image files using various models, such as DeepDanbooru.
한국어를 사용하시나요? 여기에 한국어 설명서가 있습니다!
## Disclaimer
I didn't make any models, and most of the code was heavily borrowed from the DeepDanbooru and MrSmillingWolf's tagger.
## Installation
1. *Extensions* -> *Install from URL* -> Enter URL of this repository -> Press *Install* button
- or clone this repository under 'extensions/'
1. *(optional)* Add interrogate model
- #### *Waifu Diffusion 1.4 Tagger by MrSmilingWolf*
Downloads automatically from the HuggingFace repository the first time you run it.
- #### *DeepDanbooru*
1. Various model files can be found below.
- DeepDanbooru models
- e621 model by Zack#1984
*(link contains NSFW contents!)*
1. Move the project folder containing the model and config to 'models/deepdanbooru'
1. The file structure should look like:
1. Start or restart the WebUI.
- or you can press refresh button after *Interrogator* dropdown box.
- "You must close stable diffusion completely after installation and re-run it!"
## Model comparison
Model comparison
## Screenshot
!Screenshot
Artwork made by hecattaart
## Copyright
Public domain, except borrowed parts (e.g. 'URL')
|
[
"# Tagger for Automatic1111's WebUI\nInterrogate booru style tags for single or multiple image files using various models, such as DeepDanbooru.\n\n한국어를 사용하시나요? 여기에 한국어 설명서가 있습니다!",
"## Disclaimer\nI didn't make any models, and most of the code was heavily borrowed from the DeepDanbooru and MrSmillingWolf's tagger.",
"## Installation\n1. *Extensions* -> *Install from URL* -> Enter URL of this repository -> Press *Install* button\n - or clone this repository under 'extensions/'\n \n\n1. *(optional)* Add interrogate model\n - #### *Waifu Diffusion 1.4 Tagger by MrSmilingWolf*\n Downloads automatically from the HuggingFace repository the first time you run it.\n\n - #### *DeepDanbooru*\n 1. Various model files can be found below.\n - DeepDanbooru models\n - e621 model by Zack#1984\n *(link contains NSFW contents!)*\n\n 1. Move the project folder containing the model and config to 'models/deepdanbooru'\n\n 1. The file structure should look like:\n \n\n1. Start or restart the WebUI.\n - or you can press refresh button after *Interrogator* dropdown box.\n - \"You must close stable diffusion completely after installation and re-run it!\"",
"## Model comparison\nModel comparison",
"## Screenshot\n!Screenshot\n\nArtwork made by hecattaart",
"## Copyright\n\nPublic domain, except borrowed parts (e.g. 'URL')"
] |
[
"TAGS\n#region-us \n",
"# Tagger for Automatic1111's WebUI\nInterrogate booru style tags for single or multiple image files using various models, such as DeepDanbooru.\n\n한국어를 사용하시나요? 여기에 한국어 설명서가 있습니다!",
"## Disclaimer\nI didn't make any models, and most of the code was heavily borrowed from the DeepDanbooru and MrSmillingWolf's tagger.",
"## Installation\n1. *Extensions* -> *Install from URL* -> Enter URL of this repository -> Press *Install* button\n - or clone this repository under 'extensions/'\n \n\n1. *(optional)* Add interrogate model\n - #### *Waifu Diffusion 1.4 Tagger by MrSmilingWolf*\n Downloads automatically from the HuggingFace repository the first time you run it.\n\n - #### *DeepDanbooru*\n 1. Various model files can be found below.\n - DeepDanbooru models\n - e621 model by Zack#1984\n *(link contains NSFW contents!)*\n\n 1. Move the project folder containing the model and config to 'models/deepdanbooru'\n\n 1. The file structure should look like:\n \n\n1. Start or restart the WebUI.\n - or you can press refresh button after *Interrogator* dropdown box.\n - \"You must close stable diffusion completely after installation and re-run it!\"",
"## Model comparison\nModel comparison",
"## Screenshot\n!Screenshot\n\nArtwork made by hecattaart",
"## Copyright\n\nPublic domain, except borrowed parts (e.g. 'URL')"
] |
[
6,
48,
39,
214,
5,
16,
19
] |
[
"passage: TAGS\n#region-us \n# Tagger for Automatic1111's WebUI\nInterrogate booru style tags for single or multiple image files using various models, such as DeepDanbooru.\n\n한국어를 사용하시나요? 여기에 한국어 설명서가 있습니다!## Disclaimer\nI didn't make any models, and most of the code was heavily borrowed from the DeepDanbooru and MrSmillingWolf's tagger.## Installation\n1. *Extensions* -> *Install from URL* -> Enter URL of this repository -> Press *Install* button\n - or clone this repository under 'extensions/'\n \n\n1. *(optional)* Add interrogate model\n - #### *Waifu Diffusion 1.4 Tagger by MrSmilingWolf*\n Downloads automatically from the HuggingFace repository the first time you run it.\n\n - #### *DeepDanbooru*\n 1. Various model files can be found below.\n - DeepDanbooru models\n - e621 model by Zack#1984\n *(link contains NSFW contents!)*\n\n 1. Move the project folder containing the model and config to 'models/deepdanbooru'\n\n 1. The file structure should look like:\n \n\n1. Start or restart the WebUI.\n - or you can press refresh button after *Interrogator* dropdown box.\n - \"You must close stable diffusion completely after installation and re-run it!\"## Model comparison\nModel comparison## Screenshot\n!Screenshot\n\nArtwork made by hecattaart## Copyright\n\nPublic domain, except borrowed parts (e.g. 'URL')"
] |
d2bcfd1c31496af64ec4a0b188629ac3dd0e1b42
|
# Dataset Card for "donut_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
arifzanko/donut_test
|
[
"region:us"
] |
2023-09-18T05:41:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 746757.0, "num_examples": 1}, {"name": "validation", "num_bytes": 746757.0, "num_examples": 1}, {"name": "test", "num_bytes": 948591.0, "num_examples": 1}], "download_size": 2477867, "dataset_size": 2442105.0}}
|
2023-09-18T08:05:22+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "donut_test"
More Information needed
|
[
"# Dataset Card for \"donut_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"donut_test\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"donut_test\"\n\nMore Information needed"
] |
bff0b71c664a397b28c76def56d22c0919031a6c
|
# Dataset of Miyamori Aoi
This is the dataset of Miyamori Aoi, containing 300 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 | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 646 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 646 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 646 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 646 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/miyamori_aoi_shirobako
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-18T05:50:06+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-18T05:52:19+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Miyamori Aoi
=======================
This is the dataset of Miyamori Aoi, containing 300 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"
] |
e5ea8c0f0b30b008efc424909207e391b7a69fb0
|
# Dataset of Yasuhara Ema
This is the dataset of Yasuhara Ema, containing 300 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 | 300 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 639 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 300 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 300 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 300 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 300 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 300 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 639 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 639 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 639 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/yasuhara_ema_shirobako
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-18T06:08:52+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-18T06:13:31+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Yasuhara Ema
=======================
This is the dataset of Yasuhara Ema, containing 300 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"
] |
17460742953be8171de92e38c7c54814f43dd6af
|
# Dataset Card for "data_aug_syllable"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
linhqyy/data_aug_syllable
|
[
"region:us"
] |
2023-09-18T06:19:13+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "entities", "list": [{"name": "type", "dtype": "string"}, {"name": "filler", "dtype": "string"}]}, {"name": "labels", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2380518, "num_examples": 11340}, {"name": "test", "num_bytes": 125890, "num_examples": 597}], "download_size": 579180, "dataset_size": 2506408}}
|
2023-09-18T06:19:17+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data_aug_syllable"
More Information needed
|
[
"# Dataset Card for \"data_aug_syllable\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data_aug_syllable\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data_aug_syllable\"\n\nMore Information needed"
] |
7060a4aed47c1372769b5c4cbab7c687db140eff
|
# Dataset Card for "bpd-twitter"
I scraped my twitter timeline some time in late 2022 / v early 2023
|
boopysaur/bpd-twitter
|
[
"region:us"
] |
2023-09-18T06:24:03+00:00
|
{"dataset_info": {"features": [{"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2007525.0, "num_examples": 30407}], "download_size": 1486546, "dataset_size": 2007525.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-18T06:39:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "bpd-twitter"
I scraped my twitter timeline some time in late 2022 / v early 2023
|
[
"# Dataset Card for \"bpd-twitter\"\n\nI scraped my twitter timeline some time in late 2022 / v early 2023"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"bpd-twitter\"\n\nI scraped my twitter timeline some time in late 2022 / v early 2023"
] |
[
6,
28
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"bpd-twitter\"\n\nI scraped my twitter timeline some time in late 2022 / v early 2023"
] |
bf40de2db6dffcd04c3c4158894f5a3baad3f939
|
# Dataset of Sakaki Shizuka
This is the dataset of Sakaki Shizuka, containing 237 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 | 237 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 522 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 237 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 237 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 237 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 237 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 237 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 522 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 522 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 522 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/sakaki_shizuka_shirobako
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-18T06:25:25+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-18T06:27:33+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Sakaki Shizuka
=========================
This is the dataset of Sakaki Shizuka, containing 237 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"
] |
864d7d0933485b15802b0000540f338c121d8c2f
|
# Dataset Card for "COVID-QA-for-sentence-transformer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
minh21/COVID-QA-for-sentence-transformer
|
[
"region:us"
] |
2023-09-18T06:25:49+00:00
|
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "answer_text", "dtype": "string"}, {"name": "answer_start", "dtype": "int64"}, {"name": "is_impossible", "dtype": "bool"}, {"name": "document_id", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1184569, "num_examples": 1615}, {"name": "test", "num_bytes": 144867, "num_examples": 202}, {"name": "validation", "num_bytes": 147532, "num_examples": 202}], "download_size": 808259, "dataset_size": 1476968}}
|
2023-09-18T06:25:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "COVID-QA-for-sentence-transformer"
More Information needed
|
[
"# Dataset Card for \"COVID-QA-for-sentence-transformer\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"COVID-QA-for-sentence-transformer\"\n\nMore Information needed"
] |
[
6,
22
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"COVID-QA-for-sentence-transformer\"\n\nMore Information needed"
] |
57d7159724e3664451190adff38eb6f4984d45a6
|
# Dataset Card for "96998511"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-muse256-muse512-wuerst-sdv15/96998511
|
[
"region:us"
] |
2023-09-18T06:28:19+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 165, "num_examples": 10}], "download_size": 1327, "dataset_size": 165}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-09-18T06:28:20+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "96998511"
More Information needed
|
[
"# Dataset Card for \"96998511\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"96998511\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"96998511\"\n\nMore Information needed"
] |
4f985b3963d7eb965c2314a01583597a52c9ccad
|
# Bangumi Image Base of Paripi Koumei
This is the image base of bangumi Paripi Koumei, we detected 33 characters, 2237 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 | 34 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 344 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 65 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 42 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 34 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 36 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 68 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 201 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 19 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 235 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 32 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 14 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 15 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 41 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 10 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 32 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 19 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 8 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 102 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 451 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 11 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 121 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 48 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 6 | [Download](24/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 25 | 8 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 7 | [Download](26/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 27 | 6 | [Download](27/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 28 | 36 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 17 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 18 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 9 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 136 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/paripikoumei
|
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] |
2023-09-18T06:32:51+00:00
|
{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["art"]}
|
2023-09-29T07:42:31+00:00
|
[] |
[] |
TAGS
#size_categories-1K<n<10K #license-mit #art #region-us
|
Bangumi Image Base of Paripi Koumei
===================================
This is the image base of bangumi Paripi Koumei, we detected 33 characters, 2237 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:
|
[] |
[
"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"
] |
0a010a393170cec2cebfa03149981e098d169020
|
# Dataset of Tōdō Misa
This is the dataset of Tōdō Misa, containing 184 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 | 184 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 389 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 184 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 184 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 184 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 184 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 184 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 389 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 389 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 389 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/todo_misa_shirobako
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-18T06:37:08+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-18T06:40:04+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Tōdō Misa
====================
This is the dataset of Tōdō Misa, containing 184 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"
] |
22cf0e1d893c57bb07e057809da588690dbf4d20
|
# Dataset Card for "hs_peer_support_chem"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
supramantest/hs_peer_support_chem
|
[
"region:us"
] |
2023-09-18T06:39:20+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2517979, "num_examples": 4000}, {"name": "test", "num_bytes": 1748526, "num_examples": 3000}, {"name": "validation", "num_bytes": 1736435, "num_examples": 3000}], "download_size": 2214089, "dataset_size": 6002940}}
|
2023-09-18T06:39:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hs_peer_support_chem"
More Information needed
|
[
"# Dataset Card for \"hs_peer_support_chem\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hs_peer_support_chem\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"hs_peer_support_chem\"\n\nMore Information needed"
] |
20121f99cf69feffb09da59ad1a3b0b1bf1d75bc
|
# Dataset Card for "JSON_expert"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Jackoon/JSON_expert
|
[
"region:us"
] |
2023-09-18T06:41:14+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35954, "num_examples": 36}], "download_size": 13720, "dataset_size": 35954}}
|
2023-09-18T06:41:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "JSON_expert"
More Information needed
|
[
"# Dataset Card for \"JSON_expert\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"JSON_expert\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"JSON_expert\"\n\nMore Information needed"
] |
bea94edc1421295fbd19016368868cfe4627c37f
|
# Dataset of Imai Midori
This is the dataset of Imai Midori, containing 276 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 | 276 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 611 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 276 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 276 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 276 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 276 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 276 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 611 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 611 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 611 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
CyberHarem/imai_midori_shirobako
|
[
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] |
2023-09-18T06:53:37+00:00
|
{"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]}
|
2023-09-18T06:56:37+00:00
|
[] |
[] |
TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
|
Dataset of Imai Midori
======================
This is the dataset of Imai Midori, containing 276 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"
] |
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