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f2e7d89fb54c4817de42ad47b5050b55e0040daa | # Dataset Card for "wikipedia"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nlp4j/wikipedia | [
"annotations_creators:no-annotation",
"source_datasets:original",
"language:ja",
"license:cc-by-sa-3.0",
"region:us"
]
| 2023-11-09T01:39:19+00:00 | {"annotations_creators": ["no-annotation"], "language": ["ja"], "license": ["cc-by-sa-3.0"], "source_datasets": ["original"], "pretty_name": "Wikipedia", "config_names": ["20230101.ja"], "configs": [{"config_name": "20230101.ja", "data_files": [{"split": "train", "path": "20230101.ja/train-*"}]}, {"config_name": "20230101.ja.type0", "data_files": [{"split": "train", "path": "20230101.ja.type0/train-*"}]}, {"config_name": "20230101.ja.type1", "data_files": [{"split": "train", "path": "20230101.ja.type1/train-*"}]}, {"config_name": "20230801.ja.type1", "data_files": [{"split": "train", "path": "20230801.ja.type1/train-*"}]}, {"config_name": "20230901.ja.type1", "data_files": [{"split": "train", "path": "20230901.ja.type1/train-*"}]}, {"config_name": "20231001.ja.type1", "data_files": [{"split": "train", "path": "20231001.ja.type1/train-*"}]}, {"config_name": "20231101.ja.type1", "data_files": [{"split": "train", "path": "20231101.ja.type1/train-*"}]}, {"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": [{"config_name": "20230101.ja", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5445627558, "num_examples": 2192693}], "download_size": 3016211435, "dataset_size": 5445627558}, {"config_name": "20230101.ja.type0", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "wikitext", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12897936907, "num_examples": 2192693}], "download_size": 6648740055, "dataset_size": 12897936907}, {"config_name": "20230101.ja.type1", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5445627558, "num_examples": 2192693}], "download_size": 3016211435, "dataset_size": 5445627558}, {"config_name": "20230801.ja.type1", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5578527799, "num_examples": 2237531}], "download_size": 3089288079, "dataset_size": 5578527799}, {"config_name": "20230901.ja.type1", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5595772816, "num_examples": 2243408}], "download_size": 3099146546, "dataset_size": 5595772816}, {"config_name": "20231001.ja.type1", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5616001418, "num_examples": 2246589}], "download_size": 3109672199, "dataset_size": 5616001418}, {"config_name": "20231101.ja.type1", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5636247958, "num_examples": 2252320}], "download_size": 3120907128, "dataset_size": 5636247958}, {"config_name": "default", "features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5132551154, "num_examples": 2192693}], "download_size": 2888006523, "dataset_size": 5132551154}]} | 2023-11-15T06:12:59+00:00 | []
| [
"ja"
]
| TAGS
#annotations_creators-no-annotation #source_datasets-original #language-Japanese #license-cc-by-sa-3.0 #region-us
| # Dataset Card for "wikipedia"
More Information needed | [
"# Dataset Card for \"wikipedia\"\n\nMore Information needed"
]
| [
"TAGS\n#annotations_creators-no-annotation #source_datasets-original #language-Japanese #license-cc-by-sa-3.0 #region-us \n",
"# Dataset Card for \"wikipedia\"\n\nMore Information needed"
]
| [
44,
11
]
| [
"passage: TAGS\n#annotations_creators-no-annotation #source_datasets-original #language-Japanese #license-cc-by-sa-3.0 #region-us \n# Dataset Card for \"wikipedia\"\n\nMore Information needed"
]
|
b5487fc76811b9e82de8148ecf3fb9ce1f6ace6a | # Dataset Card for "povarenok_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dim/povarenok_10k | [
"region:us"
]
| 2023-11-09T01:46:45+00:00 | {"dataset_info": {"features": [{"name": "full_receipt_text", "dtype": "string"}, {"name": "steps", "sequence": "string"}, {"name": "title_receipt", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "ingridients", "sequence": "string"}, {"name": "views", "dtype": "int64"}, {"name": "likes", "dtype": "int64"}, {"name": "ups", "dtype": "int64"}, {"name": "link", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37922507.52688172, "num_examples": 10000}], "download_size": 12019931, "dataset_size": 37922507.52688172}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T02:08:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "povarenok_10k"
More Information needed | [
"# Dataset Card for \"povarenok_10k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"povarenok_10k\"\n\nMore Information needed"
]
| [
6,
16
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"povarenok_10k\"\n\nMore Information needed"
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|
aba8281e51554a5620cd8ec6c554084c123fddb1 | # Dataset Card for "SWE-bench_bm25_40K"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
This dataset `SWE-bench_bm25_40K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 40,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
The `text` column can be used directly with LMs to generate patch files.
Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
```diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
```
This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | princeton-nlp/SWE-bench_bm25_40K | [
"arxiv:2310.06770",
"region:us"
]
| 2023-11-09T02:01:45+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "FAIL_TO_PASS", "dtype": "string"}, {"name": "PASS_TO_PASS", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3596634738, "num_examples": 18817}, {"name": "dev", "num_bytes": 41209504, "num_examples": 225}, {"name": "test", "num_bytes": 418767423, "num_examples": 2294}, {"name": "validation", "num_bytes": 37181237, "num_examples": 191}], "download_size": 17784463, "dataset_size": 4093792902}} | 2023-11-16T22:13:52+00:00 | [
"2310.06770"
]
| []
| TAGS
#arxiv-2310.06770 #region-us
| # Dataset Card for "SWE-bench_bm25_40K"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
This dataset 'SWE-bench_bm25_40K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 40,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.
The 'text' column can be used directly with LMs to generate patch files.
Models are instructed to generate 'patch') formatted file using the following template:
This format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
More Information needed | [
"# Dataset Card for \"SWE-bench_bm25_40K\"",
"### Dataset Summary\nSWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.\n\nThe dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?\n\nThis dataset 'SWE-bench_bm25_40K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 40,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.\nThe 'text' column can be used directly with LMs to generate patch files.\nModels are instructed to generate 'patch') formatted file using the following template:\n\n\nThis format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.",
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"## Dataset Structure",
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"### Supported Tasks and Leaderboards\nSWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL",
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"passage: TAGS\n#arxiv-2310.06770 #region-us \n# Dataset Card for \"SWE-bench_bm25_40K\"### Dataset Summary\nSWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.\n\nThe dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?\n\nThis dataset 'SWE-bench_bm25_40K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 40,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.\nThe 'text' column can be used directly with LMs to generate patch files.\nModels are instructed to generate 'patch') formatted file using the following template:\n\n\nThis format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.### Supported Tasks and Leaderboards\nSWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL### Languages\n\nThe text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.## Dataset Structure### Data Instances\nAn example of a SWE-bench datum is as follows:\n\n\n\nMore Information needed"
]
|
53b1cc4397295d97e42732eb2c81f34b7e76583a | # Dataset Card for "SWE-bench_bm25_27K"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
This dataset `SWE-bench_bm25_27K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 27,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
The `text` column can be used directly with LMs to generate patch files.
Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
```diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
```
This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | princeton-nlp/SWE-bench_bm25_27K | [
"arxiv:2310.06770",
"region:us"
]
| 2023-11-09T02:04:46+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "FAIL_TO_PASS", "dtype": "string"}, {"name": "PASS_TO_PASS", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2606110505, "num_examples": 18817}, {"name": "dev", "num_bytes": 29118974, "num_examples": 225}, {"name": "test", "num_bytes": 291723449, "num_examples": 2294}, {"name": "validation", "num_bytes": 27170684, "num_examples": 191}], "download_size": 12545702, "dataset_size": 2954123612}} | 2023-11-16T22:13:29+00:00 | [
"2310.06770"
]
| []
| TAGS
#arxiv-2310.06770 #region-us
| # Dataset Card for "SWE-bench_bm25_27K"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
This dataset 'SWE-bench_bm25_27K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 27,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.
The 'text' column can be used directly with LMs to generate patch files.
Models are instructed to generate 'patch') formatted file using the following template:
This format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
More Information needed | [
"# Dataset Card for \"SWE-bench_bm25_27K\"",
"### Dataset Summary\nSWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.\n\nThe dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?\n\nThis dataset 'SWE-bench_bm25_27K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 27,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.\nThe 'text' column can be used directly with LMs to generate patch files.\nModels are instructed to generate 'patch') formatted file using the following template:\n\n\nThis format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.",
"### Supported Tasks and Leaderboards\nSWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL",
"### Languages\n\nThe text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.",
"## Dataset Structure",
"### Data Instances\nAn example of a SWE-bench datum is as follows:\n\n\n\nMore Information needed"
]
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"TAGS\n#arxiv-2310.06770 #region-us \n",
"# Dataset Card for \"SWE-bench_bm25_27K\"",
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"passage: TAGS\n#arxiv-2310.06770 #region-us \n# Dataset Card for \"SWE-bench_bm25_27K\"### Dataset Summary\nSWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.\n\nThe dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?\n\nThis dataset 'SWE-bench_bm25_27K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 27,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.\nThe 'text' column can be used directly with LMs to generate patch files.\nModels are instructed to generate 'patch') formatted file using the following template:\n\n\nThis format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.### Supported Tasks and Leaderboards\nSWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL### Languages\n\nThe text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.## Dataset Structure### Data Instances\nAn example of a SWE-bench datum is as follows:\n\n\n\nMore Information needed"
]
|
3f227582dec87104bc15dac732162e89a8f865d3 | # Dataset Card for "SWE-bench_bm25_13K"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
This dataset `SWE-bench_bm25_13K` includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 13,000 `cl100k_base` tokens from the [`tiktoken`](https://github.com/openai/tiktoken) tokenization package used for OpenAI models.
The `text` column can be used directly with LMs to generate patch files.
Models are instructed to generate [`patch`](https://en.wikipedia.org/wiki/Patch_(Unix)) formatted file using the following template:
```diff
<patch>
diff
--- a/path/to/file.py
--- b/path/to/file.py
@@ -1,3 +1,3 @@
This is a test file.
-It contains several lines.
+It has been modified.
This is the third line.
</patch>
```
This format can be used directly with the [SWE-bench inference scripts](https://github.com/princeton-nlp/SWE-bench/tree/main/inference). Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
text: (str) - The input text including instructions, the "Oracle" retrieved file, and an example of the patch format for output.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | princeton-nlp/SWE-bench_bm25_13K | [
"arxiv:2310.06770",
"region:us"
]
| 2023-11-09T02:09:31+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "dev", "path": "data/dev-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "instance_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "FAIL_TO_PASS", "dtype": "string"}, {"name": "PASS_TO_PASS", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1537849718, "num_examples": 18817}, {"name": "dev", "num_bytes": 15941600, "num_examples": 225}, {"name": "test", "num_bytes": 154280625, "num_examples": 2294}, {"name": "validation", "num_bytes": 16292656, "num_examples": 191}], "download_size": 6567195, "dataset_size": 1724364599}} | 2023-11-16T22:13:06+00:00 | [
"2310.06770"
]
| []
| TAGS
#arxiv-2310.06770 #region-us
| # Dataset Card for "SWE-bench_bm25_13K"
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
This dataset 'SWE-bench_bm25_13K' includes a formatting of each instance using Pyserini's BM25 retrieval as described in the paper. The code context size limit is 13,000 'cl100k_base' tokens from the 'tiktoken' tokenization package used for OpenAI models.
The 'text' column can be used directly with LMs to generate patch files.
Models are instructed to generate 'patch') formatted file using the following template:
This format can be used directly with the SWE-bench inference scripts. Please refer to these scripts for more details on inference.
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at URL
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
More Information needed | [
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|
68674f95751d47880d6af7816704a7ca568d412a | # Dataset Card for "vn_news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ducha07/vn_news | [
"region:us"
]
| 2023-11-09T02:14:15+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "Unnamed: 0", "dtype": "int64"}, {"name": "start_time", "dtype": "int64"}, {"name": "end_time", "dtype": "int64"}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2111179.0, "num_examples": 60}], "download_size": 2098063, "dataset_size": 2111179.0}} | 2023-11-09T02:15:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vn_news"
More Information needed | [
"# Dataset Card for \"vn_news\"\n\nMore Information needed"
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|
9ddb3e3150b1dc96cb275f1407a95160ff5dfeac | # Dataset Card for "ha-en_RL-grow1_train_scorecompare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pranjali97/ha-en_RL-grow1_train_scorecompare | [
"region:us"
]
| 2023-11-09T02:33:02+00:00 | {"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "ref", "dtype": "string"}, {"name": "mt", "dtype": "string"}, {"name": "score_da", "dtype": "float64"}, {"name": "score_rfree", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 13814629, "num_examples": 29454}], "download_size": 3413056, "dataset_size": 13814629}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T02:33:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ha-en_RL-grow1_train_scorecompare"
More Information needed | [
"# Dataset Card for \"ha-en_RL-grow1_train_scorecompare\"\n\nMore Information needed"
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|
7990a25deaaaa139cdc2d60a7a83f6a3c6a3b20c | # Dataset Card for "ha-en_RL-grow1_valid_scorecompare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pranjali97/ha-en_RL-grow1_valid_scorecompare | [
"region:us"
]
| 2023-11-09T02:33:07+00:00 | {"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "ref", "dtype": "string"}, {"name": "mt", "dtype": "string"}, {"name": "score_da", "dtype": "float64"}, {"name": "score_rfree", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1579988, "num_examples": 3339}], "download_size": 0, "dataset_size": 1579988}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T02:35:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ha-en_RL-grow1_valid_scorecompare"
More Information needed | [
"# Dataset Card for \"ha-en_RL-grow1_valid_scorecompare\"\n\nMore Information needed"
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|
d17ed0f911e98b37867e04c0dfc674982ade20e3 | # Dataset Card for "satellite-coral-mapping"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aimsks/satellite-coral-mapping | [
"region:us"
]
| 2023-11-09T03:15:02+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "image"}, {"name": "Coral-Sea_S2_R2", "dtype": "image"}, {"name": "Coral-Sea_L8_R1", "dtype": "image"}, {"name": "Coral-Sea_S2_R1", "dtype": "image"}], "splits": [{"name": "training", "num_bytes": 13963277.0, "num_examples": 15}, {"name": "test", "num_bytes": 16455480.0, "num_examples": 18}], "download_size": 30431394, "dataset_size": 30418757.0}} | 2023-11-26T23:38:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "satellite-coral-mapping"
More Information needed | [
"# Dataset Card for \"satellite-coral-mapping\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
57f43ce2116bec84c3911f49cc01cdcfb38573f8 | <https://civitai.com/models/54867?modelVersionId=207286> | pluseen/amore_231101 | [
"license:apache-2.0",
"region:us"
]
| 2023-11-09T03:28:10+00:00 | {"license": "apache-2.0"} | 2023-12-15T02:41:26+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| <URL | []
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|
0a249af8170760ba2391ecc55cd39c0027df2633 | # Dataset Card for "riddler"
4.1 thousand high quality hand vetted riddles, augmented with gpt-4-turbo ( before they made it suck ). | unaidedelf87777/riddler | [
"license:apache-2.0",
"region:us"
]
| 2023-11-09T03:58:25+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "riddle", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 7268944, "num_examples": 4198}], "download_size": 3995459, "dataset_size": 7268944}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-13T02:10:05+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| # Dataset Card for "riddler"
4.1 thousand high quality hand vetted riddles, augmented with gpt-4-turbo ( before they made it suck ). | [
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]
|
347013da90ae072f11666581ff50d26a587b6604 | # Dataset Card for "orca-unanswerable-vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenthanhdo/orca-unanswerable-vi | [
"region:us"
]
| 2023-11-09T04:25:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 79650895, "num_examples": 27280}], "download_size": 39058385, "dataset_size": 79650895}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T04:25:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "orca-unanswerable-vi"
More Information needed | [
"# Dataset Card for \"orca-unanswerable-vi\"\n\nMore Information needed"
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9f30a7cf754f4389187b772224c58479a3481320 | # Dataset Card for "rexroth-finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | skymericsales/rexroth-finetune | [
"region:us"
]
| 2023-11-09T06:06:42+00:00 | {"dataset_info": {"features": [{"name": "Human", "dtype": "string"}, {"name": "Assistant", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 292324, "num_examples": 675}], "download_size": 103134, "dataset_size": 292324}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T06:13:03+00:00 | []
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| TAGS
#region-us
| # Dataset Card for "rexroth-finetune"
More Information needed | [
"# Dataset Card for \"rexroth-finetune\"\n\nMore Information needed"
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|
89408293046e8715b2539c856f7ffe5408a6db68 | # Dataset Card for "stack-smol-python-pii_checks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | sickboy9665/stack-smol-python-pii_checks | [
"region:us"
]
| 2023-11-09T06:18:39+00:00 | {"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "secrets", "dtype": "string"}, {"name": "has_secrets", "dtype": "bool"}, {"name": "number_secrets", "dtype": "int64"}, {"name": "new_content", "dtype": "string"}, {"name": "modified", "dtype": "bool"}, {"name": "references", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 867.0, "num_examples": 1}], "download_size": 9817, "dataset_size": 867.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-16T06:52:25+00:00 | []
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#region-us
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More Information needed | [
"# Dataset Card for \"stack-smol-python-pii_checks\"\n\nMore Information needed"
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|
6b884d8b152c20dbde7b87901ea5878a103afe9c |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | selajuf/dataset-selajuf123 | [
"region:us"
]
| 2023-11-09T06:43:39+00:00 | {} | 2023-11-09T07:01:42+00:00 | []
| []
| TAGS
#region-us
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# Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
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#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
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|
71d1ecc38bcb438d312ffeb9957c51c732f3fb43 | # Dataset Card for "phi-boolq-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | automated-research-group/phi-boolq-results | [
"region:us"
]
| 2023-11-09T06:49:10+00:00 | {"dataset_info": {"config_name": "{'do_sample'=False, 'beams'=1}", "features": [{"name": "id", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "bool_accuracy", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 475041, "num_examples": 3270}], "download_size": 282821, "dataset_size": 475041}, "configs": [{"config_name": "{'do_sample'=False, 'beams'=1}", "data_files": [{"split": "train", "path": "{'do_sample'=False, 'beams'=1}/train-*"}]}]} | 2023-11-30T07:39:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "phi-boolq-results"
More Information needed | [
"# Dataset Card for \"phi-boolq-results\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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cdfdf62d5f9f55665e1d2ba8ee2405b67f3ee6d3 | # Dataset Card for "flan2022"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kowndinya23/flan2022 | [
"region:us"
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| 2023-11-09T06:49:11+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "flan2021", "path": "data/flan2021-*"}, {"split": "t0", "path": "data/t0-*"}, {"split": "cot", "path": "data/cot-*"}, {"split": "niv2", "path": "data/niv2-*"}, {"split": "dialog", "path": "data/dialog-*"}]}], "dataset_info": {"features": [{"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "task_source", "dtype": "string"}, {"name": "task_name", "dtype": "string"}, {"name": "template_type", "dtype": "string"}], "splits": [{"name": "flan2021", "num_bytes": 8988026240, "num_examples": 5362361}, {"name": "t0", "num_bytes": 4602180562, "num_examples": 1650308}, {"name": "cot", "num_bytes": 209004809, "num_examples": 183848}, {"name": "niv2", "num_bytes": 13104211362, "num_examples": 10066896}, {"name": "dialog", "num_bytes": 1024507265, "num_examples": 553869}], "download_size": 16511300644, "dataset_size": 27927930238}} | 2023-11-09T07:10:33+00:00 | []
| []
| TAGS
#region-us
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More Information needed | [
"# Dataset Card for \"flan2022\"\n\nMore Information needed"
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|
d3e90687b96178da1cf88bf337a819469f005d28 | # Dataset Card for "SWE-bench__style-3__fs-oracle"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | feedback-to-code/SWE-bench__style-3__fs-oracle_large_tokenlength | [
"region:us"
]
| 2023-11-09T07:09:03+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": "instance_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "repo", "dtype": "string"}, {"name": "base_commit", "dtype": "string"}, {"name": "problem_statement", "dtype": "string"}, {"name": "hints_text", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[us]"}, {"name": "patch", "dtype": "string"}, {"name": "test_patch", "dtype": "string"}, {"name": "version", "dtype": "string"}, {"name": "FAIL_TO_PASS", "dtype": "string"}, {"name": "PASS_TO_PASS", "dtype": "string"}, {"name": "environment_setup_commit", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 74223487.16883117, "num_examples": 76}, {"name": "test", "num_bytes": 67214377, "num_examples": 21}, {"name": "validation", "num_bytes": 76946, "num_examples": 1}], "download_size": 62023490, "dataset_size": 141514810.16883117}} | 2023-11-09T09:15:08+00:00 | []
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More Information needed | [
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63e3c579b0d5101c047bcc60c5d4b22616c982b4 | # Dataset Card for "vi_text_news"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dinhbinh161/vi_text_news | [
"region:us"
]
| 2023-11-09T07:27:58+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 145710391, "num_examples": 1020122}], "download_size": 79305270, "dataset_size": 145710391}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T15:18:11+00:00 | []
| []
| TAGS
#region-us
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More Information needed | [
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9b6cd2c764faa2b4a3eb480803dfa12887209d50 | # Dataset Card for "black-outlined-essential-icons-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | leeseongmin451/black-outlined-essential-icons-1 | [
"region:us"
]
| 2023-11-09T07:38:57+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 874942.0, "num_examples": 380}], "download_size": 670334, "dataset_size": 874942.0}} | 2023-11-09T07:39:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "black-outlined-essential-icons-1"
More Information needed | [
"# Dataset Card for \"black-outlined-essential-icons-1\"\n\nMore Information needed"
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|
cd91f59428f745811c0e28b622c4f05c0ac91388 |
# Dataset Card for Evaluation run of psmathur/model_009
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/psmathur/model_009
- **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 [psmathur/model_009](https://huggingface.co/psmathur/model_009) 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 aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_psmathur__model_009_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T07:41:27.734814](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_009_public/blob/main/results_2023-11-09T07-41-27.734814.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": 0.3341023489932886,
"em_stderr": 0.004830400685277283,
"f1": 0.440147860738256,
"f1_stderr": 0.0045184970708564655,
"acc": 0.6087212395126058,
"acc_stderr": 0.0120913878225072
},
"harness|drop|3": {
"em": 0.3341023489932886,
"em_stderr": 0.004830400685277283,
"f1": 0.440147860738256,
"f1_stderr": 0.0045184970708564655
},
"harness|gsm8k|5": {
"acc": 0.39423805913570886,
"acc_stderr": 0.01346085235709565
},
"harness|winogrande|5": {
"acc": 0.8232044198895028,
"acc_stderr": 0.010721923287918747
}
}
```
### 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_psmathur__model_009 | [
"region:us"
]
| 2023-11-09T07:41:46+00:00 | {"pretty_name": "Evaluation run of psmathur/model_009", "dataset_summary": "Dataset automatically created during the evaluation run of model [psmathur/model_009](https://huggingface.co/psmathur/model_009) 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 aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_psmathur__model_009_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T07:41:27.734814](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_009_public/blob/main/results_2023-11-09T07-41-27.734814.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\": 0.3341023489932886,\n \"em_stderr\": 0.004830400685277283,\n \"f1\": 0.440147860738256,\n \"f1_stderr\": 0.0045184970708564655,\n \"acc\": 0.6087212395126058,\n \"acc_stderr\": 0.0120913878225072\n },\n \"harness|drop|3\": {\n \"em\": 0.3341023489932886,\n \"em_stderr\": 0.004830400685277283,\n \"f1\": 0.440147860738256,\n \"f1_stderr\": 0.0045184970708564655\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.39423805913570886,\n \"acc_stderr\": 0.01346085235709565\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8232044198895028,\n \"acc_stderr\": 0.010721923287918747\n }\n}\n```", "repo_url": "https://huggingface.co/psmathur/model_009", "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_11_09T07_41_27.734814", "path": ["**/details_harness|drop|3_2023-11-09T07-41-27.734814.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T07-41-27.734814.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T07_41_27.734814", "path": ["**/details_harness|gsm8k|5_2023-11-09T07-41-27.734814.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-09T07-41-27.734814.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_09T07_41_27.734814", "path": ["**/details_harness|winogrande|5_2023-11-09T07-41-27.734814.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-09T07-41-27.734814.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_09T07_41_27.734814", "path": ["results_2023-11-09T07-41-27.734814.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T07-41-27.734814.parquet"]}]}]} | 2023-11-09T07:41:54+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of psmathur/model_009
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model psmathur/model_009 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 aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T07:41:27.734814(note that their might be results for other tasks in 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 psmathur/model_009",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model psmathur/model_009 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 aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model psmathur/model_009 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 aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T07:41:27.734814(note that their might be results for other tasks in 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 psmathur/model_009## 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 psmathur/model_009 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 aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T07:41:27.734814(note that their might be results for other tasks in 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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|
86a621b7552af05c1ab74854305fbb98253c9110 | # Dataset Card for "dataset_2000_complexquestion_0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_0 | [
"region:us"
]
| 2023-11-09T07:44:24+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17893, "num_examples": 200}], "download_size": 0, "dataset_size": 17893}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_0"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_0\"\n\nMore Information needed"
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bf6dd7469467ad0b2452880caf3613c28f600915 | # Dataset Card for "dataset_2000_complexquestion_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_1 | [
"region:us"
]
| 2023-11-09T07:44:28+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17518, "num_examples": 200}], "download_size": 0, "dataset_size": 17518}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_1"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_1\"\n\nMore Information needed"
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|
1dd30a4def9c060d0434c2136476ab4da94dcee1 | # Dataset Card for "dataset_2000_complexquestion_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_2 | [
"region:us"
]
| 2023-11-09T07:44:31+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16852, "num_examples": 200}], "download_size": 0, "dataset_size": 16852}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_2"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_2\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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|
b44877c8c2a589656dead69df8a7dc4b6513b098 | # Dataset Card for "dataset_2000_complexquestion_3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_3 | [
"region:us"
]
| 2023-11-09T07:44:34+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17911, "num_examples": 200}], "download_size": 0, "dataset_size": 17911}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_3"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_3\"\n\nMore Information needed"
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"TAGS\n#region-us \n",
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|
c7d78cba013996e0234f0bb4087dac171e31c5b2 | # Dataset Card for "dataset_2000_complexquestion_4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_4 | [
"region:us"
]
| 2023-11-09T07:44:37+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17561, "num_examples": 200}], "download_size": 0, "dataset_size": 17561}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_4"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_4\"\n\nMore Information needed"
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|
d4ade275d76bb06f07ff40c9dd9e339c8bd19ee7 | # Dataset Card for "dataset_2000_complexquestion_5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_5 | [
"region:us"
]
| 2023-11-09T07:44:40+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17458, "num_examples": 200}], "download_size": 0, "dataset_size": 17458}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_5"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dataset_2000_complexquestion_5\"\n\nMore Information needed"
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|
b25fed4b1d1b279847c5ec38fe67189b57fbe48e | # Dataset Card for "dataset_2000_complexquestion_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_6 | [
"region:us"
]
| 2023-11-09T07:44:44+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17425, "num_examples": 200}], "download_size": 0, "dataset_size": 17425}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_6"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_6\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dataset_2000_complexquestion_6\"\n\nMore Information needed"
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| [
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|
414ce5a168c382035a95df596f4f59fa19bb976d | # Dataset Card for "dataset_2000_complexquestion_7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_7 | [
"region:us"
]
| 2023-11-09T07:44:47+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17635, "num_examples": 200}], "download_size": 0, "dataset_size": 17635}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_7"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_7\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
8cc43d00a64d5cbfa443603514b7606ba02c8d3c | # Dataset Card for "dataset_2000_complexquestion_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_8 | [
"region:us"
]
| 2023-11-09T07:44:50+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 17792, "num_examples": 200}], "download_size": 10911, "dataset_size": 17792}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T03:11:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dataset_2000_complexquestion_8"
More Information needed | [
"# Dataset Card for \"dataset_2000_complexquestion_8\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
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"passage: TAGS\n#region-us \n# Dataset Card for \"dataset_2000_complexquestion_8\"\n\nMore Information needed"
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|
954f5959aee9102347670a670e2afce7c1b0a0ef | # Dataset Card for "ultrachat-aem-v1.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenthanhdo/ultrachat-aem-v1.0 | [
"region:us"
]
| 2023-11-09T08:03:55+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "data", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 311481287.8581631, "num_examples": 54411}], "download_size": 169997532, "dataset_size": 311481287.8581631}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T08:04:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ultrachat-aem-v1.0"
More Information needed | [
"# Dataset Card for \"ultrachat-aem-v1.0\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ultrachat-aem-v1.0\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"ultrachat-aem-v1.0\"\n\nMore Information needed"
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|
42746d17c4a7fb1fbfb81ae483422014958ff6e5 | # Dataset Card for "black-outlined-essential-icons-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | leeseongmin451/black-outlined-essential-icons-small | [
"region:us"
]
| 2023-11-09T08:05:24+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 385944.0, "num_examples": 163}], "download_size": 306427, "dataset_size": 385944.0}} | 2023-11-09T08:05:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "black-outlined-essential-icons-small"
More Information needed | [
"# Dataset Card for \"black-outlined-essential-icons-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"black-outlined-essential-icons-small\"\n\nMore Information needed"
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25
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"passage: TAGS\n#region-us \n# Dataset Card for \"black-outlined-essential-icons-small\"\n\nMore Information needed"
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|
e93f85123f8ce118805d09e96199172bb025346c | # Dataset Card for "ultrachat-aem-alpaca-v1.0"
This dataset is a subset of the https://huggingface.co/datasets/stingning/ultrachat.
This dataset focuses on the question answering task on an existing context, using a simple keyword filter (any question containing one of these keywords: passage, article, context). I also extract only the first round of conversation and convert it to the familiar alpaca format, and further filter so that the dataset only contain long input (which means complex instruction imo).
Code for generate the dataset:
```py
from datasets import load_dataset
ultra = load_dataset(
"stingning/ultrachat",
data_files=[
"train_6.jsonl",
"train_7.jsonl",
"train_8.jsonl",
"train_9.jsonl"
],
split="train"
)
def get_first_turn(example):
data = example["data"]
instruction, output = data[0], data[1]
example.pop("data")
example["instruction"] = instruction
example["input"] = ''
example["output"] = output
return example
## Assistance on Existing Materials
def aem(example):
keywords = ["article", "context", "passage"]
data = example["data"]
first_instruction = data[0]
flag = False
if any([kw in first_instruction.lower() for kw in keywords]):
flag = True
return flag
ultra_aem = ultra.filter(aem)
ultra_aem_long = ultra_aem.filter(lambda x: len(x["data"][0].split()) > 200)
ultra_aem_first_turn = ultra_aem_long.map(get_first_turn)
ultra_aem_first_turn.push_to_hub("nguyenthanhdo/ultrachat-aem-alpaca-v1.0")
```
**TODO**
Intended use for this dataset was for closed question answering only. But ultrachat dataset also contains rewriting, translation and summarization tasks.
- Only keep the question answering task by further filtering, since currently this dataset still contains contamination because of samples for other tasks.
- Better filtering to seperate 4 tasks: question answering, rewriting, translation and summarization. | nguyenthanhdo/ultrachat-aem-alpaca-v1.0 | [
"region:us"
]
| 2023-11-09T08:07:06+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 208601043, "num_examples": 54411}], "download_size": 126826003, "dataset_size": 208601043}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T08:25:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ultrachat-aem-alpaca-v1.0"
This dataset is a subset of the URL
This dataset focuses on the question answering task on an existing context, using a simple keyword filter (any question containing one of these keywords: passage, article, context). I also extract only the first round of conversation and convert it to the familiar alpaca format, and further filter so that the dataset only contain long input (which means complex instruction imo).
Code for generate the dataset:
TODO
Intended use for this dataset was for closed question answering only. But ultrachat dataset also contains rewriting, translation and summarization tasks.
- Only keep the question answering task by further filtering, since currently this dataset still contains contamination because of samples for other tasks.
- Better filtering to seperate 4 tasks: question answering, rewriting, translation and summarization. | [
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"passage: TAGS\n#region-us \n# Dataset Card for \"ultrachat-aem-alpaca-v1.0\"\nThis dataset is a subset of the URL\n\nThis dataset focuses on the question answering task on an existing context, using a simple keyword filter (any question containing one of these keywords: passage, article, context). I also extract only the first round of conversation and convert it to the familiar alpaca format, and further filter so that the dataset only contain long input (which means complex instruction imo).\nCode for generate the dataset:\n\n\nTODO\nIntended use for this dataset was for closed question answering only. But ultrachat dataset also contains rewriting, translation and summarization tasks.\n- Only keep the question answering task by further filtering, since currently this dataset still contains contamination because of samples for other tasks.\n- Better filtering to seperate 4 tasks: question answering, rewriting, translation and summarization."
]
|
a6ee58253a6d19e3e7ac0d8cb6af30417e16368f |
# Dataset Card for Dataset Name
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## Dataset Details
### Dataset Description
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- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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## Uses
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## Dataset Structure
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### Annotations [optional]
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#### Annotation process
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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[More Information Needed] | egoing/dataset_repository_name | [
"region:us"
]
| 2023-11-09T08:17:36+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:12:25+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Dataset Name
## Dataset Details
### Dataset Description
- Curated by:
- Funded by [optional]:
- Shared by [optional]:
- Language(s) (NLP):
- License:
### Dataset Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Out-of-Scope Use
## Dataset Structure
## Dataset Creation
### Curation Rationale
### Source Data
#### Data Collection and Processing
#### Who are the source data producers?
### Annotations [optional]
#### Annotation process
#### Who are the annotators?
#### Personal and Sensitive Information
## Bias, Risks, and Limitations
### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Dataset Card Authors [optional]
## Dataset Card Contact
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84d511b4a92cac59d1be68927bb012d684b783f0 | # Dataset Card for "temp03"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | growth-cadet/temp03 | [
"region:us"
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| 2023-11-09T08:27:54+00:00 | {"dataset_info": {"features": [{"name": "round_name", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "sequence", "dtype": "string"}, {"name": "labels", "sequence": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 1296454, "num_examples": 4098}], "download_size": 0, "dataset_size": 1296454}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T18:53:28+00:00 | []
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1aab6ec83c246dc52bbf2add2f6c1fc7ad356aba | # Dataset Card for "guanaco-llama2-1k_transformed_sana"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SanaFalakJ/guanaco-llama2-1k_transformed_sana | [
"region:us"
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| 2023-11-09T08:54:41+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-*"}]}]} | 2023-11-09T08:54:46+00:00 | []
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| # Dataset Card for "guanaco-llama2-1k_transformed_sana"
More Information needed | [
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31d5bbf455b1362778698a2cd3f76ee90d2c7267 | # Dataset Card for "portraits_xs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | recoilme/portraits_xs | [
"region:us"
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More Information needed | [
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|
859f3749ec2e7989f12df56fa35221c240401647 |
# Dataset Card for Evaluation run of psmathur/model_007_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/psmathur/model_007_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 [psmathur/model_007_v2](https://huggingface.co/psmathur/model_007_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 aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_psmathur__model_007_v2_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T09:02:32.950364](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_007_v2_public/blob/main/results_2023-11-09T09-02-32.950364.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": 0.1636954697986577,
"em_stderr": 0.0037891361135837117,
"f1": 0.31382655201342385,
"f1_stderr": 0.0038067833114928977,
"acc": 0.5639691402386229,
"acc_stderr": 0.011361388955682963
},
"harness|drop|3": {
"em": 0.1636954697986577,
"em_stderr": 0.0037891361135837117,
"f1": 0.31382655201342385,
"f1_stderr": 0.0038067833114928977
},
"harness|gsm8k|5": {
"acc": 0.28658074298711145,
"acc_stderr": 0.012454841668337704
},
"harness|winogrande|5": {
"acc": 0.8413575374901342,
"acc_stderr": 0.010267936243028223
}
}
```
### 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_psmathur__model_007_v2 | [
"region:us"
]
| 2023-11-09T09:02:51+00:00 | {"pretty_name": "Evaluation run of psmathur/model_007_v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [psmathur/model_007_v2](https://huggingface.co/psmathur/model_007_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 aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_psmathur__model_007_v2_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T09:02:32.950364](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_007_v2_public/blob/main/results_2023-11-09T09-02-32.950364.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\": 0.1636954697986577,\n \"em_stderr\": 0.0037891361135837117,\n \"f1\": 0.31382655201342385,\n \"f1_stderr\": 0.0038067833114928977,\n \"acc\": 0.5639691402386229,\n \"acc_stderr\": 0.011361388955682963\n },\n \"harness|drop|3\": {\n \"em\": 0.1636954697986577,\n \"em_stderr\": 0.0037891361135837117,\n \"f1\": 0.31382655201342385,\n \"f1_stderr\": 0.0038067833114928977\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.28658074298711145,\n \"acc_stderr\": 0.012454841668337704\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8413575374901342,\n \"acc_stderr\": 0.010267936243028223\n }\n}\n```", "repo_url": "https://huggingface.co/psmathur/model_007_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_11_09T09_02_32.950364", "path": ["**/details_harness|drop|3_2023-11-09T09-02-32.950364.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T09-02-32.950364.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T09_02_32.950364", "path": ["**/details_harness|gsm8k|5_2023-11-09T09-02-32.950364.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-09T09-02-32.950364.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_09T09_02_32.950364", "path": ["**/details_harness|winogrande|5_2023-11-09T09-02-32.950364.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-09T09-02-32.950364.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_09T09_02_32.950364", "path": ["results_2023-11-09T09-02-32.950364.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T09-02-32.950364.parquet"]}]}]} | 2023-11-09T09:02:59+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of psmathur/model_007_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 psmathur/model_007_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 aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T09:02:32.950364(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model psmathur/model_007_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 aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of psmathur/model_007_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 psmathur/model_007_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 aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T09:02:32.950364(note that their might be results for other tasks in 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"
]
|
b95d7379a78d2c0316effc869a51d89fbcdd884b |
# Code-Evol-Instruct-OSS
## Summary
Code-Evol-Instruct-OSS is a dataset that was generated with Code Evol-Instruct by prompting open-souce LLMs, WizardLM-13B-v1.2 and WizardCoder-34B-Python.
The underlying process is explained in the paper [code-evol-instruct](https://arxiv.org/abs/2306.08568). This algorithm gave birth to famous open-souce code LLMs, WizardCoder-Family.
## Our approach
- We did not use any closed-source LLMs.
- Our seed dataset is sourced from [self-instruct-starcoder](https://huggingface.co/datasets/codeparrot/self-instruct-starcoder).
- We leverage the WizardLM-13B-v1.2 to evol the instructions in three rounds.
- The responses to each instruction are generated using WizardCoder-34B-Python.
- Samples that are excessively long or lack code responses are filtered out.
- The final dataset contains 4308 samples.
## Preliminary Experiments
We've fine-tuned the starcoderbase-3b using this dataset, achieving a 28.7 pass@1 on HumanEval (greedy), surpassing the original model by approximately 8 points.
## Citation
If you use this dataset, please cite the paper of WizardCoder.
```
@misc{luo2023wizardcoder,
title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
author={Ziyang Luo and Can Xu and Pu Zhao and Qingfeng Sun and Xiubo Geng and Wenxiang Hu and Chongyang Tao and Jing Ma and Qingwei Lin and Daxin Jiang},
year={2023},
eprint={2306.08568},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | HKBU-NLP/Code-Evol-Instruct-OSS | [
"size_categories:1K<n<10K",
"language:en",
"license:bigcode-openrail-m",
"arxiv:2306.08568",
"region:us"
]
| 2023-11-09T09:08:08+00:00 | {"language": ["en"], "license": "bigcode-openrail-m", "size_categories": ["1K<n<10K"]} | 2023-11-09T14:05:04+00:00 | [
"2306.08568"
]
| [
"en"
]
| TAGS
#size_categories-1K<n<10K #language-English #license-bigcode-openrail-m #arxiv-2306.08568 #region-us
|
# Code-Evol-Instruct-OSS
## Summary
Code-Evol-Instruct-OSS is a dataset that was generated with Code Evol-Instruct by prompting open-souce LLMs, WizardLM-13B-v1.2 and WizardCoder-34B-Python.
The underlying process is explained in the paper code-evol-instruct. This algorithm gave birth to famous open-souce code LLMs, WizardCoder-Family.
## Our approach
- We did not use any closed-source LLMs.
- Our seed dataset is sourced from self-instruct-starcoder.
- We leverage the WizardLM-13B-v1.2 to evol the instructions in three rounds.
- The responses to each instruction are generated using WizardCoder-34B-Python.
- Samples that are excessively long or lack code responses are filtered out.
- The final dataset contains 4308 samples.
## Preliminary Experiments
We've fine-tuned the starcoderbase-3b using this dataset, achieving a 28.7 pass@1 on HumanEval (greedy), surpassing the original model by approximately 8 points.
If you use this dataset, please cite the paper of WizardCoder.
| [
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"## Summary\n\nCode-Evol-Instruct-OSS is a dataset that was generated with Code Evol-Instruct by prompting open-souce LLMs, WizardLM-13B-v1.2 and WizardCoder-34B-Python.\nThe underlying process is explained in the paper code-evol-instruct. This algorithm gave birth to famous open-souce code LLMs, WizardCoder-Family.",
"## Our approach\n\n- We did not use any closed-source LLMs.\n- Our seed dataset is sourced from self-instruct-starcoder.\n- We leverage the WizardLM-13B-v1.2 to evol the instructions in three rounds.\n- The responses to each instruction are generated using WizardCoder-34B-Python.\n- Samples that are excessively long or lack code responses are filtered out.\n- The final dataset contains 4308 samples.",
"## Preliminary Experiments\n\nWe've fine-tuned the starcoderbase-3b using this dataset, achieving a 28.7 pass@1 on HumanEval (greedy), surpassing the original model by approximately 8 points.\n\nIf you use this dataset, please cite the paper of WizardCoder."
]
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"## Our approach\n\n- We did not use any closed-source LLMs.\n- Our seed dataset is sourced from self-instruct-starcoder.\n- We leverage the WizardLM-13B-v1.2 to evol the instructions in three rounds.\n- The responses to each instruction are generated using WizardCoder-34B-Python.\n- Samples that are excessively long or lack code responses are filtered out.\n- The final dataset contains 4308 samples.",
"## Preliminary Experiments\n\nWe've fine-tuned the starcoderbase-3b using this dataset, achieving a 28.7 pass@1 on HumanEval (greedy), surpassing the original model by approximately 8 points.\n\nIf you use this dataset, please cite the paper of WizardCoder."
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"passage: TAGS\n#size_categories-1K<n<10K #language-English #license-bigcode-openrail-m #arxiv-2306.08568 #region-us \n# Code-Evol-Instruct-OSS## Summary\n\nCode-Evol-Instruct-OSS is a dataset that was generated with Code Evol-Instruct by prompting open-souce LLMs, WizardLM-13B-v1.2 and WizardCoder-34B-Python.\nThe underlying process is explained in the paper code-evol-instruct. This algorithm gave birth to famous open-souce code LLMs, WizardCoder-Family.## Our approach\n\n- We did not use any closed-source LLMs.\n- Our seed dataset is sourced from self-instruct-starcoder.\n- We leverage the WizardLM-13B-v1.2 to evol the instructions in three rounds.\n- The responses to each instruction are generated using WizardCoder-34B-Python.\n- Samples that are excessively long or lack code responses are filtered out.\n- The final dataset contains 4308 samples.## Preliminary Experiments\n\nWe've fine-tuned the starcoderbase-3b using this dataset, achieving a 28.7 pass@1 on HumanEval (greedy), surpassing the original model by approximately 8 points.\n\nIf you use this dataset, please cite the paper of WizardCoder."
]
|
b5af40207d31e749d1ea11db13d349989f16ca7e |
Test data: PhoMT
Train data: PhoMT (filter len between 40 to 100) | NghiemAbe/translation-vietnamese-english | [
"task_categories:translation",
"size_categories:100M<n<1B",
"language:vi",
"language:en",
"license:mit",
"region:us"
]
| 2023-11-09T09:22:40+00:00 | {"language": ["vi", "en"], "license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["translation"]} | 2023-11-09T09:34:40+00:00 | []
| [
"vi",
"en"
]
| TAGS
#task_categories-translation #size_categories-100M<n<1B #language-Vietnamese #language-English #license-mit #region-us
|
Test data: PhoMT
Train data: PhoMT (filter len between 40 to 100) | []
| [
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|
ecd7a3bf82a6c8c09551b39023d203551b61c1c3 | # Dataset Card for "english-to-hindi"
**Dataset Card: English-to-Hindi Translation**
**Overview:**
- **Dataset Name:** English-to-Hindi Translation
- **Dataset Size:** 128K sentences
- **Source:** Curated list of English sentences paired with their Hindi translations.
- **Use Case:** Training machine translation models, specifically English-to-Hindi translation using transformer architectures.
**Data Collection:**
- **Collection Method:** Manual translation by bilingual speakers.
- **Data Quality:** High quality with accurate translations.
**Dataset Composition:**
- **Language Pair:** English to Hindi
- **Text Type:** General sentences, covering a wide range of topics.
- **Text Length:** Varied lengths of sentences.
**Data Format:**
- **Format:** CSV, each row containing an English sentence and its corresponding Hindi translation.
**Licensing:**
- **License:** MIT
**Dataset Distribution:**
- **Availability:**
```python
from datasets import load_dataset
dataset = load_dataset("Aarif1430/english-to-hindi")
```
```shell
curl -X GET "https://datasets-server.huggingface.co/rows?dataset=Aarif1430%2Fenglish-to-hindi&config=default&split=train&offset=0&length=100"
```
- **Download Size:** 21.7 MB
**Potential Use Cases:**
- Training and evaluating machine translation models.
- Research in natural language processing, specifically in the field of translation.
**Limitations:**
- Limited coverage of domain-specific language or specialized terminology.
**Additional Information:**
- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.
**Citation:**
- If you use this dataset in your work, please cite the dataset using the provided citation information.
**References:**
- https://huggingface.co/datasets/ai4bharat/samanantar
| Aarif1430/english-to-hindi | [
"region:us"
]
| 2023-11-09T09:29:28+00:00 | {"dataset_info": {"features": [{"name": "english_sentence", "dtype": "string"}, {"name": "hindi_sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41188315, "num_examples": 127705}], "download_size": 21737146, "dataset_size": 41188315}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-12T09:13:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "english-to-hindi"
Dataset Card: English-to-Hindi Translation
Overview:
- Dataset Name: English-to-Hindi Translation
- Dataset Size: 128K sentences
- Source: Curated list of English sentences paired with their Hindi translations.
- Use Case: Training machine translation models, specifically English-to-Hindi translation using transformer architectures.
Data Collection:
- Collection Method: Manual translation by bilingual speakers.
- Data Quality: High quality with accurate translations.
Dataset Composition:
- Language Pair: English to Hindi
- Text Type: General sentences, covering a wide range of topics.
- Text Length: Varied lengths of sentences.
Data Format:
- Format: CSV, each row containing an English sentence and its corresponding Hindi translation.
Licensing:
- License: MIT
Dataset Distribution:
- Availability:
- Download Size: 21.7 MB
Potential Use Cases:
- Training and evaluating machine translation models.
- Research in natural language processing, specifically in the field of translation.
Limitations:
- Limited coverage of domain-specific language or specialized terminology.
Additional Information:
- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.
Citation:
- If you use this dataset in your work, please cite the dataset using the provided citation information.
References:
- URL
| [
"# Dataset Card for \"english-to-hindi\"\nDataset Card: English-to-Hindi Translation\n\nOverview:\n- Dataset Name: English-to-Hindi Translation\n- Dataset Size: 128K sentences\n- Source: Curated list of English sentences paired with their Hindi translations.\n- Use Case: Training machine translation models, specifically English-to-Hindi translation using transformer architectures.\n\nData Collection:\n- Collection Method: Manual translation by bilingual speakers.\n- Data Quality: High quality with accurate translations.\n\nDataset Composition:\n- Language Pair: English to Hindi\n- Text Type: General sentences, covering a wide range of topics.\n- Text Length: Varied lengths of sentences.\n\nData Format:\n- Format: CSV, each row containing an English sentence and its corresponding Hindi translation.\n\nLicensing:\n- License: MIT\n\nDataset Distribution:\n- Availability:\n \n \n- Download Size: 21.7 MB\n\nPotential Use Cases:\n- Training and evaluating machine translation models.\n- Research in natural language processing, specifically in the field of translation.\n\nLimitations:\n- Limited coverage of domain-specific language or specialized terminology.\n\nAdditional Information:\n- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.\n\nCitation:\n- If you use this dataset in your work, please cite the dataset using the provided citation information.\n\nReferences:\n- URL"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"english-to-hindi\"\nDataset Card: English-to-Hindi Translation\n\nOverview:\n- Dataset Name: English-to-Hindi Translation\n- Dataset Size: 128K sentences\n- Source: Curated list of English sentences paired with their Hindi translations.\n- Use Case: Training machine translation models, specifically English-to-Hindi translation using transformer architectures.\n\nData Collection:\n- Collection Method: Manual translation by bilingual speakers.\n- Data Quality: High quality with accurate translations.\n\nDataset Composition:\n- Language Pair: English to Hindi\n- Text Type: General sentences, covering a wide range of topics.\n- Text Length: Varied lengths of sentences.\n\nData Format:\n- Format: CSV, each row containing an English sentence and its corresponding Hindi translation.\n\nLicensing:\n- License: MIT\n\nDataset Distribution:\n- Availability:\n \n \n- Download Size: 21.7 MB\n\nPotential Use Cases:\n- Training and evaluating machine translation models.\n- Research in natural language processing, specifically in the field of translation.\n\nLimitations:\n- Limited coverage of domain-specific language or specialized terminology.\n\nAdditional Information:\n- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.\n\nCitation:\n- If you use this dataset in your work, please cite the dataset using the provided citation information.\n\nReferences:\n- URL"
]
| [
6,
326
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"english-to-hindi\"\nDataset Card: English-to-Hindi Translation\n\nOverview:\n- Dataset Name: English-to-Hindi Translation\n- Dataset Size: 128K sentences\n- Source: Curated list of English sentences paired with their Hindi translations.\n- Use Case: Training machine translation models, specifically English-to-Hindi translation using transformer architectures.\n\nData Collection:\n- Collection Method: Manual translation by bilingual speakers.\n- Data Quality: High quality with accurate translations.\n\nDataset Composition:\n- Language Pair: English to Hindi\n- Text Type: General sentences, covering a wide range of topics.\n- Text Length: Varied lengths of sentences.\n\nData Format:\n- Format: CSV, each row containing an English sentence and its corresponding Hindi translation.\n\nLicensing:\n- License: MIT\n\nDataset Distribution:\n- Availability:\n \n \n- Download Size: 21.7 MB\n\nPotential Use Cases:\n- Training and evaluating machine translation models.\n- Research in natural language processing, specifically in the field of translation.\n\nLimitations:\n- Limited coverage of domain-specific language or specialized terminology.\n\nAdditional Information:\n- The dataset was created to facilitate research and development in English-to-Hindi machine translation. Researchers and developers are encouraged to contribute to and improve the dataset.\n\nCitation:\n- If you use this dataset in your work, please cite the dataset using the provided citation information.\n\nReferences:\n- URL"
]
|
9f111b0c5d423c9073a0090d5ec99d5bf3b05239 | # Dataset Card for "giant-midi-base-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roszcz/giant-midi-base-v2 | [
"region:us"
]
| 2023-11-09T09:51:52+00:00 | {"dataset_info": {"features": [{"name": "notes", "struct": [{"name": "end", "sequence": "float64"}, {"name": "pitch", "sequence": "int64"}, {"name": "start", "sequence": "float64"}, {"name": "velocity", "sequence": "int64"}]}, {"name": "control_changes", "struct": [{"name": "number", "sequence": "int64"}, {"name": "time", "sequence": "float64"}, {"name": "value", "sequence": "int64"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1397531927, "num_examples": 10854}], "download_size": 443661339, "dataset_size": 1397531927}} | 2024-01-16T18:23:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "giant-midi-base-v2"
More Information needed | [
"# Dataset Card for \"giant-midi-base-v2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
df51f039fe56a5502b537caed9c12e90f5f9ad76 | # Dataset Card for "giant-midi-sustain-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roszcz/giant-midi-sustain-v2 | [
"region:us"
]
| 2023-11-09T10:00:02+00:00 | {"dataset_info": {"features": [{"name": "notes", "struct": [{"name": "duration", "sequence": "float64"}, {"name": "end", "sequence": "float64"}, {"name": "pitch", "sequence": "int64"}, {"name": "start", "sequence": "float64"}, {"name": "velocity", "sequence": "int64"}]}, {"name": "source", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1549407071, "num_examples": 10854}], "download_size": 483682235, "dataset_size": 1549407071}} | 2024-01-16T18:33:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "giant-midi-sustain-v2"
More Information needed | [
"# Dataset Card for \"giant-midi-sustain-v2\"\n\nMore Information needed"
]
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|
a42c6af9b0bba8bf9723dcc1ceab0bc22c0192a4 | # Dataset Card for "ultrafeedback-enable-checkpoint-100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | alvarobartt/ultrafeedback-enable-checkpoint-100 | [
"region:us"
]
| 2023-11-09T10:06:16+00:00 | {"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "generation_prompt", "dtype": "string"}, {"name": "raw_generation_responses", "sequence": "string"}, {"name": "generations", "sequence": "string"}, {"name": "labelling_prompt", "list": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}, {"name": "raw_labelling_response", "struct": [{"name": "choices", "list": [{"name": "finish_reason", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "message", "struct": [{"name": "content", "dtype": "string"}, {"name": "role", "dtype": "string"}]}]}, {"name": "created", "dtype": "int64"}, {"name": "id", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "object", "dtype": "string"}, {"name": "usage", "struct": [{"name": "completion_tokens", "dtype": "int64"}, {"name": "prompt_tokens", "dtype": "int64"}, {"name": "total_tokens", "dtype": "int64"}]}]}, {"name": "rating", "sequence": "int64"}, {"name": "rationale", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 926832, "num_examples": 100}], "download_size": 391509, "dataset_size": 926832}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T10:06:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ultrafeedback-enable-checkpoint-100"
More Information needed | [
"# Dataset Card for \"ultrafeedback-enable-checkpoint-100\"\n\nMore Information needed"
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|
23acb374a28270239dd5d7d893b8117dbee95907 | # Dataset Card for "kaggle_scripts_new_format_subset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | loubnabnl/kaggle_scripts_new_format_subset | [
"region:us"
]
| 2023-11-09T10:19:45+00:00 | {"dataset_info": {"features": [{"name": "file_id", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "local_path", "dtype": "string"}, {"name": "kaggle_dataset_name", "dtype": "string"}, {"name": "kaggle_dataset_owner", "dtype": "string"}, {"name": "kversion", "dtype": "string"}, {"name": "kversion_datasetsources", "dtype": "string"}, {"name": "dataset_versions", "dtype": "string"}, {"name": "datasets", "dtype": "string"}, {"name": "users", "dtype": "string"}, {"name": "script", "dtype": "string"}, {"name": "df_info", "dtype": "string"}, {"name": "has_data_info", "dtype": "bool"}, {"name": "nb_filenames", "dtype": "int64"}, {"name": "retreived_data_description", "dtype": "string"}, {"name": "script_nb_tokens", "dtype": "int64"}, {"name": "upvotes", "dtype": "int64"}, {"name": "tokens_description", "dtype": "int64"}, {"name": "tokens_script", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 26174515828, "num_examples": 1160428}], "download_size": 10883466302, "dataset_size": 26174515828}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T11:55:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "kaggle_scripts_new_format_subset"
More Information needed | [
"# Dataset Card for \"kaggle_scripts_new_format_subset\"\n\nMore Information needed"
]
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"TAGS\n#region-us \n",
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|
4128e09cefdf7401a4c2796b4db5bad04a8a16e4 | # Dataset Card for "orca-unanswerable-vi-v1.1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nguyenthanhdo/orca-unanswerable-vi-v1.1 | [
"region:us"
]
| 2023-11-09T10:28:12+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58590707.84329179, "num_examples": 20067}], "download_size": 34046227, "dataset_size": 58590707.84329179}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T10:28:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "orca-unanswerable-vi-v1.1"
More Information needed | [
"# Dataset Card for \"orca-unanswerable-vi-v1.1\"\n\nMore Information needed"
]
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|
b8bda78e9d337e47ae9686df005315b9a3042f7d | # Dataset Card for "summaries-de-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tucan-ai/summaries-de-v2 | [
"region:us"
]
| 2023-11-09T10:40:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23211177.6, "num_examples": 2015}], "download_size": 13703832, "dataset_size": 23211177.6}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T10:42:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summaries-de-v2"
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"# Dataset Card for \"summaries-de-v2\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"summaries-de-v2\"\n\nMore Information needed"
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|
44f926923f1e5c6ca0ab45bff950764083d84e9a | # Aalap Instruction dataset
<!-- Provide a quick summary of the dataset. -->
This dataset aims to build an AI Assistant for Legal and Paralegal functions in India (Aalap). The main objective behind creating Aalap was to train a small-sized LLM specializing in specific legal tasks focusing on legal reasoning. Hence, prioritizing which legal tasks to focus on was an important step.
We discussed with Legal practitioners which legal tasks they would want to be automated. Then, we selected the most common legal tasks for publicly available input datasets. Since the Indian judiciary operates mainly in English, all the datasets are currently in the English language.
<!--
This dataset contains the following legal tasks:
1. **Issue generation:** Based on the facts of the case, create legal issues for a court. Legal issues are the key points on which the verdict needs to be delivered.
2. **Court Argument Generation:** Based on the facts of a case, legal issues, and applicable statute names, generate arguments for a given party. Arguments for both the appellant and counterarguments for the defendant are provided.
3. **Generating event timelines of legal docs:** Extract important events and their dates from the input text description of events and output a chronologically sorted event list with date and a brief event description.
4. **Combining multiple event timelines of legal docs:** Merging different event timelines and sorting the final timeline chronologically.
5. **Court Judgment Summaries (Headnotes):** Create a court judgment summary based on the input judgment text.
6. **Statute Ingredients:** Break the input statute definition into the ingredients that are needed for the statute to be applicable.
This dataset leverages the following publicly available datasets.
7. **Legalbench Training Data Explanations:** ORCA style explanation of the [legalbench training data](https://huggingface.co/datasets/nguha/legalbench) created by GPT4.
8. **Contract clause generation and modification:** Generation of new contract clauses and modification of existing contract clauses. [Data](https://huggingface.co/datasets/NebulaSense/Legal_Clause_Instructions)
9. **Legal Open ORCA**: Filtered the open orca dataset [1M GPT4 instructions data](https://huggingface.co/datasets/Open-Orca/OpenOrca/blob/main/1M-GPT4-Augmented.parquet) for law-related data.
10. **Natural Instructions filtered for law**: Random sample of [Natural Instructions dataset](https://github.com/allenai/natural-instructions/tree/master/tasks) filtered for law
While the focus of this dataset is Indian legal tasks, it can be used to train models that work in other countries that follow common law.
## How were these tasks chosen?
We discussed with Legal practitioners about which legal tasks they would want to be automated. Based on that feedback and the availability of the dataset, we selected these legal tasks to focus on.
-->
## Common Data Sources
The following are the commonly used data sources for preparing the input instructions.
1. **Judgments Facts**: Case facts are typically written in a court judgment. Such facts from judgments texts were extracted using Opennyai's rhetorical roles model \cite{kalamkarcorpus} on randomly chosen Indian Supreme Court \& high court judgments. These judgment facts were used as inputs for multiple tasks like argument generation, issues generation, and event timeline creation.
2. **Judgments Statutes**: Relevant Statutes of a judgment are the ones that were extracted from corresponding judgment text using Opennyai's Named Entity Recognition model \cite{kalamkar2022named}.
3. **FIRs**: Publicly available police First Investigation Reports from the states of Maharashtra and Delhi were used to collect descriptions of real-life events. These FIRs were used to create event timeline creation tasks.
## Dataset Details
<!--
| Data Name | Source | Size | License | Description |
| ----------------------------------------- | ---------------------------------------------------------------------------------------- | ----- | ----------- |----------- |
| issues_generation | Judgments | 601 | CC0-1.0 | Facts of the case are extracted using Opennyai's [rhetorical roles model](https://github.com/OpenNyAI/Opennyai) on randomly chosen Indian Supreme Court & high court judgments. These facts were then sent to gpt-3.5-turbo for generating legal issues on which the court needs to make decisions.|
| argument_generation | Judgements | 1200 | CC0-1.0 | The same sample court judgments used for Issues Generation were used for generating arguments. Arguments for petitioners were created using chatgpt-3.5-turbo based on the facts of the case, chatgpt-3.5-turbo, earlier generated legal issues and statutes names extracted using Opennyai's [Named Entity Recognition](https://github.com/Legal-NLP-EkStep/legal_NER) model. Counterarguments for defendants were created using these arguments for the petitioner, legal issues, facts of the case, and statute names. |
| event_timeline | Judgments & FIR | 725 | CC0-1.0 | Based on the input text, which contains a sequence of events, create brief event descriptions along with the dates and chronologically sort them. |
| combine_event_timeline | Judgments & FIR | 204 | CC0-1.0 | For extraction event timelines from very long texts, it is often split into chunks, and event time for each chunk is created independently. This task focuses on merging of such timelines and sorting them chronologically. |
| legalbench | [Legal bench](https://huggingface.co/datasets/nguha/legalbench) | 607 | Other | Training data for the legalbench was filtered to keep only the legal reasoning tasks. Tasks that solely focus on the statute recall were excluded. ORCA-style explanations of these MCQs were created using GPT4.|
| statute_ingredients | Statutes | 526 | CC0-1.0 | Definitions of the most popular sections of Indian central acts were used for the generation of the statute ingredients. e.g., Example: Section 420 IPC: "Whoever cheats and thereby dishonestly induces the person deceived to deliver any property to any person, or to make, alter or destroy the whole or any part of a valuable security, or anything which is signed or sealed, and which is capable of being converted into a valuable security, shall be punished with imprisonment of either description for a term which may extend to seven years, and shall also be liable to fine." The ingredients of this statute are "cheating", "dishonest inducement", "delivery of property to any person"," alteration of valuable security", "destruction of a valuable security", "anything which is signed", "anything which is sealed" and "capable of being converted into a valuable security"|
| summary_generation | SC headnotes | 700 | CC0-1.0 | Indian Supreme Court judgments from 1950 to 1994 are published with headnotes. These headnotes are the summaries of these judgments. A random sample of 300 judgments is taken, and the input text is the entire judgment text, and the output is the headnote. |
| legal_open_orca | [FLAN Dataset] | 8555 | MIT | [OpenORCA dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca/blob/main/1M-GPT4-Augmented.parquet) is ORCA style explanations of [Natural Instructions dataset](https://github.com/allenai/natural-instructions/tree/master/tasks). The Natural Instruction dataset (NI) is filtered for law-related datasets. This filtered NI data is matched against the 1M GPT4 openORCA dataset using tf-idf matching to get legal OpenORCA data. |
| contract_clause_generation | [Contract Clauses](https://huggingface.co/datasets/NebulaSense/Legal_Clause_Instructions)| 4557 | cc-by-nc-4.0 | This dataset has two tasks of generating a contract clause and modifying an existing contract clause as per instructions. Entire data is used as is. |
| legal_niv2_mcq | Natural Instructions data | 2000 | Apache 2.0 | Random sample of NI dataset MCQs filtered for law. This helps to teach a model to give concise answers, which is helpful during evaluation.|
| incomplete_instructions | Judgments | 1546 | CC0-1.0 | These are the examples where the given information is incomplete for completing the task. E.g. LLM prompt does not specify facts of the case and asks to generate arguments. In such cases, the response is to ask for the required information. Some examples are picked up from [nisaar/Articles_Constitution_3300_Instruction_Set](https://huggingface.co/datasets/nisaar/Articles_Constitution_3300_Instruction_Set) where the precedents text need to be recalled based on the names.|
| constitution_general_knowledge | Constitution Q&A | 933 | Apache 2.0 | General Knowldege question and answers about Constitution of India from [nisaar/Constitution_of_India](https://huggingface.co/datasets/nisaar/Constitution_of_India)|
The dataset is split into train and test. The size in the above table indicates train+test size.
-->
| Task Name | Task Description | Data Creation Methodology |
|--------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Issues Generation | Based on the case facts, create legal issues for a court. Legal issues are the key points on which the verdict needs to be delivered. | Judgment facts were then sent to gpt-3.5-turbo to generate legal issues. |
| Argument Generation | Based on the facts of a case, legal issues, and applicable statute names, generate arguments for a given party. Arguments for the petitioner and counterarguments for the respondent are provided. | Arguments for the petitioners were created using gpt-3.5-turbo, using judgment facts, generated issues, and relevant statutes as inputs. Counterarguments for defendants were created using these petitioners' arguments, legal issues, case facts, and relevant statutes. |
| Event Timeline | Extract important events and their dates from the input text descriptions of events and output a chronologically sorted event list with dates and a brief event description. | FIRs and judgment facts were used as input text descriptions. These were sent to gpt-3.5-turbo to create event descriptions and dates. |
| Combine Event Timelines | For extraction of event timelines from very long texts, it is often split into chunks, and the event timeline for each chunk is created independently, which are merged later. | Individually created timelines coming from the same judgment or FIR were merged using gpt-3.5-turbo. |
| Legalbench | [Legalbench data](https://huggingface.co/datasets/nguha/legalbench) contains multiple-choice questions from 162 different legal tasks. | Training data for the legalbench was filtered to keep only the legal reasoning tasks. ORCA-style explanations of these MCQs were created using GPT4 in a zero-shot setting. |
| Statute Ingredients | Break the input statute definition into the ingredients needed to apply the statute. | Definitions of the most popular sections of Indian central acts were used to generate the statute ingredients using gpt-3.5-turbo. |
| Summary Generation | Create a summary in judgment headnotes format using the input court judgment text | Indian Supreme Court judgments from 1950 to 1994 are published with headnotes, which are summaries of those judgments. |
| Legal Open ORCA | [OpenORCA dataset]((https://huggingface.co/datasets/Open-Orca/OpenOrca/blob/main/1M-GPT4-Augmented.parquet)) is an ORCA-style explanation of the Natural Instructions dataset. | The Natural Instruction dataset (NI) is filtered for law-related datasets. This filtered NI data is matched against the 1M GPT4 openORCA dataset using tf-idf matching to get legal OpenORCA data. |
| Contract Clause Generation | Generation of new contract clauses and modification of existing contract clauses as per the instructions. | [existing data](https://huggingface.co/datasets/NebulaSense/Legal_Clause_Instructions) |
| Legal NIv2 MCQ | [Natural Instructions v2 data](https://github.com/allenai/natural-instructions) consists of multiple-choice questions about diverse topics. We filtered this data for law-related questions. | A random sample of NI dataset MCQs filtered for law. This helps to teach a model to give concise answers, which is helpful during evaluation. |
| Constitution General Knowledge | [QA about various aspects of the Indian Constitution](https://huggingface.co/datasets/nisaar/Constitution_of_India) | A about various aspects of the Indian Constitution. | \url{https://huggingface.co/datasets/nisaar/Constitution_of_India} |
| Incomplete Instructions | These are the examples where the given information is incomplete for completing the task. In such cases, the response is to ask for the required information. | Randomly selected instructions belonging to each of these tasks above where the information is incomplete. E.g., the LLM prompt does not specify the facts of the case and asks to generate arguments. |
The table below shows the summary statistics of various task categories and licenses associated with each dataset.
| Task Category | train count | test count | Average input tokens | Average output tokens | License |
|--------------------------------|----------------------|---------------------|-------------------------------|--------------------------------|------------------|
| Issue Generation | 577 | 24 | 1376 | 161 | CC0-1.0 |
| Argument Generation | 1142 | 58 | 2381 | 943 | CC0-1.0 |
| Event Timeline | 676 | 49 | 3476 | 342 | CC0-1.0 |
| Combine Event Timeline | 195 | 9 | 883 | 772 | CC0-1.0 |
| legalbench | 580 | 27 | 229 | 218 | Other |
| Statute Ingredients | 499 | 27 | 402 | 111 | CC0-1.0 |
| Summary Generation | 686 | 14 | 7635 | 1413 | CC0-1.0 |
| Legal Open ORCA | 8142 | 413 | 449 | 91 | MIT |
| Contract Clause Generation | 4325 | 232 | 76 | 179 | cc-by-nc-4.0 |
| Legal NIv2 MCQ | 1891 | 109 | 408 | 9 | Apache 2.0 |
| Constitution General Knowledge | 889 | 44 | 36 | 85 | Apache 2.0 |
| Incomplete Instructions | 1464 | 82 | 97 | 81 | CC0-1.0 |
| General Alap | 112 | 6 | 31 | 25 | CC0-1.0 |
## Guiding Principles
1. **Focus on legal reasoning rather than legal recall**: Relying on LLMs to generate legal precedents and statute definitions is a bad idea. This is because LLMs are more likely to hallucinate and generate false content. Instead, precedents information and statutes should be retrieved from authentic sources, given as input to LLMs, and let LLMs do the legal reasoning. This is in line with what humans do.
2. **Use real-life situations**: Make the datasets as close as possible to real-life situations. Hence, we used descriptions from court judgments and police First Investigation Reports (FIR) so that the model learns the language used in such texts.
3. **Explain answers**: ORCA-style explanations of answers help teach models the reasons behind coming up with answers to multiple-choice questions. We created GPT4 to create explanations wherever possible and reused relevant legal datasets that provided such explanations.
4. **Use synthetically generated responses if needed**: Many times, the actual documents, like lawyers' pleadings, witness statements, medical reports, etc., are not available publicly. So, we have used synthetically generated responses from OpenAI models like GPT4 and gpt-3.5-turbo wherever needed.
- **Curated by:** [OpenNyAI team](https://opennyai.org/)
- **License:** Since this is a compilation of multiple datasets, please refer to each of the datasets for the license information.
## Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
1) Precedent Information is missing in the argument generation. Precedents are important elements to support generated arguments. It is important to fetch the right paragraphs from the precedents based on the situation at hand to build an argument. Since we did not have access to an automatic tool that can do this, we have excluded precedents information from generated arguments.
2) Facts in the judgments are worded much differently than what is available from statements, filings, and other reports that lawyers get as inputs. Since this information is not public, we had to rely on publicly available datasets.
| opennyaiorg/aalap_instruction_dataset | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:other",
"legal",
"region:us"
]
| 2023-11-09T10:55:00+00:00 | {"language": ["en"], "license": "other", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "tags": ["legal"], "dataset_info": {"features": [{"name": "input_text", "dtype": "string"}, {"name": "system_prompt", "dtype": "string"}, {"name": "user_prompt", "dtype": "string"}, {"name": "output_text", "dtype": "string"}, {"name": "task", "dtype": "string"}, {"name": "combined_input_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 188932482, "num_examples": 21178}, {"name": "test", "num_bytes": 8307879, "num_examples": 1094}], "download_size": 97051698, "dataset_size": 197240361}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2023-12-20T10:32:31+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-other #legal #region-us
| Aalap Instruction dataset
=========================
This dataset aims to build an AI Assistant for Legal and Paralegal functions in India (Aalap). The main objective behind creating Aalap was to train a small-sized LLM specializing in specific legal tasks focusing on legal reasoning. Hence, prioritizing which legal tasks to focus on was an important step.
We discussed with Legal practitioners which legal tasks they would want to be automated. Then, we selected the most common legal tasks for publicly available input datasets. Since the Indian judiciary operates mainly in English, all the datasets are currently in the English language.
Common Data Sources
-------------------
The following are the commonly used data sources for preparing the input instructions.
1. Judgments Facts: Case facts are typically written in a court judgment. Such facts from judgments texts were extracted using Opennyai's rhetorical roles model \cite{kalamkarcorpus} on randomly chosen Indian Supreme Court & high court judgments. These judgment facts were used as inputs for multiple tasks like argument generation, issues generation, and event timeline creation.
2. Judgments Statutes: Relevant Statutes of a judgment are the ones that were extracted from corresponding judgment text using Opennyai's Named Entity Recognition model \cite{kalamkar2022named}.
3. FIRs: Publicly available police First Investigation Reports from the states of Maharashtra and Delhi were used to collect descriptions of real-life events. These FIRs were used to create event timeline creation tasks.
Dataset Details
---------------
Task Name: Issues Generation, Task Description: Based on the case facts, create legal issues for a court. Legal issues are the key points on which the verdict needs to be delivered., Data Creation Methodology: Judgment facts were then sent to gpt-3.5-turbo to generate legal issues.
Task Name: Argument Generation, Task Description: Based on the facts of a case, legal issues, and applicable statute names, generate arguments for a given party. Arguments for the petitioner and counterarguments for the respondent are provided., Data Creation Methodology: Arguments for the petitioners were created using gpt-3.5-turbo, using judgment facts, generated issues, and relevant statutes as inputs. Counterarguments for defendants were created using these petitioners' arguments, legal issues, case facts, and relevant statutes.
Task Name: Event Timeline, Task Description: Extract important events and their dates from the input text descriptions of events and output a chronologically sorted event list with dates and a brief event description., Data Creation Methodology: FIRs and judgment facts were used as input text descriptions. These were sent to gpt-3.5-turbo to create event descriptions and dates.
Task Name: Combine Event Timelines, Task Description: For extraction of event timelines from very long texts, it is often split into chunks, and the event timeline for each chunk is created independently, which are merged later., Data Creation Methodology: Individually created timelines coming from the same judgment or FIR were merged using gpt-3.5-turbo.
Task Name: Legalbench, Task Description: Legalbench data contains multiple-choice questions from 162 different legal tasks., Data Creation Methodology: Training data for the legalbench was filtered to keep only the legal reasoning tasks. ORCA-style explanations of these MCQs were created using GPT4 in a zero-shot setting.
Task Name: Statute Ingredients, Task Description: Break the input statute definition into the ingredients needed to apply the statute., Data Creation Methodology: Definitions of the most popular sections of Indian central acts were used to generate the statute ingredients using gpt-3.5-turbo.
Task Name: Summary Generation, Task Description: Create a summary in judgment headnotes format using the input court judgment text, Data Creation Methodology: Indian Supreme Court judgments from 1950 to 1994 are published with headnotes, which are summaries of those judgments.
Task Name: Legal Open ORCA, Task Description: OpenORCA dataset) is an ORCA-style explanation of the Natural Instructions dataset., Data Creation Methodology: The Natural Instruction dataset (NI) is filtered for law-related datasets. This filtered NI data is matched against the 1M GPT4 openORCA dataset using tf-idf matching to get legal OpenORCA data.
Task Name: Contract Clause Generation, Task Description: Generation of new contract clauses and modification of existing contract clauses as per the instructions., Data Creation Methodology: existing data
Task Name: Legal NIv2 MCQ, Task Description: Natural Instructions v2 data consists of multiple-choice questions about diverse topics. We filtered this data for law-related questions., Data Creation Methodology: A random sample of NI dataset MCQs filtered for law. This helps to teach a model to give concise answers, which is helpful during evaluation.
Task Name: Constitution General Knowledge, Task Description: QA about various aspects of the Indian Constitution, Data Creation Methodology: A about various aspects of the Indian Constitution.
Task Name: Incomplete Instructions, Task Description: These are the examples where the given information is incomplete for completing the task. In such cases, the response is to ask for the required information., Data Creation Methodology: Randomly selected instructions belonging to each of these tasks above where the information is incomplete. E.g., the LLM prompt does not specify the facts of the case and asks to generate arguments.
The table below shows the summary statistics of various task categories and licenses associated with each dataset.
Guiding Principles
------------------
1. Focus on legal reasoning rather than legal recall: Relying on LLMs to generate legal precedents and statute definitions is a bad idea. This is because LLMs are more likely to hallucinate and generate false content. Instead, precedents information and statutes should be retrieved from authentic sources, given as input to LLMs, and let LLMs do the legal reasoning. This is in line with what humans do.
2. Use real-life situations: Make the datasets as close as possible to real-life situations. Hence, we used descriptions from court judgments and police First Investigation Reports (FIR) so that the model learns the language used in such texts.
3. Explain answers: ORCA-style explanations of answers help teach models the reasons behind coming up with answers to multiple-choice questions. We created GPT4 to create explanations wherever possible and reused relevant legal datasets that provided such explanations.
4. Use synthetically generated responses if needed: Many times, the actual documents, like lawyers' pleadings, witness statements, medical reports, etc., are not available publicly. So, we have used synthetically generated responses from OpenAI models like GPT4 and gpt-3.5-turbo wherever needed.
* Curated by: OpenNyAI team
* License: Since this is a compilation of multiple datasets, please refer to each of the datasets for the license information.
Limitations
-----------
1. Precedent Information is missing in the argument generation. Precedents are important elements to support generated arguments. It is important to fetch the right paragraphs from the precedents based on the situation at hand to build an argument. Since we did not have access to an automatic tool that can do this, we have excluded precedents information from generated arguments.
2. Facts in the judgments are worded much differently than what is available from statements, filings, and other reports that lawyers get as inputs. Since this information is not public, we had to rely on publicly available datasets.
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|
0b8b84fa9ac064ba5ea04b2055b8182d8ccb8648 |
# Dataset Card for Dataset Name
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[More Information Needed] | zeroman1318/daegu-ai-06 | [
"region:us"
]
| 2023-11-09T10:56:05+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-30T19:43:54+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Dataset Name
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"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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<!-- Provide the basic links for the dataset. -->
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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<!-- This section describes the people or systems who created the annotations. -->
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# Dataset Card for Dataset Name
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<!-- Provide the basic links for the dataset. -->
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862232d3600536b548bb73b5dace5f125037e758 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
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<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
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<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | Seokeunsoo/dataset_repository_name | [
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| 2023-11-09T11:10:12+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:10:12+00:00 | []
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dae84a0198719cc5f82281dc758abb7daf4aac78 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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<!-- Provide the basic links for the dataset. -->
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<!-- Address questions around how the dataset is intended to be used. -->
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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[More Information Needed]
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[More Information Needed]
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
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| 2023-11-09T11:10:39+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:10:39+00:00 | []
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8f591a02a6036a582bd5e72e93fb9cd059c4578e |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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<!-- Provide the basic links for the dataset. -->
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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[More Information Needed]
## Dataset Card Contact
[More Information Needed] | ej94/dataset_repository_name | [
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| 2023-11-09T11:11:21+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:15:09+00:00 | []
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8c65cbbc1d59c491ab2bdb6bd97d1720464b15f6 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | grang13lue/dataset_repository_name | [
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| 2023-11-09T11:13:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:13:16+00:00 | []
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[More Information Needed] | Seokeunsoo/md_bbiyong | [
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| 2023-11-09T11:16:48+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:20:23+00:00 | []
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# Dataset Card for Dataset Name
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| 2023-11-09T11:16:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:21:40+00:00 | []
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dd98f08e8b1ac2d146afc3894bee5d4d0d557a85 |
# Dataset Card for Dataset Name
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[More Information Needed] | ej94/md-daegu231109 | [
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| 2023-11-09T11:16:53+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:20:13+00:00 | []
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### Dataset Sources [optional]
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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|
e543599aa6697a7c642f1fd3608450315a826c51 | # Dataset Card for "dataset_2000_complexquestion_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_complexquestion_test | [
"region:us"
]
| 2023-11-09T11:17:02+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "sequence": "null"}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 889, "num_examples": 10}], "download_size": 3333, "dataset_size": 889}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T11:31:42+00:00 | []
| []
| TAGS
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More Information needed | [
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|
c9cdfc466e39707e3cbb1497c1eabe230987735e |
# Description
The airborne hyperspectral dataset was taken by Headwall Hyperspec-VNIR-C imaging sensor over agricultural and urban areas in Chikusei, Ibaraki, Japan, on July 29, 2014 between the times 9:56 to 10:53 UTC+9. The central point of the scene is located at coordinates: 36.294946N, 140.008380E. The hyperspectral dataset has 128 bands in the spectral range from 363 nm to 1018 nm. The scene consists of 2517x2335 pixels and the ground sampling distance was 2.5 m. Ground truth of 19 classes was collected via a field survey and visual inspection using high-resolution color images obtained by Canon EOS 5D Mark II together with the hyperspectral data. The hyperspectral data and ground truth were made available to the scientific community in the ENVI and MATLAB formats at http://park.itc.u-tokyo.ac.jp/sal/hyperdata. More details of the experiment are presented in the technical report within the dataset.
# Quick look
<figure>
<img src= "assets/Chikusei.jpg" alt="Chikusei" width="300" />
<figcaption>Bands visualization of the Chikusei dataset.</figcaption>
</figure>
# Credits
Originally downloaded from: https://naotoyokoya.com/Download.html
In order to use the datasets, please fulfill the following three requirements:
- Giving an acknowledgement as follows:
The authors gratefully acknowledge Space Application Laboratory, Department of Advanced Interdisciplinary Studies, the University of Tokyo for providing the hyperspectral data.
- Using the following license for hyperspectral data:
http://creativecommons.org/licenses/by/3.0/
- This dataset was made public by Dr. Naoto Yokoya and Prof. Akira Iwasaki from the University of Tokyo. Please cite:
In WORD:
```
N. Yokoya and A. Iwasaki, "Airborne hyperspectral data over Chikusei," Space Appl. Lab., Univ. Tokyo, Japan, Tech. Rep., May 2016.
```
In LaTex:
```
@techreport{NYokoya2016,
author = {N. Yokoya and A. Iwasaki},
title = {Airborne hyperspectral data over Chikusei},
institution = {Space Application Laboratory, University of Tokyo},
year = 2016,
address = {Japan},
month = {May},
year = 2016,
}
``` | danaroth/chikusei | [
"license:cc-by-3.0",
"region:us"
]
| 2023-11-09T11:28:23+00:00 | {"license": "cc-by-3.0"} | 2023-11-09T15:53:41+00:00 | []
| []
| TAGS
#license-cc-by-3.0 #region-us
|
# Description
The airborne hyperspectral dataset was taken by Headwall Hyperspec-VNIR-C imaging sensor over agricultural and urban areas in Chikusei, Ibaraki, Japan, on July 29, 2014 between the times 9:56 to 10:53 UTC+9. The central point of the scene is located at coordinates: 36.294946N, 140.008380E. The hyperspectral dataset has 128 bands in the spectral range from 363 nm to 1018 nm. The scene consists of 2517x2335 pixels and the ground sampling distance was 2.5 m. Ground truth of 19 classes was collected via a field survey and visual inspection using high-resolution color images obtained by Canon EOS 5D Mark II together with the hyperspectral data. The hyperspectral data and ground truth were made available to the scientific community in the ENVI and MATLAB formats at URL More details of the experiment are presented in the technical report within the dataset.
# Quick look
<figure>
<img src= "assets/URL" alt="Chikusei" width="300" />
<figcaption>Bands visualization of the Chikusei dataset.</figcaption>
</figure>
# Credits
Originally downloaded from: URL
In order to use the datasets, please fulfill the following three requirements:
- Giving an acknowledgement as follows:
The authors gratefully acknowledge Space Application Laboratory, Department of Advanced Interdisciplinary Studies, the University of Tokyo for providing the hyperspectral data.
- Using the following license for hyperspectral data:
URL
- This dataset was made public by Dr. Naoto Yokoya and Prof. Akira Iwasaki from the University of Tokyo. Please cite:
In WORD:
In LaTex:
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|
f7ba6998f7a8e4cda38bbbe84d7a669e3b9f3874 | # Dataset Card for "comprehension"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/comprehension | [
"region:us"
]
| 2023-11-09T11:31:05+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 2742115, "num_examples": 900}], "download_size": 1261593, "dataset_size": 2742115}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-09T11:31:08+00:00 | []
| []
| TAGS
#region-us
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56803bf4f646751f41e65b89327a408bf193d65c | # Dataset Card for "exams_dialy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/exams_dialy | [
"region:us"
]
| 2023-11-09T11:37:52+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "grade", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 856006, "num_examples": 2300}], "download_size": 382169, "dataset_size": 856006}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-09T12:17:39+00:00 | []
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| TAGS
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More Information needed | [
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a652133776d8a7ad2e24d5541825dd997f7473ac | # Dataset Card for "exams_lichsu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/exams_lichsu | [
"region:us"
]
| 2023-11-09T11:41:26+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "grade", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 2291100, "num_examples": 5350}], "download_size": 1044296, "dataset_size": 2291100}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-09T12:19:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "exams_lichsu"
More Information needed | [
"# Dataset Card for \"exams_lichsu\"\n\nMore Information needed"
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118b7d82b95055f8832addb4bce68c49cf45157c | # Dataset Card for "exams_sinhhoc"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/exams_sinhhoc | [
"region:us"
]
| 2023-11-09T11:43:06+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "grade", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 1181756, "num_examples": 3100}], "download_size": 527389, "dataset_size": 1181756}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-09T12:21:36+00:00 | []
| []
| TAGS
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| # Dataset Card for "exams_sinhhoc"
More Information needed | [
"# Dataset Card for \"exams_sinhhoc\"\n\nMore Information needed"
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|
dcc53874eb9a80414b76898429deb85fa6860e0a | # Dataset Card for "exams_vatli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | vlsp-2023-vllm/exams_vatli | [
"region:us"
]
| 2023-11-09T11:45:21+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "choices", "struct": [{"name": "label", "sequence": "string"}, {"name": "text", "sequence": "string"}]}, {"name": "answerKey", "dtype": "string"}, {"name": "metadata", "struct": [{"name": "grade", "dtype": "string"}, {"name": "subject", "dtype": "string"}]}], "splits": [{"name": "test", "num_bytes": 365835, "num_examples": 850}], "download_size": 0, "dataset_size": 365835}, "configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}]}]} | 2023-11-19T22:12:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "exams_vatli"
More Information needed | [
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ab40e3ba3506a7c334fbda17401706c202937358 | # XLM-R-BERTić dataset
## Composition and usage
This dataset contains 11.5 billion words of texts written in Croatian, Bosnian, Montenegrin and Serbian.
It is an extension of the [BERTić-data dataset](http://hdl.handle.net/11356/1426), a 8.4 billion-words collection used to pre-train the [BERTić model](https://huggingface.co/classla/bcms-bertic) ([paper](https://aclanthology.org/2021.bsnlp-1.5.pdf)). In this dataset there are two major additions: the MaCoCu HBS crawling collection, a collection of crawled news items, and the [mC4](https://huggingface.co/datasets/mc4) HBS dataset. The order of deduplication is as stated in the list of parts/splits:
* macocu_hbs
* hr_news
* mC4
* BERTić-data
* hrwac
* classla_hr
* cc100_hr
* riznica
* srwac
* classla_sr
* cc100_sr
* bswac
* classla_bs
* cnrwac
The dataset was deduplicated with `onion` on the basis of 5-tuples of words with duplicate threshold set to 90%.
The entire dataset can be downloaded and used as follows:
```python
import datasets
dict_of_datasets = datasets.load_dataset("classla/xlm-r-bertic-data")
full_dataset = datasets.concatenate_datasets([d for d in dict_of_datasets.values()])
```
A single split can be taken as well, but note that this means all the splits will be downloaded and generated, which can take a long time:
```python
import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica")
```
To circumvent this one option is using streaming:
```python
import datasets
riznica = datasets.load_dataset("classla/xlm-r-bertic-data", split="riznica", streaming=True)
for i in riznica.take(2):
print(i)
# Output:
# {'text': 'PRAGMATIČARI DOGMATI SANJARI'}
# {'text': 'Ivica Župan'}
```
Read more on streaming [here](https://huggingface.co/docs/datasets/stream). | classla/xlm-r-bertic-data | [
"size_categories:10B<n<100B",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-11-09T11:45:41+00:00 | {"license": "cc-by-sa-4.0", "size_categories": ["10B<n<100B"]} | 2023-12-18T14:02:27+00:00 | []
| []
| TAGS
#size_categories-10B<n<100B #license-cc-by-sa-4.0 #region-us
| # XLM-R-BERTić dataset
## Composition and usage
This dataset contains 11.5 billion words of texts written in Croatian, Bosnian, Montenegrin and Serbian.
It is an extension of the BERTić-data dataset, a 8.4 billion-words collection used to pre-train the BERTić model (paper). In this dataset there are two major additions: the MaCoCu HBS crawling collection, a collection of crawled news items, and the mC4 HBS dataset. The order of deduplication is as stated in the list of parts/splits:
* macocu_hbs
* hr_news
* mC4
* BERTić-data
* hrwac
* classla_hr
* cc100_hr
* riznica
* srwac
* classla_sr
* cc100_sr
* bswac
* classla_bs
* cnrwac
The dataset was deduplicated with 'onion' on the basis of 5-tuples of words with duplicate threshold set to 90%.
The entire dataset can be downloaded and used as follows:
A single split can be taken as well, but note that this means all the splits will be downloaded and generated, which can take a long time:
To circumvent this one option is using streaming:
Read more on streaming here. | [
"# XLM-R-BERTić dataset",
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|
db62897dc57deea60db75cb994a8bd5eb972353b | # Dataset Card for "dataset_2000_decompese_question_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | presencesw/dataset_2000_decompese_question_test | [
"region:us"
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| 2023-11-09T11:48:32+00:00 | {"dataset_info": {"features": [{"name": "entities", "sequence": "null"}, {"name": "triplets", "list": [{"name": "question", "dtype": "string"}]}, {"name": "answer", "dtype": "string"}, {"name": "complex_question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3405, "num_examples": 10}], "download_size": 4156, "dataset_size": 3405}} | 2023-11-09T12:40:59+00:00 | []
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More Information needed | [
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99ec661c0ba379968d667ce2af125601aca565a1 |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | namok21/dataset_repository_name | [
"region:us"
]
| 2023-11-09T11:52:01+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-09T11:53:40+00:00 | []
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### Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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APA:
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cfbfc6a2f28424107416b9872d659bca34d6c5ed |
# Malaya-Speech Speech-to-Text dataset
This dataset combined from semisupervised Google Speech-to-Text and private datasets.
Processing script https://github.com/mesolitica/malaya-speech/blob/master/pretrained-model/prepare-stt/prepare-malay-stt-train.ipynb
This repository is to centralize the dataset for https://malaya-speech.readthedocs.io/ | mesolitica/malaya-speech-malay-stt | [
"region:us"
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| 2023-11-09T11:52:29+00:00 | {"dataset_info": {"features": [{"name": "filename", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "Y", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53095303942.242, "num_examples": 1635599}], "download_size": 53282183764, "dataset_size": 53095303942.242}} | 2023-11-09T17:45:49+00:00 | []
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# Malaya-Speech Speech-to-Text dataset
This dataset combined from semisupervised Google Speech-to-Text and private datasets.
Processing script URL
This repository is to centralize the dataset for URL | [
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|
616e6bed3a9650bf52eaf0b5d705e422265745fe |
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | kinit-tomassako/ver_claimdetection_demo | [
"region:us"
]
| 2023-11-09T12:46:48+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data.csv"}]}]} | 2023-11-13T08:17:53+00:00 | []
| []
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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APA:
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a954054acf7449358e3fd27dde255ae01863a72f | # Dataset Card for "bw_spec_cls_80_22"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arieg/bw_spec_cls_80_22 | [
"region:us"
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| 2023-11-09T12:47:34+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "55076", "1": "55097", "2": "55100", "3": "55101", "4": "55102", "5": "55113", "6": "55119", "7": "55120", "8": "55121", "9": "55122", "10": "55123", "11": "55124", "12": "55149", "13": "55231", "14": "55232", "15": "55233", "16": "55234", "17": "55235", "18": "55236", "19": "55237", "20": "55238", "21": "55240", "22": "55241", "23": "55242", "24": "55285", "25": "55286", "26": "55287", "27": "55288", "28": "55289", "29": "55290", "30": "55291", "31": "55292", "32": "55293", "33": "55294", "34": "55295", "35": "55402", "36": "55430", "37": "55436", "38": "55437", "39": "55480", "40": "55481", "41": "55549", "42": "55572", "43": "55709", "44": "55710", "45": "55711", "46": "55712", "47": "55713", "48": "55714", "49": "55715", "50": "55716", "51": "55717", "52": "55718", "53": "55719", "54": "55783", "55": "55786", "56": "55807", "57": "55808", "58": "55809", "59": "55810", "60": "55811", "61": "55826", "62": "55827", "63": "55828", "64": "55830", "65": "55831", "66": "55832", "67": "55833", "68": "55900", "69": "56010", "70": "56015", "71": "56020", "72": "56028", "73": "56029", "74": "56030", "75": "56031", "76": "56033", "77": "56034", "78": "56036", "79": "56247"}}}}], "splits": [{"name": "train", "num_bytes": 88281430.4, "num_examples": 1600}, {"name": "test", "num_bytes": 22107725.0, "num_examples": 400}], "download_size": 110670044, "dataset_size": 110389155.4}} | 2023-11-09T12:47:52+00:00 | []
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"# Dataset Card for \"bw_spec_cls_80_22\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bw_spec_cls_80_22\"\n\nMore Information needed"
]
|
3ef433e3b6b5d1462470649414ee3814caf41cb0 |
# Dataset Card for Evaluation run of luffycodes/llama-shishya-7b-ep3-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/luffycodes/llama-shishya-7b-ep3-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [luffycodes/llama-shishya-7b-ep3-v1](https://huggingface.co/luffycodes/llama-shishya-7b-ep3-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T12:48:08.068028](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v1_public/blob/main/results_2023-11-09T12-48-08.068028.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.4594923428252717,
"acc_stderr": 0.03404628674654547,
"acc_norm": 0.46668909375227274,
"acc_norm_stderr": 0.03497039082366745,
"mc1": 0.204406364749082,
"mc1_stderr": 0.014117174337432616,
"mc2": 0.3089869590457097,
"mc2_stderr": 0.013843169413571187,
"em": 0.3115562080536913,
"em_stderr": 0.004742879599828378,
"f1": 0.3699653942953032,
"f1_stderr": 0.004671420668393907
},
"harness|arc:challenge|25": {
"acc": 0.45307167235494883,
"acc_stderr": 0.01454689205200563,
"acc_norm": 0.4803754266211604,
"acc_norm_stderr": 0.014600132075947092
},
"harness|hellaswag|10": {
"acc": 0.5934076877116112,
"acc_stderr": 0.00490193651154613,
"acc_norm": 0.7662816172077276,
"acc_norm_stderr": 0.004223302177263009
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.04560480215720684,
"acc_norm": 0.29,
"acc_norm_stderr": 0.04560480215720684
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4666666666666667,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.4666666666666667,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.4605263157894737,
"acc_stderr": 0.04056242252249034,
"acc_norm": 0.4605263157894737,
"acc_norm_stderr": 0.04056242252249034
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.45,
"acc_stderr": 0.049999999999999996,
"acc_norm": 0.45,
"acc_norm_stderr": 0.049999999999999996
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.49433962264150944,
"acc_stderr": 0.030770900763851302,
"acc_norm": 0.49433962264150944,
"acc_norm_stderr": 0.030770900763851302
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4583333333333333,
"acc_stderr": 0.04166666666666665,
"acc_norm": 0.4583333333333333,
"acc_norm_stderr": 0.04166666666666665
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_stderr": 0.03733626655383509,
"acc_norm": 0.3988439306358382,
"acc_norm_stderr": 0.03733626655383509
},
"harness|hendrycksTest-college_physics|5": {
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"acc_stderr": 0.04280105837364395,
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"acc_norm_stderr": 0.04280105837364395
},
"harness|hendrycksTest-computer_security|5": {
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"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm_stderr": 0.03196758697835362
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"acc_norm": 0.3333333333333333,
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},
"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm_stderr": 0.023636975996101806
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.33,
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.35960591133004927,
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-marketing|5": {
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},
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"acc_norm_stderr": 0.016562433867284176
},
"harness|hendrycksTest-moral_disputes|5": {
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"harness|hendrycksTest-moral_scenarios|5": {
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.5130718954248366,
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"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.5594855305466238,
"acc_norm_stderr": 0.02819640057419743
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"harness|hendrycksTest-prehistory|5": {
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"acc_stderr": 0.027767689606833932,
"acc_norm": 0.5308641975308642,
"acc_norm_stderr": 0.027767689606833932
},
"harness|hendrycksTest-professional_accounting|5": {
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"acc_norm_stderr": 0.011901895635786097
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.04782001791380063
},
"harness|hendrycksTest-security_studies|5": {
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"acc_stderr": 0.03196412734523272,
"acc_norm": 0.5265306122448979,
"acc_norm_stderr": 0.03196412734523272
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6119402985074627,
"acc_stderr": 0.034457899643627506,
"acc_norm": 0.6119402985074627,
"acc_norm_stderr": 0.034457899643627506
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252607,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252607
},
"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": 0.672514619883041,
"acc_stderr": 0.035993357714560276,
"acc_norm": 0.672514619883041,
"acc_norm_stderr": 0.035993357714560276
},
"harness|truthfulqa:mc|0": {
"mc1": 0.204406364749082,
"mc1_stderr": 0.014117174337432616,
"mc2": 0.3089869590457097,
"mc2_stderr": 0.013843169413571187
},
"harness|winogrande|5": {
"acc": 0.6945540647198106,
"acc_stderr": 0.012945038632552022
},
"harness|drop|3": {
"em": 0.3115562080536913,
"em_stderr": 0.004742879599828378,
"f1": 0.3699653942953032,
"f1_stderr": 0.004671420668393907
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### 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_luffycodes__llama-shishya-7b-ep3-v1 | [
"region:us"
]
| 2023-11-09T12:51:13+00:00 | {"pretty_name": "Evaluation run of luffycodes/llama-shishya-7b-ep3-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [luffycodes/llama-shishya-7b-ep3-v1](https://huggingface.co/luffycodes/llama-shishya-7b-ep3-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v1_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T12:48:08.068028](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v1_public/blob/main/results_2023-11-09T12-48-08.068028.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\": 0.4594923428252717,\n \"acc_stderr\": 0.03404628674654547,\n \"acc_norm\": 0.46668909375227274,\n \"acc_norm_stderr\": 0.03497039082366745,\n \"mc1\": 0.204406364749082,\n \"mc1_stderr\": 0.014117174337432616,\n \"mc2\": 0.3089869590457097,\n \"mc2_stderr\": 0.013843169413571187,\n \"em\": 0.3115562080536913,\n \"em_stderr\": 0.004742879599828378,\n \"f1\": 0.3699653942953032,\n \"f1_stderr\": 0.004671420668393907\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.45307167235494883,\n \"acc_stderr\": 0.01454689205200563,\n \"acc_norm\": 0.4803754266211604,\n \"acc_norm_stderr\": 0.014600132075947092\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5934076877116112,\n \"acc_stderr\": 0.00490193651154613,\n \"acc_norm\": 0.7662816172077276,\n \"acc_norm_stderr\": 0.004223302177263009\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4666666666666667,\n \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.4666666666666667,\n \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4605263157894737,\n \"acc_stderr\": 0.04056242252249034,\n \"acc_norm\": 0.4605263157894737,\n \"acc_norm_stderr\": 0.04056242252249034\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.049999999999999996,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.049999999999999996\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.49433962264150944,\n \"acc_stderr\": 0.030770900763851302,\n \"acc_norm\": 0.49433962264150944,\n \"acc_norm_stderr\": 0.030770900763851302\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4583333333333333,\n \"acc_stderr\": 0.04166666666666665,\n \"acc_norm\": 0.4583333333333333,\n \"acc_norm_stderr\": 0.04166666666666665\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.03733626655383509,\n \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.03733626655383509\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.39574468085106385,\n \"acc_stderr\": 0.03196758697835362,\n \"acc_norm\": 0.39574468085106385,\n \"acc_norm_stderr\": 0.03196758697835362\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.4206896551724138,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.4206896551724138,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.023636975996101806,\n \"acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.023636975996101806\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.1984126984126984,\n \"acc_stderr\": 0.03567016675276864,\n \"acc_norm\": 0.1984126984126984,\n \"acc_norm_stderr\": 0.03567016675276864\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.535483870967742,\n \"acc_stderr\": 0.02837228779796293,\n \"acc_norm\": 0.535483870967742,\n \"acc_norm_stderr\": 0.02837228779796293\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.35960591133004927,\n \"acc_stderr\": 0.03376458246509567,\n \"acc_norm\": 0.35960591133004927,\n \"acc_norm_stderr\": 0.03376458246509567\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.037425970438065864,\n \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.037425970438065864\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5454545454545454,\n \"acc_stderr\": 0.03547601494006937,\n \"acc_norm\": 0.5454545454545454,\n \"acc_norm_stderr\": 0.03547601494006937\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.6735751295336787,\n \"acc_stderr\": 0.033840286211432945,\n \"acc_norm\": 0.6735751295336787,\n \"acc_norm_stderr\": 0.033840286211432945\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.38974358974358975,\n \"acc_stderr\": 0.024726967886647078,\n \"acc_norm\": 0.38974358974358975,\n \"acc_norm_stderr\": 0.024726967886647078\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.24444444444444444,\n \"acc_stderr\": 0.02620276653465215,\n \"acc_norm\": 0.24444444444444444,\n \"acc_norm_stderr\": 0.02620276653465215\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.03196876989195778,\n \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.03196876989195778\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2913907284768212,\n \"acc_stderr\": 0.03710185726119995,\n \"acc_norm\": 0.2913907284768212,\n \"acc_norm_stderr\": 0.03710185726119995\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6440366972477064,\n \"acc_stderr\": 0.020528559278244214,\n \"acc_norm\": 0.6440366972477064,\n \"acc_norm_stderr\": 0.020528559278244214\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.2916666666666667,\n \"acc_stderr\": 0.03099866630456053,\n \"acc_norm\": 0.2916666666666667,\n \"acc_norm_stderr\": 0.03099866630456053\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6029411764705882,\n \"acc_stderr\": 0.0343413116471913,\n \"acc_norm\": 0.6029411764705882,\n \"acc_norm_stderr\": 0.0343413116471913\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.6455696202531646,\n \"acc_stderr\": 0.031137304297185815,\n \"acc_norm\": 0.6455696202531646,\n \"acc_norm_stderr\": 0.031137304297185815\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5426008968609866,\n \"acc_stderr\": 0.033435777055830646,\n \"acc_norm\": 0.5426008968609866,\n \"acc_norm_stderr\": 0.033435777055830646\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6363636363636364,\n \"acc_stderr\": 0.043913262867240704,\n \"acc_norm\": 0.6363636363636364,\n \"acc_norm_stderr\": 0.043913262867240704\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5092592592592593,\n \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.49693251533742333,\n \"acc_stderr\": 0.03928297078179663,\n \"acc_norm\": 0.49693251533742333,\n \"acc_norm_stderr\": 0.03928297078179663\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.23214285714285715,\n \"acc_stderr\": 0.04007341809755806,\n \"acc_norm\": 0.23214285714285715,\n \"acc_norm_stderr\": 0.04007341809755806\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6601941747572816,\n \"acc_stderr\": 0.04689765937278135,\n \"acc_norm\": 0.6601941747572816,\n \"acc_norm_stderr\": 0.04689765937278135\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7307692307692307,\n \"acc_stderr\": 0.029058588303748842,\n \"acc_norm\": 0.7307692307692307,\n \"acc_norm_stderr\": 0.029058588303748842\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6883780332056194,\n \"acc_stderr\": 0.016562433867284176,\n \"acc_norm\": 0.6883780332056194,\n \"acc_norm_stderr\": 0.016562433867284176\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.49421965317919075,\n \"acc_stderr\": 0.02691729617914911,\n \"acc_norm\": 0.49421965317919075,\n \"acc_norm_stderr\": 0.02691729617914911\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2536312849162011,\n \"acc_stderr\": 0.014551553659369922,\n \"acc_norm\": 0.2536312849162011,\n \"acc_norm_stderr\": 0.014551553659369922\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.5130718954248366,\n \"acc_stderr\": 0.028620130800700246,\n \"acc_norm\": 0.5130718954248366,\n \"acc_norm_stderr\": 0.028620130800700246\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5594855305466238,\n \"acc_stderr\": 0.02819640057419743,\n \"acc_norm\": 0.5594855305466238,\n \"acc_norm_stderr\": 0.02819640057419743\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.5308641975308642,\n \"acc_stderr\": 0.027767689606833932,\n \"acc_norm\": 0.5308641975308642,\n \"acc_norm_stderr\": 0.027767689606833932\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.34397163120567376,\n \"acc_stderr\": 0.028338017428611327,\n \"acc_norm\": 0.34397163120567376,\n \"acc_norm_stderr\": 0.028338017428611327\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.31877444589308995,\n \"acc_stderr\": 0.011901895635786097,\n \"acc_norm\": 0.31877444589308995,\n \"acc_norm_stderr\": 0.011901895635786097\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.4264705882352941,\n \"acc_stderr\": 0.030042615832714878,\n \"acc_norm\": 0.4264705882352941,\n \"acc_norm_stderr\": 0.030042615832714878\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.4493464052287582,\n \"acc_stderr\": 0.020123766528027266,\n \"acc_norm\": 0.4493464052287582,\n \"acc_norm_stderr\": 0.020123766528027266\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.4727272727272727,\n \"acc_stderr\": 0.04782001791380063,\n \"acc_norm\": 0.4727272727272727,\n \"acc_norm_stderr\": 0.04782001791380063\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.5265306122448979,\n \"acc_stderr\": 0.03196412734523272,\n \"acc_norm\": 0.5265306122448979,\n \"acc_norm_stderr\": 0.03196412734523272\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6119402985074627,\n \"acc_stderr\": 0.034457899643627506,\n \"acc_norm\": 0.6119402985074627,\n \"acc_norm_stderr\": 0.034457899643627506\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4036144578313253,\n \"acc_stderr\": 0.038194861407583984,\n \"acc_norm\": 0.4036144578313253,\n \"acc_norm_stderr\": 0.038194861407583984\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.672514619883041,\n \"acc_stderr\": 0.035993357714560276,\n \"acc_norm\": 0.672514619883041,\n \"acc_norm_stderr\": 0.035993357714560276\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.204406364749082,\n \"mc1_stderr\": 0.014117174337432616,\n \"mc2\": 0.3089869590457097,\n \"mc2_stderr\": 0.013843169413571187\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6945540647198106,\n \"acc_stderr\": 0.012945038632552022\n },\n \"harness|drop|3\": {\n \"em\": 0.3115562080536913,\n \"em_stderr\": 0.004742879599828378,\n \"f1\": 0.3699653942953032,\n \"f1_stderr\": 0.004671420668393907\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/luffycodes/llama-shishya-7b-ep3-v1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_09T12_48_08.068028", "path": ["**/details_harness|arc:challenge|25_2023-11-09T12-48-08.068028.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-09T12-48-08.068028.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_09T12_48_08.068028", "path": ["**/details_harness|drop|3_2023-11-09T12-48-08.068028.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T12-48-08.068028.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T12_48_08.068028", "path": ["**/details_harness|gsm8k|5_2023-11-09T12-48-08.068028.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-09T12-48-08.068028.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_09T12_48_08.068028", "path": ["**/details_harness|hellaswag|10_2023-11-09T12-48-08.068028.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-09T12-48-08.068028.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_09T12_48_08.068028", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-formal_logic|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-global_facts|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_biology|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_geography|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-09T12-48-08.068028.parquet", "**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-09T12-48-08.068028.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_09T12_48_08.068028", "path": ["results_2023-11-09T12-48-08.068028.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T12-48-08.068028.parquet"]}]}]} | 2023-11-09T12:52:16+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of luffycodes/llama-shishya-7b-ep3-v1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model luffycodes/llama-shishya-7b-ep3-v1 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T12:48:08.068028(note that their might be results for other tasks in 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 luffycodes/llama-shishya-7b-ep3-v1",
"## 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 luffycodes/llama-shishya-7b-ep3-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T12:48:08.068028(note that their might be results for other tasks in 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",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model luffycodes/llama-shishya-7b-ep3-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T12:48:08.068028(note that their might be results for other tasks in 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 luffycodes/llama-shishya-7b-ep3-v1## 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 luffycodes/llama-shishya-7b-ep3-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T12:48:08.068028(note that their might be results for other tasks in 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"
]
|
426038af1a39700b2366939133a5e4b3e2834dc6 |
# Dataset Card for Evaluation run of luffycodes/llama-shishya-7b-ep3-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/luffycodes/llama-shishya-7b-ep3-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 [luffycodes/llama-shishya-7b-ep3-v2](https://huggingface.co/luffycodes/llama-shishya-7b-ep3-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v2_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T12:57:06.707192](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v2_public/blob/main/results_2023-11-09T12-57-06.707192.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.43776292457137356,
"acc_stderr": 0.03405236312139111,
"acc_norm": 0.44440566326787106,
"acc_norm_stderr": 0.03497626520397757,
"mc1": 0.19583843329253367,
"mc1_stderr": 0.01389234436774209,
"mc2": 0.3016304809342682,
"mc2_stderr": 0.013699598037265183,
"em": 0.30557885906040266,
"em_stderr": 0.004717509363446725,
"f1": 0.36205327181208175,
"f1_stderr": 0.004656030495449622
},
"harness|arc:challenge|25": {
"acc": 0.44197952218430037,
"acc_stderr": 0.014512682523128345,
"acc_norm": 0.4735494880546075,
"acc_norm_stderr": 0.014590931358120172
},
"harness|hellaswag|10": {
"acc": 0.5865365465046803,
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"harness|gsm8k|5": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v2 | [
"region:us"
]
| 2023-11-09T13:00:12+00:00 | {"pretty_name": "Evaluation run of luffycodes/llama-shishya-7b-ep3-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [luffycodes/llama-shishya-7b-ep3-v2](https://huggingface.co/luffycodes/llama-shishya-7b-ep3-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v2_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T12:57:06.707192](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__llama-shishya-7b-ep3-v2_public/blob/main/results_2023-11-09T12-57-06.707192.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\": 0.43776292457137356,\n \"acc_stderr\": 0.03405236312139111,\n \"acc_norm\": 0.44440566326787106,\n \"acc_norm_stderr\": 0.03497626520397757,\n \"mc1\": 0.19583843329253367,\n \"mc1_stderr\": 0.01389234436774209,\n \"mc2\": 0.3016304809342682,\n \"mc2_stderr\": 0.013699598037265183,\n \"em\": 0.30557885906040266,\n \"em_stderr\": 0.004717509363446725,\n \"f1\": 0.36205327181208175,\n \"f1_stderr\": 0.004656030495449622\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.44197952218430037,\n \"acc_stderr\": 0.014512682523128345,\n \"acc_norm\": 0.4735494880546075,\n \"acc_norm_stderr\": 0.014590931358120172\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5865365465046803,\n \"acc_stderr\": 0.004914480534533716,\n \"acc_norm\": 0.7588129854610636,\n \"acc_norm_stderr\": 0.004269291950109927\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768081,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768081\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4222222222222222,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.4222222222222222,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4144736842105263,\n \"acc_stderr\": 0.040089737857792046,\n \"acc_norm\": 0.4144736842105263,\n \"acc_norm_stderr\": 0.040089737857792046\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.4716981132075472,\n \"acc_stderr\": 0.0307235352490061,\n \"acc_norm\": 0.4716981132075472,\n \"acc_norm_stderr\": 0.0307235352490061\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4236111111111111,\n \"acc_stderr\": 0.04132125019723369,\n \"acc_norm\": 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| TAGS
#region-us
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# Dataset Card for Evaluation run of luffycodes/llama-shishya-7b-ep3-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 luffycodes/llama-shishya-7b-ep3-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T12:57:06.707192(note that their might be results for other tasks in 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 luffycodes/llama-shishya-7b-ep3-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 luffycodes/llama-shishya-7b-ep3-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T12:57:06.707192(note that their might be results for other tasks in 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 Summary\n\nDataset automatically created during the evaluation run of model luffycodes/llama-shishya-7b-ep3-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of luffycodes/llama-shishya-7b-ep3-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 luffycodes/llama-shishya-7b-ep3-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T12:57:06.707192(note that their might be results for other tasks in 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"
]
|
d253280713ca9cded3861ee49787302fc511aabd |
# Dataset Card for PubLayNet
[](https://github.com/shunk031/huggingface-datasets_PubLayNet/actions/workflows/ci.yaml)
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://developer.ibm.com/exchanges/data/all/publaynet/
- **Repository:** https://github.com/shunk031/huggingface-datasets_PubLayNet
- **Paper (Preprint):** https://arxiv.org/abs/1908.07836
- **Paper (ICDAR2019):** https://ieeexplore.ieee.org/document/8977963
### Dataset Summary
PubLayNet is a dataset for document layout analysis. It contains images of research papers and articles and annotations for various elements in a page such as "text", "list", "figure" etc in these research paper images. The dataset was obtained by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```python
import datasets as ds
dataset = ds.load_dataset(
path="shunk031/PubLayNet",
decode_rle=True, # True if Run-length Encoding (RLE) is to be decoded and converted to binary mask.
)
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### 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
- [CDLA-Permissive](https://cdla.io/permissive-1-0/)
### Citation Information
```bibtex
@inproceedings{zhong2019publaynet,
title={Publaynet: largest dataset ever for document layout analysis},
author={Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno},
booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
pages={1015--1022},
year={2019},
organization={IEEE}
}
```
### Contributions
Thanks to [ibm-aur-nlp/PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) for creating this dataset.
| pytorch-layout-generation/PubLayNet | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"task_categories:image-to-text",
"task_categories:question-answering",
"task_categories:other",
"task_categories:multiple-choice",
"task_categories:token-classification",
"task_categories:tabular-to-text",
"task_categories:object-detection",
"task_categories:table-question-answering",
"task_categories:text-classification",
"task_categories:table-to-text",
"task_ids:multi-label-image-classification",
"task_ids:multi-class-image-classification",
"task_ids:semantic-segmentation",
"task_ids:image-captioning",
"task_ids:extractive-qa",
"task_ids:closed-domain-qa",
"task_ids:multiple-choice-qa",
"task_ids:named-entity-recognition",
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"language:en",
"license:cdla-permissive-1.0",
"graphic design",
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"region:us"
]
| 2023-11-09T13:02:05+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["en"], "license": ["cdla-permissive-1.0"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": ["original"], "task_categories": ["image-classification", "image-segmentation", "image-to-text", "question-answering", "other", "multiple-choice", "token-classification", "tabular-to-text", "object-detection", "table-question-answering", "text-classification", "table-to-text"], "task_ids": ["multi-label-image-classification", "multi-class-image-classification", "semantic-segmentation", "image-captioning", "extractive-qa", "closed-domain-qa", "multiple-choice-qa", "named-entity-recognition"], "pretty_name": "PubLayNet", "tags": ["graphic design", "layout-generation"]} | 2023-11-09T13:09:05+00:00 | [
"1908.07836"
]
| [
"en"
]
| TAGS
#task_categories-image-classification #task_categories-image-segmentation #task_categories-image-to-text #task_categories-question-answering #task_categories-other #task_categories-multiple-choice #task_categories-token-classification #task_categories-tabular-to-text #task_categories-object-detection #task_categories-table-question-answering #task_categories-text-classification #task_categories-table-to-text #task_ids-multi-label-image-classification #task_ids-multi-class-image-classification #task_ids-semantic-segmentation #task_ids-image-captioning #task_ids-extractive-qa #task_ids-closed-domain-qa #task_ids-multiple-choice-qa #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #source_datasets-original #language-English #license-cdla-permissive-1.0 #graphic design #layout-generation #arxiv-1908.07836 #region-us
|
# Dataset Card for PubLayNet
: URL
- Paper (ICDAR2019): URL
### Dataset Summary
PubLayNet is a dataset for document layout analysis. It contains images of research papers and articles and annotations for various elements in a page such as "text", "list", "figure" etc in these research paper images. The dataset was obtained by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central.
### 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
- CDLA-Permissive
### Contributions
Thanks to ibm-aur-nlp/PubLayNet for creating this dataset.
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"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper (Preprint): URL\n- Paper (ICDAR2019): URL",
"### Dataset Summary\n\nPubLayNet is a dataset for document layout analysis. It contains images of research papers and articles and annotations for various elements in a page such as \"text\", \"list\", \"figure\" etc in these research paper images. The dataset was obtained by automatically matching the XML representations and the content of over 1 million PDF articles that are publicly available on PubMed Central.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
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|
d28a029f35c4ff7f616df47b0edf54e6882395e6 | ## MIRACL Dataset
This dataset is a reformatted version of the original [MIRACL dataset](https://huggingface.co/datasets/miracl/miracl),
into the format expected for MTEB reranking tasks. | jinaai/miracl | [
"license:apache-2.0",
"region:us"
]
| 2023-11-09T13:04:10+00:00 | {"license": "apache-2.0"} | 2024-01-16T14:57:56+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| ## MIRACL Dataset
This dataset is a reformatted version of the original MIRACL dataset,
into the format expected for MTEB reranking tasks. | [
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|
25187027e2c4226a746b1a851fa782c7d37366fc | # Dataset Card for "bw_spec_cls_80_23"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arieg/bw_spec_cls_80_23 | [
"region:us"
]
| 2023-11-09T13:13:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "56248", "1": "56249", "2": "56273", "3": "56274", "4": "56275", "5": "56465", "6": "56466", "7": "56467", "8": "56468", "9": "56469", "10": "56470", "11": "56471", "12": "56472", "13": "56474", "14": "56493", "15": "56495", "16": "56496", "17": "56497", "18": "56498", "19": "56499", "20": "56516", "21": "56517", "22": "56518", "23": "56519", "24": "56520", "25": "56521", "26": "56639", "27": "56640", "28": "56641", "29": "56645", "30": "56646", "31": "56648", "32": "56649", "33": "56650", "34": "56651", "35": "56686", "36": "56687", "37": "56688", "38": "56689", "39": "56690", "40": "56691", "41": "56692", "42": "56693", "43": "56694", "44": "56695", "45": "56696", "46": "56795", "47": "56796", "48": "56797", "49": "56798", "50": "56799", "51": "56800", "52": "56801", "53": "56802", "54": "56803", "55": "56804", "56": "56805", "57": "56888", "58": "57164", "59": "57175", "60": "57176", "61": "57177", "62": "57178", "63": "57179", "64": "57180", "65": "57344", "66": "57360", "67": "57371", "68": "57417", "69": "57418", "70": "57440", "71": "57442", "72": "57500", "73": "57569", "74": "57626", "75": "57627", "76": "57628", "77": "57629", "78": "57630", "79": "57639"}}}}], "splits": [{"name": "train", "num_bytes": 89167510.4, "num_examples": 1600}, {"name": "test", "num_bytes": 22075775.0, "num_examples": 400}], "download_size": 110305776, "dataset_size": 111243285.4}} | 2023-11-09T13:13:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bw_spec_cls_80_23"
More Information needed | [
"# Dataset Card for \"bw_spec_cls_80_23\"\n\nMore Information needed"
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|
5587e43e015212d0c6702bc0f4b0f054da4ee6c0 | # Dataset Card for "alffa_wolof"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Isma/alffa_wolof | [
"task_categories:automatic-speech-recognition",
"language:wo",
"region:us"
]
| 2023-11-09T13:18:34+00:00 | {"language": ["wo"], "task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "audio", "sequence": "float32"}, {"name": "speaker_id", "dtype": "string"}, {"name": "transcription", "dtype": "string"}, {"name": "filename", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3879044515, "num_examples": 13998}, {"name": "dev", "num_bytes": 279984004, "num_examples": 1120}], "download_size": 2072416720, "dataset_size": 4159028519}} | 2023-11-09T13:31:25+00:00 | []
| [
"wo"
]
| TAGS
#task_categories-automatic-speech-recognition #language-Wolof #region-us
| # Dataset Card for "alffa_wolof"
More Information needed | [
"# Dataset Card for \"alffa_wolof\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #language-Wolof #region-us \n",
"# Dataset Card for \"alffa_wolof\"\n\nMore Information needed"
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|
fa30033ccfe0db8e84f7f2e8c18b165a08e75439 | # Dataset Card for "tsla_stock_price"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pavement/tsla_stock_price | [
"region:us"
]
| 2023-11-09T13:23:31+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": "start", "dtype": "string"}, {"name": "target", "sequence": "float64"}, {"name": "feat_static_cat", "sequence": "int64"}, {"name": "feat_dynamic_real", "dtype": "null"}, {"name": "item_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 371409, "num_examples": 3356}, {"name": "validation", "num_bytes": 371412, "num_examples": 3356}, {"name": "test", "num_bytes": 371412, "num_examples": 3356}], "download_size": 311298, "dataset_size": 1114233}} | 2023-11-09T13:47:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tsla_stock_price"
More Information needed | [
"# Dataset Card for \"tsla_stock_price\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
8c24676e0e8490b8dd6db990fc7edbdf60b59364 |
# Dataset Card for Evaluation run of luffycodes/vicuna-shishya-7b-ep3-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/luffycodes/vicuna-shishya-7b-ep3-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [luffycodes/vicuna-shishya-7b-ep3-v1](https://huggingface.co/luffycodes/vicuna-shishya-7b-ep3-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_luffycodes__vicuna-shishya-7b-ep3-v1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T13:24:49.230828](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-shishya-7b-ep3-v1_public/blob/main/results_2023-11-09T13-24-49.230828.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": 0.03399727784474729,
"acc_norm": 0.5041920928165192,
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"mc1_stderr": 0.015415241740237012,
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"mc2_stderr": 0.014739501986326583,
"em": 0.2950922818791946,
"em_stderr": 0.004670729426706436,
"f1": 0.3578932466442965,
"f1_stderr": 0.004607902070294773
},
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"acc": 0.5751842262497511,
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"acc_norm": 0.7635929097789285,
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},
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},
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},
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"acc_norm": 0.47,
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},
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},
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"acc_norm": 0.4861111111111111,
"acc_norm_stderr": 0.041795966175810016
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
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},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
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},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.16666666666666666,
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},
"harness|hendrycksTest-computer_security|5": {
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"harness|hendrycksTest-conceptual_physics|5": {
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},
"harness|hendrycksTest-econometrics|5": {
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"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.30158730158730157,
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},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.31746031746031744,
"acc_norm_stderr": 0.04163453031302859
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.35,
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"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm_stderr": 0.02837228779796293
},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.3891625615763547,
"acc_norm_stderr": 0.034304624161038716
},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.25555555555555554,
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_luffycodes__vicuna-shishya-7b-ep3-v1 | [
"region:us"
]
| 2023-11-09T13:27:53+00:00 | {"pretty_name": "Evaluation run of luffycodes/vicuna-shishya-7b-ep3-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [luffycodes/vicuna-shishya-7b-ep3-v1](https://huggingface.co/luffycodes/vicuna-shishya-7b-ep3-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_luffycodes__vicuna-shishya-7b-ep3-v1_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T13:24:49.230828](https://huggingface.co/datasets/open-llm-leaderboard/details_luffycodes__vicuna-shishya-7b-ep3-v1_public/blob/main/results_2023-11-09T13-24-49.230828.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\": 0.49601218028660454,\n \"acc_stderr\": 0.03399727784474729,\n \"acc_norm\": 0.5041920928165192,\n \"acc_norm_stderr\": 0.03492449912034474,\n \"mc1\": 0.2631578947368421,\n \"mc1_stderr\": 0.015415241740237012,\n \"mc2\": 0.4032424062517679,\n \"mc2_stderr\": 0.014739501986326583,\n \"em\": 0.2950922818791946,\n \"em_stderr\": 0.004670729426706436,\n \"f1\": 0.3578932466442965,\n \"f1_stderr\": 0.004607902070294773\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.439419795221843,\n \"acc_stderr\": 0.014503747823580129,\n \"acc_norm\": 0.4590443686006826,\n \"acc_norm_stderr\": 0.014562291073601234\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5751842262497511,\n \"acc_stderr\": 0.004933047726996794,\n \"acc_norm\": 0.7635929097789285,\n \"acc_norm_stderr\": 0.004240066898702511\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.04560480215720684,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.04560480215720684\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4740740740740741,\n \"acc_stderr\": 0.04313531696750574,\n \"acc_norm\": 0.4740740740740741,\n \"acc_norm_stderr\": 0.04313531696750574\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.4868421052631579,\n \"acc_stderr\": 0.04067533136309172,\n \"acc_norm\": 0.4868421052631579,\n \"acc_norm_stderr\": 0.04067533136309172\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.5169811320754717,\n \"acc_stderr\": 0.030755120364119905,\n \"acc_norm\": 0.5169811320754717,\n \"acc_norm_stderr\": 0.030755120364119905\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4861111111111111,\n \"acc_stderr\": 0.041795966175810016,\n \"acc_norm\": 0.4861111111111111,\n \"acc_norm_stderr\": 0.041795966175810016\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.47398843930635837,\n \"acc_stderr\": 0.03807301726504511,\n \"acc_norm\": 0.47398843930635837,\n \"acc_norm_stderr\": 0.03807301726504511\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.16666666666666666,\n \"acc_stderr\": 0.03708284662416544,\n \"acc_norm\": 0.16666666666666666,\n \"acc_norm_stderr\": 0.03708284662416544\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.4553191489361702,\n \"acc_stderr\": 0.03255525359340355,\n \"acc_norm\": 0.4553191489361702,\n \"acc_norm_stderr\": 0.03255525359340355\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.04266339443159393,\n \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.04266339443159393\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.45517241379310347,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.45517241379310347,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.023636975996101806,\n \"acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.023636975996101806\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n \"acc_stderr\": 0.04163453031302859,\n \"acc_norm\": 0.31746031746031744,\n \"acc_norm_stderr\": 0.04163453031302859\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.535483870967742,\n \"acc_stderr\": 0.02837228779796293,\n \"acc_norm\": 0.535483870967742,\n \"acc_norm_stderr\": 0.02837228779796293\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.3891625615763547,\n \"acc_stderr\": 0.034304624161038716,\n \"acc_norm\": 0.3891625615763547,\n \"acc_norm_stderr\": 0.034304624161038716\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6424242424242425,\n \"acc_stderr\": 0.037425970438065864,\n \"acc_norm\": 0.6424242424242425,\n \"acc_norm_stderr\": 0.037425970438065864\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5909090909090909,\n \"acc_stderr\": 0.03502975799413007,\n \"acc_norm\": 0.5909090909090909,\n \"acc_norm_stderr\": 0.03502975799413007\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.7305699481865285,\n \"acc_stderr\": 0.03201867122877794,\n \"acc_norm\": 0.7305699481865285,\n \"acc_norm_stderr\": 0.03201867122877794\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.4307692307692308,\n \"acc_stderr\": 0.02510682066053975,\n \"acc_norm\": 0.4307692307692308,\n \"acc_norm_stderr\": 0.02510682066053975\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.25555555555555554,\n \"acc_stderr\": 0.02659393910184408,\n \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.02659393910184408\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.4495798319327731,\n \"acc_stderr\": 0.03231293497137707,\n \"acc_norm\": 0.4495798319327731,\n \"acc_norm_stderr\": 0.03231293497137707\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2781456953642384,\n \"acc_stderr\": 0.03658603262763743,\n \"acc_norm\": 0.2781456953642384,\n \"acc_norm_stderr\": 0.03658603262763743\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.6862385321100918,\n \"acc_stderr\": 0.019894723341469116,\n \"acc_norm\": 0.6862385321100918,\n \"acc_norm_stderr\": 0.019894723341469116\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.3611111111111111,\n \"acc_stderr\": 0.032757734861009996,\n \"acc_norm\": 0.3611111111111111,\n \"acc_norm_stderr\": 0.032757734861009996\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.6813725490196079,\n \"acc_stderr\": 0.03270287181482081,\n \"acc_norm\": 0.6813725490196079,\n \"acc_norm_stderr\": 0.03270287181482081\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7341772151898734,\n \"acc_stderr\": 0.02875679962965834,\n \"acc_norm\": 0.7341772151898734,\n \"acc_norm_stderr\": 0.02875679962965834\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6188340807174888,\n \"acc_stderr\": 0.03259625118416827,\n \"acc_norm\": 0.6188340807174888,\n \"acc_norm_stderr\": 0.03259625118416827\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.6412213740458015,\n \"acc_stderr\": 0.04206739313864908,\n \"acc_norm\": 0.6412213740458015,\n \"acc_norm_stderr\": 0.04206739313864908\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6198347107438017,\n \"acc_stderr\": 0.04431324501968431,\n \"acc_norm\": 0.6198347107438017,\n 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["**/details_harness|hendrycksTest-security_studies|5_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["**/details_harness|winogrande|5_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-09T13-24-49.230828.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_09T13_24_49.230828", "path": ["results_2023-11-09T13-24-49.230828.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T13-24-49.230828.parquet"]}]}]} | 2023-11-09T13:28:58+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of luffycodes/vicuna-shishya-7b-ep3-v1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model luffycodes/vicuna-shishya-7b-ep3-v1 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T13:24:49.230828(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model luffycodes/vicuna-shishya-7b-ep3-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T13:24:49.230828(note that their might be results for other tasks in 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 luffycodes/vicuna-shishya-7b-ep3-v1## 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 luffycodes/vicuna-shishya-7b-ep3-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T13:24:49.230828(note that their might be results for other tasks in 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"
]
|
6b09a76965986ea0d1ad760a43edd4a8e970738b | # Dataset Card for "correct_addition"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | daspartho/correct_addition | [
"region:us"
]
| 2023-11-09T13:38:27+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "incorrect_statement", "dtype": "string"}, {"name": "correct_statement", "dtype": "string"}, {"name": "close_statement", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 131851, "num_examples": 2500}], "download_size": 73485, "dataset_size": 131851}} | 2023-11-10T16:23:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "correct_addition"
More Information needed | [
"# Dataset Card for \"correct_addition\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"correct_addition\"\n\nMore Information needed"
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6,
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"passage: TAGS\n#region-us \n# Dataset Card for \"correct_addition\"\n\nMore Information needed"
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|
dc99129929ebd57de666605f25fbb016707ce8c0 | # Dataset Card for "bw_spec_cls_80_24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arieg/bw_spec_cls_80_24 | [
"region:us"
]
| 2023-11-09T13:39:25+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "57640", "1": "57648", "2": "57658", "3": "57661", "4": "57662", "5": "57663", "6": "57665", "7": "57691", "8": "57697", "9": "57819", "10": "57820", "11": "57821", "12": "57822", "13": "57823", "14": "57936", "15": "57937", "16": "57938", "17": "57939", "18": "57943", "19": "57968", "20": "58052", "21": "58053", "22": "58054", "23": "58060", "24": "58061", "25": "58063", "26": "58068", "27": "58070", "28": "58115", "29": "58116", "30": "58117", "31": "58135", "32": "58140", "33": "58161", "34": "58162", "35": "58164", "36": "58166", "37": "58169", "38": "58170", "39": "58173", "40": "58174", "41": "58212", "42": "58213", "43": "58215", "44": "58221", "45": "58225", "46": "58341", "47": "58474", "48": "59078", "49": "59373", "50": "59374", "51": "59561", "52": "59653", "53": "59654", "54": "59656", "55": "59657", "56": "59658", "57": "59659", "58": "59660", "59": "59663", "60": "59664", "61": "59666", "62": "59667", "63": "59669", "64": "59671", "65": "59673", "66": "59675", "67": "59676", "68": "59677", "69": "59678", "70": "59679", "71": "59680", "72": "59681", "73": "59682", "74": "59683", "75": "59684", "76": "59685", "77": "59686", "78": "59687", "79": "59688"}}}}], "splits": [{"name": "train", "num_bytes": 87569851.2, "num_examples": 1600}, {"name": "test", "num_bytes": 22682287.0, "num_examples": 400}], "download_size": 113474750, "dataset_size": 110252138.2}} | 2023-11-09T13:39:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bw_spec_cls_80_24"
More Information needed | [
"# Dataset Card for \"bw_spec_cls_80_24\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bw_spec_cls_80_24\"\n\nMore Information needed"
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|
a9e304360675c69a75e8be3bff4b11e5ab0cdd24 | # Dataset Card for "simulator-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zhangyi617/simulator-dataset | [
"region:us"
]
| 2023-11-09T13:41:21+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 200544915.0, "num_examples": 300}], "download_size": 200335894, "dataset_size": 200544915.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T13:45:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "simulator-dataset"
More Information needed | [
"# Dataset Card for \"simulator-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"simulator-dataset\"\n\nMore Information needed"
]
| [
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]
|
8d8fbcf97db85d69e1c42e9276722e240186aecc | # Dataset Card for "tsla_stock_price_real"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pavement/tsla_stock_price_real | [
"region:us"
]
| 2023-11-09T13:47:25+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": "start", "dtype": "string"}, {"name": "target", "sequence": "float64"}, {"name": "feat_static_cat", "sequence": "int64"}, {"name": "feat_dynamic_real", "dtype": "null"}, {"name": "item_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 317713, "num_examples": 3356}, {"name": "validation", "num_bytes": 344561, "num_examples": 3356}, {"name": "test", "num_bytes": 371409, "num_examples": 3356}], "download_size": 320770, "dataset_size": 1033683}} | 2023-11-09T14:11:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tsla_stock_price_real"
More Information needed | [
"# Dataset Card for \"tsla_stock_price_real\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
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|
f1d5cdf0acacb245662b37416ff59b17219da728 | # Dataset Card for "math"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Yama/math | [
"region:us"
]
| 2023-11-09T13:49:12+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 355584, "num_examples": 1200}, {"name": "test", "num_bytes": 50010, "num_examples": 189}], "download_size": 0, "dataset_size": 405594}} | 2023-11-09T15:16:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "math"
More Information needed | [
"# Dataset Card for \"math\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"math\"\n\nMore Information needed"
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| [
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|
bec92184b96bad411595d87f4dc18812306ea315 |
# 法律法规 #
从国家法律法规数据库（https://flk.npc.gov.cn/） 下载的法律法规
解压后得到：
law_list.tsv 法律法规的信息列表
law_docs/ 目录下有五个文件夹，分别装有不同状态的法律法规。
status 1 有效 ，3 尚未生效 ，5 已修改（有对应的1），7 两种：【有关法律问题和重大问题的决定】或【修改、废止的决定】，9 已废止
txt_files/ 用脚本处理 status1 中的非扫描件，生成的txt文件，每一行是形式是
```
某法 第n章 第n条 法条内容
```
laws_vector_store/ 是FAISS向量数据库，embedding模型采用text2vec （https://huggingface.co/GanymedeNil/text2vec-large-chinese）
向量数据库的每一条数据是txt的一行（向量数据库的范围是所有txt_files）
| Jinsns/flk | [
"license:mit",
"region:us"
]
| 2023-11-09T13:55:30+00:00 | {"license": "mit"} | 2023-11-09T15:42:43+00:00 | []
| []
| TAGS
#license-mit #region-us
|
# 法律法规 #
从国家法律法规数据库（URL） 下载的法律法规
解压后得到：
law_list.tsv 法律法规的信息列表
law_docs/ 目录下有五个文件夹，分别装有不同状态的法律法规。
status 1 有效 ，3 尚未生效 ，5 已修改（有对应的1），7 两种：【有关法律问题和重大问题的决定】或【修改、废止的决定】，9 已废止
txt_files/ 用脚本处理 status1 中的非扫描件，生成的txt文件，每一行是形式是
laws_vector_store/ 是FAISS向量数据库，embedding模型采用text2vec （URL）
向量数据库的每一条数据是txt的一行（向量数据库的范围是所有txt_files）
| [
"# 法律法规 #\n从国家法律法规数据库（URL） 下载的法律法规\n\n解压后得到：\nlaw_list.tsv 法律法规的信息列表\n\nlaw_docs/ 目录下有五个文件夹，分别装有不同状态的法律法规。\n\nstatus 1 有效 ，3 尚未生效 ，5 已修改（有对应的1），7 两种：【有关法律问题和重大问题的决定】或【修改、废止的决定】，9 已废止\n\ntxt_files/ 用脚本处理 status1 中的非扫描件，生成的txt文件，每一行是形式是\n\n\nlaws_vector_store/ 是FAISS向量数据库，embedding模型采用text2vec （URL）\n\n向量数据库的每一条数据是txt的一行（向量数据库的范围是所有txt_files）"
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| [
"TAGS\n#license-mit #region-us \n",
"# 法律法规 #\n从国家法律法规数据库（URL） 下载的法律法规\n\n解压后得到：\nlaw_list.tsv 法律法规的信息列表\n\nlaw_docs/ 目录下有五个文件夹，分别装有不同状态的法律法规。\n\nstatus 1 有效 ，3 尚未生效 ，5 已修改（有对应的1），7 两种：【有关法律问题和重大问题的决定】或【修改、废止的决定】，9 已废止\n\ntxt_files/ 用脚本处理 status1 中的非扫描件，生成的txt文件，每一行是形式是\n\n\nlaws_vector_store/ 是FAISS向量数据库，embedding模型采用text2vec （URL）\n\n向量数据库的每一条数据是txt的一行（向量数据库的范围是所有txt_files）"
]
| [
11,
180
]
| [
"passage: TAGS\n#license-mit #region-us \n# 法律法规 #\n从国家法律法规数据库（URL） 下载的法律法规\n\n解压后得到：\nlaw_list.tsv 法律法规的信息列表\n\nlaw_docs/ 目录下有五个文件夹，分别装有不同状态的法律法规。\n\nstatus 1 有效 ，3 尚未生效 ，5 已修改（有对应的1），7 两种：【有关法律问题和重大问题的决定】或【修改、废止的决定】，9 已废止\n\ntxt_files/ 用脚本处理 status1 中的非扫描件，生成的txt文件，每一行是形式是\n\n\nlaws_vector_store/ 是FAISS向量数据库，embedding模型采用text2vec （URL）\n\n向量数据库的每一条数据是txt的一行（向量数据库的范围是所有txt_files）"
]
|
4af377b7a5ef68755c5b82b614e891ff4dc6c2e9 |
# Dataset Card for Evaluation run of willnguyen/lacda-2-7B-chat-v0.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/willnguyen/lacda-2-7B-chat-v0.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [willnguyen/lacda-2-7B-chat-v0.1](https://huggingface.co/willnguyen/lacda-2-7B-chat-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_willnguyen__lacda-2-7B-chat-v0.1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T13:53:53.211938](https://huggingface.co/datasets/open-llm-leaderboard/details_willnguyen__lacda-2-7B-chat-v0.1_public/blob/main/results_2023-11-09T13-53-53.211938.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.46065725811605934,
"acc_stderr": 0.034477280778802896,
"acc_norm": 0.4668080345369505,
"acc_norm_stderr": 0.035310968004727446,
"mc1": 0.3011015911872705,
"mc1_stderr": 0.016058999026100612,
"mc2": 0.4456721895962505,
"mc2_stderr": 0.014265726453599933,
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196460794,
"f1": 0.05649014261744978,
"f1_stderr": 0.0013342363586640303
},
"harness|arc:challenge|25": {
"acc": 0.4803754266211604,
"acc_stderr": 0.014600132075947087,
"acc_norm": 0.5307167235494881,
"acc_norm_stderr": 0.014583792546304038
},
"harness|hellaswag|10": {
"acc": 0.5796654052977495,
"acc_stderr": 0.0049260381977145225,
"acc_norm": 0.7757418840868353,
"acc_norm_stderr": 0.0041624039148053385
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.047609522856952365,
"acc_norm": 0.34,
"acc_norm_stderr": 0.047609522856952365
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.43703703703703706,
"acc_stderr": 0.04284958639753399,
"acc_norm": 0.43703703703703706,
"acc_norm_stderr": 0.04284958639753399
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.48026315789473684,
"acc_stderr": 0.040657710025626036,
"acc_norm": 0.48026315789473684,
"acc_norm_stderr": 0.040657710025626036
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.45660377358490567,
"acc_stderr": 0.030656748696739438,
"acc_norm": 0.45660377358490567,
"acc_norm_stderr": 0.030656748696739438
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.4375,
"acc_stderr": 0.04148415739394154,
"acc_norm": 0.4375,
"acc_norm_stderr": 0.04148415739394154
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm_stderr": 0.037724468575180255
},
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"acc_norm": 0.24509803921568626,
"acc_norm_stderr": 0.04280105837364395
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-conceptual_physics|5": {
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"acc_norm_stderr": 0.03196758697835362
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.2631578947368421,
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"harness|hendrycksTest-electrical_engineering|5": {
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"acc_norm_stderr": 0.02264421261552521
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"harness|hendrycksTest-formal_logic|5": {
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"acc_norm": 0.30158730158730157,
"acc_norm_stderr": 0.04104947269903394
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.38,
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"harness|hendrycksTest-high_school_biology|5": {
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"harness|hendrycksTest-high_school_chemistry|5": {
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"harness|hendrycksTest-high_school_computer_science|5": {
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"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_geography|5": {
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},
"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
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"acc_norm_stderr": 0.02520357177302833
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_norm": 0.2851851851851852,
"acc_norm_stderr": 0.027528599210340496
},
"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.46218487394957986,
"acc_norm_stderr": 0.032385469487589795
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"harness|hendrycksTest-high_school_physics|5": {
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"acc_norm": 0.31125827814569534,
"acc_norm_stderr": 0.03780445850526733
},
"harness|hendrycksTest-high_school_psychology|5": {
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"acc_stderr": 0.020847156641915977,
"acc_norm": 0.6165137614678899,
"acc_norm_stderr": 0.020847156641915977
},
"harness|hendrycksTest-high_school_statistics|5": {
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"acc_stderr": 0.028765111718046955,
"acc_norm": 0.23148148148148148,
"acc_norm_stderr": 0.028765111718046955
},
"harness|hendrycksTest-high_school_us_history|5": {
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"acc_norm": 0.5,
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},
"harness|hendrycksTest-high_school_world_history|5": {
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"acc_norm": 0.5780590717299579,
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"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-international_law|5": {
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"acc_stderr": 0.04431324501968431,
"acc_norm": 0.6198347107438017,
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},
"harness|hendrycksTest-jurisprudence|5": {
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"acc_stderr": 0.04820403072760628,
"acc_norm": 0.5370370370370371,
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"harness|hendrycksTest-logical_fallacies|5": {
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},
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"acc_norm": 0.375,
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},
"harness|hendrycksTest-management|5": {
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"acc_norm_stderr": 0.048467482539772386
},
"harness|hendrycksTest-marketing|5": {
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},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_norm": 0.6143039591315453,
"acc_norm_stderr": 0.017406476619212907
},
"harness|hendrycksTest-moral_disputes|5": {
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"acc_norm": 0.4884393063583815,
"acc_norm_stderr": 0.026911898686377913
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.23798882681564246,
"acc_norm_stderr": 0.014242630070574915
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.4673202614379085,
"acc_norm_stderr": 0.028568699752225875
},
"harness|hendrycksTest-philosophy|5": {
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"acc_norm": 0.5884244372990354,
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},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.4783950617283951,
"acc_norm_stderr": 0.027794760105008746
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.3475177304964539,
"acc_stderr": 0.028406627809590954,
"acc_norm": 0.3475177304964539,
"acc_norm_stderr": 0.028406627809590954
},
"harness|hendrycksTest-professional_law|5": {
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"acc_norm": 0.35723598435462844,
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},
"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.46122448979591835,
"acc_norm_stderr": 0.03191282052669277
},
"harness|hendrycksTest-sociology|5": {
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"acc_norm": 0.6069651741293532,
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},
"harness|hendrycksTest-us_foreign_policy|5": {
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"acc_norm": 0.64,
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.39759036144578314,
"acc_norm_stderr": 0.038099730845402184
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.672514619883041,
"acc_stderr": 0.03599335771456027,
"acc_norm": 0.672514619883041,
"acc_norm_stderr": 0.03599335771456027
},
"harness|truthfulqa:mc|0": {
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"mc1_stderr": 0.016058999026100612,
"mc2": 0.4456721895962505,
"mc2_stderr": 0.014265726453599933
},
"harness|winogrande|5": {
"acc": 0.7419100236779794,
"acc_stderr": 0.012298278833972397
},
"harness|drop|3": {
"em": 0.001363255033557047,
"em_stderr": 0.0003778609196460794,
"f1": 0.05649014261744978,
"f1_stderr": 0.0013342363586640303
},
"harness|gsm8k|5": {
"acc": 0.06292645943896892,
"acc_stderr": 0.0066887625815327395
}
}
```
### 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_willnguyen__lacda-2-7B-chat-v0.1 | [
"region:us"
]
| 2023-11-09T13:56:17+00:00 | {"pretty_name": "Evaluation run of willnguyen/lacda-2-7B-chat-v0.1", "dataset_summary": "Dataset automatically created during the evaluation run of model [willnguyen/lacda-2-7B-chat-v0.1](https://huggingface.co/willnguyen/lacda-2-7B-chat-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_willnguyen__lacda-2-7B-chat-v0.1_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T13:53:53.211938](https://huggingface.co/datasets/open-llm-leaderboard/details_willnguyen__lacda-2-7B-chat-v0.1_public/blob/main/results_2023-11-09T13-53-53.211938.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\": 0.46065725811605934,\n \"acc_stderr\": 0.034477280778802896,\n \"acc_norm\": 0.4668080345369505,\n \"acc_norm_stderr\": 0.035310968004727446,\n \"mc1\": 0.3011015911872705,\n \"mc1_stderr\": 0.016058999026100612,\n \"mc2\": 0.4456721895962505,\n \"mc2_stderr\": 0.014265726453599933,\n \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196460794,\n \"f1\": 0.05649014261744978,\n \"f1_stderr\": 0.0013342363586640303\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.4803754266211604,\n \"acc_stderr\": 0.014600132075947087,\n \"acc_norm\": 0.5307167235494881,\n \"acc_norm_stderr\": 0.014583792546304038\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5796654052977495,\n \"acc_stderr\": 0.0049260381977145225,\n \"acc_norm\": 0.7757418840868353,\n \"acc_norm_stderr\": 0.0041624039148053385\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.047609522856952365,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.047609522856952365\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.43703703703703706,\n \"acc_stderr\": 0.04284958639753399,\n \"acc_norm\": 0.43703703703703706,\n \"acc_norm_stderr\": 0.04284958639753399\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.48026315789473684,\n \"acc_stderr\": 0.040657710025626036,\n \"acc_norm\": 0.48026315789473684,\n \"acc_norm_stderr\": 0.040657710025626036\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\": 0.45660377358490567,\n \"acc_stderr\": 0.030656748696739438,\n \"acc_norm\": 0.45660377358490567,\n \"acc_norm_stderr\": 0.030656748696739438\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.4375,\n \"acc_stderr\": 0.04148415739394154,\n \"acc_norm\": 0.4375,\n \"acc_norm_stderr\": 0.04148415739394154\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4277456647398844,\n \"acc_stderr\": 0.037724468575180255,\n \"acc_norm\": 0.4277456647398844,\n \"acc_norm_stderr\": 0.037724468575180255\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.24509803921568626,\n \"acc_stderr\": 0.04280105837364395,\n \"acc_norm\": 0.24509803921568626,\n \"acc_norm_stderr\": 0.04280105837364395\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.39574468085106385,\n \"acc_stderr\": 0.03196758697835362,\n \"acc_norm\": 0.39574468085106385,\n \"acc_norm_stderr\": 0.03196758697835362\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2631578947368421,\n \"acc_stderr\": 0.041424397194893624,\n \"acc_norm\": 0.2631578947368421,\n \"acc_norm_stderr\": 0.041424397194893624\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.041618085035015295,\n \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.041618085035015295\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2619047619047619,\n \"acc_stderr\": 0.02264421261552521,\n \"acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.02264421261552521\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30158730158730157,\n \"acc_stderr\": 0.04104947269903394,\n \"acc_norm\": 0.30158730158730157,\n \"acc_norm_stderr\": 0.04104947269903394\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.4645161290322581,\n \"acc_stderr\": 0.028372287797962956,\n \"acc_norm\": 0.4645161290322581,\n \"acc_norm_stderr\": 0.028372287797962956\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.35467980295566504,\n \"acc_stderr\": 0.0336612448905145,\n \"acc_norm\": 0.35467980295566504,\n \"acc_norm_stderr\": 0.0336612448905145\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.6303030303030303,\n \"acc_stderr\": 0.03769430314512566,\n \"acc_norm\": 0.6303030303030303,\n \"acc_norm_stderr\": 0.03769430314512566\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.035623524993954825,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.035623524993954825\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\": 0.4461538461538462,\n \"acc_stderr\": 0.02520357177302833,\n \"acc_norm\": 0.4461538461538462,\n \"acc_norm_stderr\": 0.02520357177302833\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.2851851851851852,\n \"acc_stderr\": 0.027528599210340496,\n \"acc_norm\": 0.2851851851851852,\n \"acc_norm_stderr\": 0.027528599210340496\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.46218487394957986,\n \"acc_stderr\": 0.032385469487589795,\n \"acc_norm\": 0.46218487394957986,\n \"acc_norm_stderr\": 0.032385469487589795\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\": 0.6165137614678899,\n \"acc_stderr\": 0.020847156641915977,\n \"acc_norm\": 0.6165137614678899,\n \"acc_norm_stderr\": 0.020847156641915977\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.23148148148148148,\n \"acc_stderr\": 0.028765111718046955,\n \"acc_norm\": 0.23148148148148148,\n \"acc_norm_stderr\": 0.028765111718046955\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.5,\n \"acc_stderr\": 0.03509312031717982,\n \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.03509312031717982\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.5780590717299579,\n \"acc_stderr\": 0.032148146302403695,\n \"acc_norm\": 0.5780590717299579,\n \"acc_norm_stderr\": 0.032148146302403695\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5560538116591929,\n \"acc_stderr\": 0.03334625674242728,\n \"acc_norm\": 0.5560538116591929,\n \"acc_norm_stderr\": 0.03334625674242728\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.5725190839694656,\n \"acc_stderr\": 0.04338920305792401,\n \"acc_norm\": 0.5725190839694656,\n \"acc_norm_stderr\": 0.04338920305792401\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.6198347107438017,\n \"acc_stderr\": 0.04431324501968431,\n \"acc_norm\": 0.6198347107438017,\n \"acc_norm_stderr\": 0.04431324501968431\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.5370370370370371,\n \"acc_stderr\": 0.04820403072760628,\n \"acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.04820403072760628\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.49079754601226994,\n \"acc_stderr\": 0.039277056007874414,\n \"acc_norm\": 0.49079754601226994,\n \"acc_norm_stderr\": 0.039277056007874414\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.375,\n \"acc_stderr\": 0.04595091388086298,\n \"acc_norm\": 0.375,\n \"acc_norm_stderr\": 0.04595091388086298\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.6019417475728155,\n \"acc_stderr\": 0.048467482539772386,\n \"acc_norm\": 0.6019417475728155,\n \"acc_norm_stderr\": 0.048467482539772386\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.6709401709401709,\n \"acc_stderr\": 0.030782321577688173,\n \"acc_norm\": 0.6709401709401709,\n \"acc_norm_stderr\": 0.030782321577688173\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6143039591315453,\n \"acc_stderr\": 0.017406476619212907,\n \"acc_norm\": 0.6143039591315453,\n \"acc_norm_stderr\": 0.017406476619212907\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.4884393063583815,\n \"acc_stderr\": 0.026911898686377913,\n \"acc_norm\": 0.4884393063583815,\n \"acc_norm_stderr\": 0.026911898686377913\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.4673202614379085,\n \"acc_stderr\": 0.028568699752225875,\n \"acc_norm\": 0.4673202614379085,\n \"acc_norm_stderr\": 0.028568699752225875\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5884244372990354,\n \"acc_stderr\": 0.02795048149440127,\n \"acc_norm\": 0.5884244372990354,\n \"acc_norm_stderr\": 0.02795048149440127\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.4783950617283951,\n \"acc_stderr\": 0.027794760105008746,\n \"acc_norm\": 0.4783950617283951,\n \"acc_norm_stderr\": 0.027794760105008746\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.3475177304964539,\n \"acc_stderr\": 0.028406627809590954,\n \"acc_norm\": 0.3475177304964539,\n \"acc_norm_stderr\": 0.028406627809590954\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.35723598435462844,\n \"acc_stderr\": 0.012238615750316503,\n \"acc_norm\": 0.35723598435462844,\n \"acc_norm_stderr\": 0.012238615750316503\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.49264705882352944,\n \"acc_stderr\": 0.030369552523902173,\n \"acc_norm\": 0.49264705882352944,\n \"acc_norm_stderr\": 0.030369552523902173\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.43137254901960786,\n \"acc_stderr\": 0.020036393768352638,\n \"acc_norm\": 0.43137254901960786,\n \"acc_norm_stderr\": 0.020036393768352638\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5818181818181818,\n \"acc_stderr\": 0.04724577405731572,\n \"acc_norm\": 0.5818181818181818,\n \"acc_norm_stderr\": 0.04724577405731572\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.46122448979591835,\n \"acc_stderr\": 0.03191282052669277,\n \"acc_norm\": 0.46122448979591835,\n \"acc_norm_stderr\": 0.03191282052669277\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6069651741293532,\n \"acc_stderr\": 0.0345368246603156,\n \"acc_norm\": 0.6069651741293532,\n \"acc_norm_stderr\": 0.0345368246603156\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.64,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.64,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.39759036144578314,\n \"acc_stderr\": 0.038099730845402184,\n \"acc_norm\": 0.39759036144578314,\n \"acc_norm_stderr\": 0.038099730845402184\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.672514619883041,\n \"acc_stderr\": 0.03599335771456027,\n \"acc_norm\": 0.672514619883041,\n \"acc_norm_stderr\": 0.03599335771456027\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3011015911872705,\n \"mc1_stderr\": 0.016058999026100612,\n \"mc2\": 0.4456721895962505,\n \"mc2_stderr\": 0.014265726453599933\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7419100236779794,\n \"acc_stderr\": 0.012298278833972397\n },\n \"harness|drop|3\": {\n \"em\": 0.001363255033557047,\n \"em_stderr\": 0.0003778609196460794,\n \"f1\": 0.05649014261744978,\n \"f1_stderr\": 0.0013342363586640303\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.06292645943896892,\n \"acc_stderr\": 0.0066887625815327395\n }\n}\n```", "repo_url": "https://huggingface.co/willnguyen/lacda-2-7B-chat-v0.1", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_09T13_53_53.211938", "path": ["**/details_harness|arc:challenge|25_2023-11-09T13-53-53.211938.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-09T13-53-53.211938.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_09T13_53_53.211938", "path": ["**/details_harness|drop|3_2023-11-09T13-53-53.211938.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T13-53-53.211938.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T13_53_53.211938", "path": ["**/details_harness|gsm8k|5_2023-11-09T13-53-53.211938.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-09T13-53-53.211938.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_09T13_53_53.211938", "path": ["**/details_harness|hellaswag|10_2023-11-09T13-53-53.211938.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-09T13-53-53.211938.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_09T13_53_53.211938", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-09T13-53-53.211938.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-09T13-53-53.211938.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_09T13_53_53.211938", "path": ["results_2023-11-09T13-53-53.211938.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T13-53-53.211938.parquet"]}]}]} | 2023-11-09T13:57:18+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of willnguyen/lacda-2-7B-chat-v0.1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model willnguyen/lacda-2-7B-chat-v0.1 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T13:53:53.211938(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model willnguyen/lacda-2-7B-chat-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
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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 willnguyen/lacda-2-7B-chat-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T13:53:53.211938(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of willnguyen/lacda-2-7B-chat-v0.1## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model willnguyen/lacda-2-7B-chat-v0.1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T13:53:53.211938(note that their might be results for other tasks in 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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|
5367ec3936d0489b45492d964e9caad36511befc | # Dataset Card for "bw_spec_cls_80_25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arieg/bw_spec_cls_80_25 | [
"region:us"
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| 2023-11-09T14:05:15+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "59702", "1": "59706", "2": "59707", "3": "59708", "4": "59709", "5": "59710", "6": "59719", "7": "59720", "8": "59721", "9": "59723", "10": "59724", "11": "59725", "12": "59726", "13": "59727", "14": "59823", "15": "59876", "16": "59930", "17": "60037", "18": "60038", "19": "60041", "20": "60042", "21": "60045", "22": "60048", "23": "60074", "24": "60143", "25": "60144", "26": "60145", "27": "60146", "28": "60170", "29": "60317", "30": "60472", "31": "60474", "32": "60477", "33": "60478", "34": "60510", "35": "60544", "36": "60547", "37": "60548", "38": "60549", "39": "60736", "40": "60753", "41": "60754", "42": "60755", "43": "60756", "44": "60757", "45": "60758", "46": "60775", "47": "60776", "48": "60777", "49": "60857", "50": "60864", "51": "60865", "52": "60994", "53": "61006", "54": "61007", "55": "61008", "56": "61010", "57": "61011", "58": "61012", "59": "61013", "60": "61159", "61": "61160", "62": "61161", "63": "61172", "64": "61174", "65": "61175", "66": "61452", "67": "61453", "68": "61491", "69": "61492", "70": "61493", "71": "61587", "72": "61589", "73": "61591", "74": "61592", "75": "61668", "76": "61670", "77": "61679", "78": "61814", "79": "61884"}}}}], "splits": [{"name": "train", "num_bytes": 93110896.0, "num_examples": 1600}, {"name": "test", "num_bytes": 22653803.0, "num_examples": 400}], "download_size": 113211430, "dataset_size": 115764699.0}} | 2023-11-09T14:05:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bw_spec_cls_80_25"
More Information needed | [
"# Dataset Card for \"bw_spec_cls_80_25\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bw_spec_cls_80_25\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bw_spec_cls_80_25\"\n\nMore Information needed"
]
|
ec077ba686d2bb5cbe4eb06bc10185e2bb8c1c3c |
# Dataset Card for Evaluation run of Weyaxi/Dolphin2.1-OpenOrca-7B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Weyaxi/Dolphin2.1-OpenOrca-7B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [Weyaxi/Dolphin2.1-OpenOrca-7B](https://huggingface.co/Weyaxi/Dolphin2.1-OpenOrca-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Weyaxi__Dolphin2.1-OpenOrca-7B_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T14:21:27.933712](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Dolphin2.1-OpenOrca-7B_public/blob/main/results_2023-11-09T14-21-27.933712.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": 0.032466479047476085,
"acc_norm": 0.6308724361156662,
"acc_norm_stderr": 0.033159611933737225,
"mc1": 0.36107711138310894,
"mc1_stderr": 0.016814312844836886,
"mc2": 0.538254375639854,
"mc2_stderr": 0.015244755693358225,
"em": 0.0030411073825503355,
"em_stderr": 0.0005638896908753155,
"f1": 0.08151740771812048,
"f1_stderr": 0.0016591952257614033
},
"harness|arc:challenge|25": {
"acc": 0.6083617747440273,
"acc_stderr": 0.01426412212493821,
"acc_norm": 0.6416382252559727,
"acc_norm_stderr": 0.014012883334859857
},
"harness|hellaswag|10": {
"acc": 0.650368452499502,
"acc_stderr": 0.004758790172436687,
"acc_norm": 0.8424616610237005,
"acc_norm_stderr": 0.0036356303524759065
},
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"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm_stderr": 0.04232073695151589
},
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},
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"acc_norm_stderr": 0.04943110704237102
},
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},
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},
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},
"harness|gsm8k|5": {
"acc": 0.19711902956785443,
"acc_stderr": 0.01095802163030062
}
}
```
### 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_Weyaxi__Dolphin2.1-OpenOrca-7B | [
"region:us"
]
| 2023-11-09T14:16:24+00:00 | {"pretty_name": "Evaluation run of Weyaxi/Dolphin2.1-OpenOrca-7B", "dataset_summary": "Dataset automatically created during the evaluation run of model [Weyaxi/Dolphin2.1-OpenOrca-7B](https://huggingface.co/Weyaxi/Dolphin2.1-OpenOrca-7B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Weyaxi__Dolphin2.1-OpenOrca-7B_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T14:21:27.933712](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Dolphin2.1-OpenOrca-7B_public/blob/main/results_2023-11-09T14-21-27.933712.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\": 0.6224567348896717,\n \"acc_stderr\": 0.032466479047476085,\n \"acc_norm\": 0.6308724361156662,\n \"acc_norm_stderr\": 0.033159611933737225,\n \"mc1\": 0.36107711138310894,\n \"mc1_stderr\": 0.016814312844836886,\n \"mc2\": 0.538254375639854,\n \"mc2_stderr\": 0.015244755693358225,\n \"em\": 0.0030411073825503355,\n \"em_stderr\": 0.0005638896908753155,\n \"f1\": 0.08151740771812048,\n \"f1_stderr\": 0.0016591952257614033\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.01426412212493821,\n \"acc_norm\": 0.6416382252559727,\n \"acc_norm_stderr\": 0.014012883334859857\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.650368452499502,\n \"acc_stderr\": 0.004758790172436687,\n \"acc_norm\": 0.8424616610237005,\n \"acc_norm_stderr\": 0.0036356303524759065\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6,\n \"acc_stderr\": 0.04232073695151589,\n \"acc_norm\": 0.6,\n \"acc_norm_stderr\": 0.04232073695151589\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6710526315789473,\n \"acc_stderr\": 0.03823428969926605,\n \"acc_norm\": 0.6710526315789473,\n \"acc_norm_stderr\": 0.03823428969926605\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.037455547914624555,\n \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.037455547914624555\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201943,\n \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201943\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5531914893617021,\n \"acc_stderr\": 0.0325005368436584,\n \"acc_norm\": 0.5531914893617021,\n \"acc_norm_stderr\": 0.0325005368436584\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4298245614035088,\n \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.4298245614035088,\n \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3968253968253968,\n \"acc_stderr\": 0.025197101074246487,\n \"acc_norm\": 0.3968253968253968,\n \"acc_norm_stderr\": 0.025197101074246487\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.03374402644139403,\n \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.03374402644139403\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7727272727272727,\n \"acc_stderr\": 0.029857515673386414,\n \"acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.029857515673386414\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6333333333333333,\n \"acc_stderr\": 0.02443301646605246,\n \"acc_norm\": 0.6333333333333333,\n \"acc_norm_stderr\": 0.02443301646605246\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6596638655462185,\n \"acc_stderr\": 0.03077805742293167,\n \"acc_norm\": 0.6596638655462185,\n \"acc_norm_stderr\": 0.03077805742293167\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8128440366972477,\n \"acc_stderr\": 0.016722684526200148,\n \"acc_norm\": 0.8128440366972477,\n \"acc_norm_stderr\": 0.016722684526200148\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.02812597226565437,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565437\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601436,\n \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601436\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6681614349775785,\n \"acc_stderr\": 0.03160295143776679,\n \"acc_norm\": 0.6681614349775785,\n \"acc_norm_stderr\": 0.03160295143776679\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.039166677628225836,\n \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.039166677628225836\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n \"acc_stderr\": 0.02220930907316561,\n \"acc_norm\": 0.8675213675213675,\n \"acc_norm_stderr\": 0.02220930907316561\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8058748403575989,\n \"acc_stderr\": 0.014143970276657567,\n \"acc_norm\": 0.8058748403575989,\n \"acc_norm_stderr\": 0.014143970276657567\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.024405173935783234,\n \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.024405173935783234\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3340782122905028,\n \"acc_stderr\": 0.015774911422381625,\n \"acc_norm\": 0.3340782122905028,\n \"acc_norm_stderr\": 0.015774911422381625\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.6993464052287581,\n \"acc_stderr\": 0.026256053835718964,\n \"acc_norm\": 0.6993464052287581,\n \"acc_norm_stderr\": 0.026256053835718964\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.684887459807074,\n \"acc_stderr\": 0.02638527370346449,\n \"acc_norm\": 0.684887459807074,\n \"acc_norm_stderr\": 0.02638527370346449\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7191358024691358,\n \"acc_stderr\": 0.02500646975579921,\n \"acc_norm\": 0.7191358024691358,\n \"acc_norm_stderr\": 0.02500646975579921\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666904,\n \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666904\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4621903520208605,\n \"acc_stderr\": 0.012733671880342507,\n \"acc_norm\": 0.4621903520208605,\n \"acc_norm_stderr\": 0.012733671880342507\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.029520095697687765,\n \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.029520095697687765\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6470588235294118,\n \"acc_stderr\": 0.01933314202079716,\n \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.01933314202079716\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.7183673469387755,\n \"acc_stderr\": 0.028795185574291296,\n \"acc_norm\": 0.7183673469387755,\n \"acc_norm_stderr\": 0.028795185574291296\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.845771144278607,\n \"acc_stderr\": 0.025538433368578337,\n \"acc_norm\": 0.845771144278607,\n \"acc_norm_stderr\": 0.025538433368578337\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.83,\n \"acc_stderr\": 0.0377525168068637,\n \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.0377525168068637\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5421686746987951,\n \"acc_stderr\": 0.0387862677100236,\n \"acc_norm\": 0.5421686746987951,\n \"acc_norm_stderr\": 0.0387862677100236\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.36107711138310894,\n \"mc1_stderr\": 0.016814312844836886,\n \"mc2\": 0.538254375639854,\n \"mc2_stderr\": 0.015244755693358225\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.77663772691397,\n \"acc_stderr\": 0.011705697565205193\n },\n \"harness|drop|3\": {\n \"em\": 0.0030411073825503355,\n \"em_stderr\": 0.0005638896908753155,\n \"f1\": 0.08151740771812048,\n \"f1_stderr\": 0.0016591952257614033\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.19711902956785443,\n \"acc_stderr\": 0.01095802163030062\n }\n}\n```", "repo_url": "https://huggingface.co/Weyaxi/Dolphin2.1-OpenOrca-7B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_09T14_13_23.628272", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-13-23.628272.parquet"]}, {"split": "2023_11_09T14_21_27.933712", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-21-27.933712.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-21-27.933712.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_09T14_13_23.628272", "path": ["**/details_harness|drop|3_2023-11-09T14-13-23.628272.parquet"]}, {"split": "2023_11_09T14_21_27.933712", "path": ["**/details_harness|drop|3_2023-11-09T14-21-27.933712.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T14-21-27.933712.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T14_13_23.628272", "path": ["**/details_harness|gsm8k|5_2023-11-09T14-13-23.628272.parquet"]}, {"split": "2023_11_09T14_21_27.933712", "path": ["**/details_harness|gsm8k|5_2023-11-09T14-21-27.933712.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-09T14-21-27.933712.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_09T14_13_23.628272", "path": ["**/details_harness|hellaswag|10_2023-11-09T14-13-23.628272.parquet"]}, {"split": "2023_11_09T14_21_27.933712", "path": ["**/details_harness|hellaswag|10_2023-11-09T14-21-27.933712.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-09T14-21-27.933712.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_09T14_13_23.628272", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-09T14-13-23.628272.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-09T14-13-23.628272.parquet", 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| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of Weyaxi/Dolphin2.1-OpenOrca-7B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model Weyaxi/Dolphin2.1-OpenOrca-7B on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T14:21:27.933712(note that their might be results for other tasks in 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 Weyaxi/Dolphin2.1-OpenOrca-7B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Dolphin2.1-OpenOrca-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:21:27.933712(note that their might be results for other tasks in 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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"## Dataset Structure",
"### Data Instances",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"# Dataset Card for Evaluation run of Weyaxi/Dolphin2.1-OpenOrca-7B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Dolphin2.1-OpenOrca-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:21:27.933712(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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"### Dataset Curators",
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"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of Weyaxi/Dolphin2.1-OpenOrca-7B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model Weyaxi/Dolphin2.1-OpenOrca-7B on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T14:21:27.933712(note that their might be results for other tasks in 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"
]
|
a942809714c5c7445161314543915faf37240e2d | # Dataset Card for "bw_spec_cls_160"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arieg/bw_spec_cls_160 | [
"region:us"
]
| 2023-11-09T14:19:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "56248", "1": "56249", "2": "56273", "3": "56274", "4": "56275", "5": "56465", "6": "56466", "7": "56467", "8": "56468", "9": "56469", "10": "56470", "11": "56471", "12": "56472", "13": "56474", "14": "56493", "15": "56495", "16": "56496", "17": "56497", "18": "56498", "19": "56499", "20": "56516", "21": "56517", "22": "56518", "23": "56519", "24": "56520", "25": "56521", "26": "56639", "27": "56640", "28": "56641", "29": "56645", "30": "56646", "31": "56648", "32": "56649", "33": "56650", "34": "56651", "35": "56686", "36": "56687", "37": "56688", "38": "56689", "39": "56690", "40": "56691", "41": "56692", "42": "56693", "43": "56694", "44": "56695", "45": "56696", "46": "56795", "47": "56796", "48": "56797", "49": "56798", "50": "56799", "51": "56800", "52": "56801", "53": "56802", "54": "56803", "55": "56804", "56": "56805", "57": "56888", "58": "57164", "59": "57175", "60": "57176", "61": "57177", "62": "57178", "63": "57179", "64": "57180", "65": "57344", "66": "57360", "67": "57371", "68": "57417", "69": "57418", "70": "57440", "71": "57442", "72": "57500", "73": "57569", "74": "57626", "75": "57627", "76": "57628", "77": "57629", "78": "57630", "79": "57639", "80": "57640", "81": "57648", "82": "57658", "83": "57661", "84": "57662", "85": "57663", "86": "57665", "87": "57691", "88": "57697", "89": "57819", "90": "57820", "91": "57821", "92": "57822", "93": "57823", "94": "57936", "95": "57937", "96": "57938", "97": "57939", "98": "57943", "99": "57968", "100": "58052", "101": "58053", "102": "58054", "103": "58060", "104": "58061", "105": "58063", "106": "58068", "107": "58070", "108": "58115", "109": "58116", "110": "58117", "111": "58135", "112": "58140", "113": "58161", "114": "58162", "115": "58164", "116": "58166", "117": "58169", "118": "58170", "119": "58173", "120": "58174", "121": "58212", "122": "58213", "123": "58215", "124": "58221", "125": "58225", "126": "58341", "127": "58474", "128": "59078", "129": "59373", "130": "59374", "131": "59561", "132": "59653", "133": "59654", "134": "59656", "135": "59657", "136": "59658", "137": "59659", "138": "59660", "139": "59663", "140": "59664", "141": "59666", "142": "59667", "143": "59669", "144": "59671", "145": "59673", "146": "59675", "147": "59676", "148": "59677", "149": "59678", "150": "59679", "151": "59680", "152": "59681", "153": "59682", "154": "59683", "155": "59684", "156": "59685", "157": "59686", "158": "59687", "159": "59688"}}}}], "splits": [{"name": "train", "num_bytes": 179214128.0, "num_examples": 3200}], "download_size": 179008943, "dataset_size": 179214128.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-10T12:25:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bw_spec_cls_160"
More Information needed | [
"# Dataset Card for \"bw_spec_cls_160\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bw_spec_cls_160\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bw_spec_cls_160\"\n\nMore Information needed"
]
|
c024a8ed26f45d13ba81df57b8bc02d1f3c16bac | # Dataset Card for "LoC-meme-generator"
This is an official meme dataset from the [library of congress](https://lccn.loc.gov/2018655320).
---
# Meme Dataset Exploratory Data Analysis Report
> courtesy of chatGPT data analysis
## Basic Dataset Information
- **Number of Entries**: 57685
- **Number of Columns**: 10
- **Columns**:
- Meme ID
- Archived URL
- Base Meme Name
- Meme Page URL
- MD5 Hash
- File Size (In Bytes)
- Alternate Text
- Display Name
- Upper Text
- Lower Text
## File Size Summary
{
"count": 57685.0,
"mean": 51045.948513478375,
"std": 11030.275842112662,
"min": 0.0,
"25%": 43371.0,
"50%": 51154.0,
"75%": 58668.0,
"max": 161623.0
}
## Top 10 Most Common Base Meme Names
{
"Y U No": 766,
"Futurama Fry": 663,
"Insanity Wolf": 612,
"Philosoraptor": 531,
"The Most Interesting Man In The World": 511,
"Success Kid": 510,
"Foul Bachelor Frog": 469,
"Socially Awkward Penguin": 446,
"Advice Yoda Gives": 420,
"Joseph Ducreux": 415
}
---
# Original Dataset Card
**Meme Generator** allows users to create and share image macros—images featuring text superimposed on pictures or artwork—in the style of popular internet memes. The site functions as a searchable collection of user-created images, where meme images, also known as image macros, are commonly used for online communication utilizing a range of templated text overlays.
## Names
- **Library of Congress**, collector
- **American Folklife Center**, sponsor
## Created / Published
2012-2018.
## Headings
- Memes
- Visual communication
## Genre
Data sets
## Notes
- The collection includes a dataset created on May 5, 2018, from crawls of the Library of Congress's Web Cultures Web Archive. This dataset, `memes-5-17`, encompasses data for 57,652 memes. The Meme Generator dataset includes a total of 86,310 meme images which represent 57,652 unique memes.
- *Cite as*: Meme Generator dataset, 2018-05-17.
- Meme Generator archived web site (AFC 9999/999), Archive of Folk Culture, American Folklife Center, Library of Congress, Washington, D.C.
- Library of Congress Web Archiving Team; Accession; 2012-2018.
## Medium
- Archived website, textual datasets, images
- 57,652 items (`.csv`)
## Repository
Library of Congress Archive of Folk Culture
American Folklife Center
101 Independence Ave. S.E.,
Washington, DC USA 20540-4610
[http://hdl.loc.gov/loc.afc/folklife.home](http://hdl.loc.gov/loc.afc/folklife.home)
### Online Electronic Resource
## Digital Id
- [https://www.loc.gov/item/lcwaN0010226/](https://www.loc.gov/item/lcwaN0010226/)
- [https://hdl.loc.gov/loc.gdc/gdcdatasets.2018655320](https://hdl.loc.gov/loc.gdc/gdcdatasets.2018655320) | pszemraj/LoC-meme-generator | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"size_categories:10K<n<100K",
"language:en",
"language:ru",
"language:es",
"license:odc-by",
"meme",
"library of congress",
"government meme dataset",
"region:us"
]
| 2023-11-09T14:21:15+00:00 | {"language": ["en", "ru", "es"], "license": "odc-by", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image", "image-to-text"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "Meme ID", "dtype": "int64"}, {"name": "Archived URL", "dtype": "string"}, {"name": "Base Meme Name", "dtype": "string"}, {"name": "Meme Page URL", "dtype": "string"}, {"name": "MD5 Hash", "dtype": "string"}, {"name": "File Size (In Bytes)", "dtype": "int64"}, {"name": "Alternate Text", "dtype": "string"}, {"name": "Display Name", "dtype": "string"}, {"name": "Upper Text", "dtype": "string"}, {"name": "Lower Text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21358616, "num_examples": 57687}], "download_size": 10157337, "dataset_size": 21358616}, "tags": ["meme", "library of congress", "government meme dataset"], "thumbnail": "https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/5dfPWMwTxq-XfN0tIzeDZ.png"} | 2023-11-13T20:58:40+00:00 | []
| [
"en",
"ru",
"es"
]
| TAGS
#task_categories-text-to-image #task_categories-image-to-text #size_categories-10K<n<100K #language-English #language-Russian #language-Spanish #license-odc-by #meme #library of congress #government meme dataset #region-us
| # Dataset Card for "LoC-meme-generator"
This is an official meme dataset from the library of congress.
!image/png
---
# Meme Dataset Exploratory Data Analysis Report
> courtesy of chatGPT data analysis
## Basic Dataset Information
- Number of Entries: 57685
- Number of Columns: 10
- Columns:
- Meme ID
- Archived URL
- Base Meme Name
- Meme Page URL
- MD5 Hash
- File Size (In Bytes)
- Alternate Text
- Display Name
- Upper Text
- Lower Text
## File Size Summary
{
"count": 57685.0,
"mean": 51045.948513478375,
"std": 11030.275842112662,
"min": 0.0,
"25%": 43371.0,
"50%": 51154.0,
"75%": 58668.0,
"max": 161623.0
}
## Top 10 Most Common Base Meme Names
{
"Y U No": 766,
"Futurama Fry": 663,
"Insanity Wolf": 612,
"Philosoraptor": 531,
"The Most Interesting Man In The World": 511,
"Success Kid": 510,
"Foul Bachelor Frog": 469,
"Socially Awkward Penguin": 446,
"Advice Yoda Gives": 420,
"Joseph Ducreux": 415
}
---
# Original Dataset Card
Meme Generator allows users to create and share image macros—images featuring text superimposed on pictures or artwork—in the style of popular internet memes. The site functions as a searchable collection of user-created images, where meme images, also known as image macros, are commonly used for online communication utilizing a range of templated text overlays.
## Names
- Library of Congress, collector
- American Folklife Center, sponsor
## Created / Published
2012-2018.
## Headings
- Memes
- Visual communication
## Genre
Data sets
## Notes
- The collection includes a dataset created on May 5, 2018, from crawls of the Library of Congress's Web Cultures Web Archive. This dataset, 'memes-5-17', encompasses data for 57,652 memes. The Meme Generator dataset includes a total of 86,310 meme images which represent 57,652 unique memes.
- *Cite as*: Meme Generator dataset, 2018-05-17.
- Meme Generator archived web site (AFC 9999/999), Archive of Folk Culture, American Folklife Center, Library of Congress, Washington, D.C.
- Library of Congress Web Archiving Team; Accession; 2012-2018.
## Medium
- Archived website, textual datasets, images
- 57,652 items ('.csv')
## Repository
Library of Congress Archive of Folk Culture
American Folklife Center
101 Independence Ave. S.E.,
Washington, DC USA 20540-4610
URL
### Online Electronic Resource
## Digital Id
- URL
- URL | [
"# Dataset Card for \"LoC-meme-generator\"\n\nThis is an official meme dataset from the library of congress.\n\n\n!image/png\n\n\n---",
"# Meme Dataset Exploratory Data Analysis Report\n\n> courtesy of chatGPT data analysis",
"## Basic Dataset Information\n- Number of Entries: 57685\n- Number of Columns: 10\n- Columns:\n- Meme ID\n- Archived URL\n- Base Meme Name\n- Meme Page URL\n- MD5 Hash\n- File Size (In Bytes)\n- Alternate Text\n- Display Name\n- Upper Text\n- Lower Text",
"## File Size Summary\n{\n \"count\": 57685.0,\n \"mean\": 51045.948513478375,\n \"std\": 11030.275842112662,\n \"min\": 0.0,\n \"25%\": 43371.0,\n \"50%\": 51154.0,\n \"75%\": 58668.0,\n \"max\": 161623.0\n}",
"## Top 10 Most Common Base Meme Names\n{\n \"Y U No\": 766,\n \"Futurama Fry\": 663,\n \"Insanity Wolf\": 612,\n \"Philosoraptor\": 531,\n \"The Most Interesting Man In The World\": 511,\n \"Success Kid\": 510,\n \"Foul Bachelor Frog\": 469,\n \"Socially Awkward Penguin\": 446,\n \"Advice Yoda Gives\": 420,\n \"Joseph Ducreux\": 415\n}\n\n---",
"# Original Dataset Card\n\nMeme Generator allows users to create and share image macros—images featuring text superimposed on pictures or artwork—in the style of popular internet memes. The site functions as a searchable collection of user-created images, where meme images, also known as image macros, are commonly used for online communication utilizing a range of templated text overlays.",
"## Names\n\n- Library of Congress, collector\n- American Folklife Center, sponsor",
"## Created / Published\n\n2012-2018.",
"## Headings\n\n- Memes\n- Visual communication",
"## Genre\n\nData sets",
"## Notes\n\n- The collection includes a dataset created on May 5, 2018, from crawls of the Library of Congress's Web Cultures Web Archive. This dataset, 'memes-5-17', encompasses data for 57,652 memes. The Meme Generator dataset includes a total of 86,310 meme images which represent 57,652 unique memes. \n - *Cite as*: Meme Generator dataset, 2018-05-17.\n- Meme Generator archived web site (AFC 9999/999), Archive of Folk Culture, American Folklife Center, Library of Congress, Washington, D.C.\n- Library of Congress Web Archiving Team; Accession; 2012-2018.",
"## Medium\n\n- Archived website, textual datasets, images\n- 57,652 items ('.csv')",
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"# Dataset Card for \"LoC-meme-generator\"\n\nThis is an official meme dataset from the library of congress.\n\n\n!image/png\n\n\n---",
"# Meme Dataset Exploratory Data Analysis Report\n\n> courtesy of chatGPT data analysis",
"## Basic Dataset Information\n- Number of Entries: 57685\n- Number of Columns: 10\n- Columns:\n- Meme ID\n- Archived URL\n- Base Meme Name\n- Meme Page URL\n- MD5 Hash\n- File Size (In Bytes)\n- Alternate Text\n- Display Name\n- Upper Text\n- Lower Text",
"## File Size Summary\n{\n \"count\": 57685.0,\n \"mean\": 51045.948513478375,\n \"std\": 11030.275842112662,\n \"min\": 0.0,\n \"25%\": 43371.0,\n \"50%\": 51154.0,\n \"75%\": 58668.0,\n \"max\": 161623.0\n}",
"## Top 10 Most Common Base Meme Names\n{\n \"Y U No\": 766,\n \"Futurama Fry\": 663,\n \"Insanity Wolf\": 612,\n \"Philosoraptor\": 531,\n \"The Most Interesting Man In The World\": 511,\n \"Success Kid\": 510,\n \"Foul Bachelor Frog\": 469,\n \"Socially Awkward Penguin\": 446,\n \"Advice Yoda Gives\": 420,\n \"Joseph Ducreux\": 415\n}\n\n---",
"# Original Dataset Card\n\nMeme Generator allows users to create and share image macros—images featuring text superimposed on pictures or artwork—in the style of popular internet memes. The site functions as a searchable collection of user-created images, where meme images, also known as image macros, are commonly used for online communication utilizing a range of templated text overlays.",
"## Names\n\n- Library of Congress, collector\n- American Folklife Center, sponsor",
"## Created / Published\n\n2012-2018.",
"## Headings\n\n- Memes\n- Visual communication",
"## Genre\n\nData sets",
"## Notes\n\n- The collection includes a dataset created on May 5, 2018, from crawls of the Library of Congress's Web Cultures Web Archive. This dataset, 'memes-5-17', encompasses data for 57,652 memes. The Meme Generator dataset includes a total of 86,310 meme images which represent 57,652 unique memes. \n - *Cite as*: Meme Generator dataset, 2018-05-17.\n- Meme Generator archived web site (AFC 9999/999), Archive of Folk Culture, American Folklife Center, Library of Congress, Washington, D.C.\n- Library of Congress Web Archiving Team; Accession; 2012-2018.",
"## Medium\n\n- Archived website, textual datasets, images\n- 57,652 items ('.csv')",
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"passage: TAGS\n#task_categories-text-to-image #task_categories-image-to-text #size_categories-10K<n<100K #language-English #language-Russian #language-Spanish #license-odc-by #meme #library of congress #government meme dataset #region-us \n# Dataset Card for \"LoC-meme-generator\"\n\nThis is an official meme dataset from the library of congress.\n\n\n!image/png\n\n\n---# Meme Dataset Exploratory Data Analysis Report\n\n> courtesy of chatGPT data analysis## Basic Dataset Information\n- Number of Entries: 57685\n- Number of Columns: 10\n- Columns:\n- Meme ID\n- Archived URL\n- Base Meme Name\n- Meme Page URL\n- MD5 Hash\n- File Size (In Bytes)\n- Alternate Text\n- Display Name\n- Upper Text\n- Lower Text## File Size Summary\n{\n \"count\": 57685.0,\n \"mean\": 51045.948513478375,\n \"std\": 11030.275842112662,\n \"min\": 0.0,\n \"25%\": 43371.0,\n \"50%\": 51154.0,\n \"75%\": 58668.0,\n \"max\": 161623.0\n}## Top 10 Most Common Base Meme Names\n{\n \"Y U No\": 766,\n \"Futurama Fry\": 663,\n \"Insanity Wolf\": 612,\n \"Philosoraptor\": 531,\n \"The Most Interesting Man In The World\": 511,\n \"Success Kid\": 510,\n \"Foul Bachelor Frog\": 469,\n \"Socially Awkward Penguin\": 446,\n \"Advice Yoda Gives\": 420,\n \"Joseph Ducreux\": 415\n}\n\n---# Original Dataset Card\n\nMeme Generator allows users to create and share image macros—images featuring text superimposed on pictures or artwork—in the style of popular internet memes. The site functions as a searchable collection of user-created images, where meme images, also known as image macros, are commonly used for online communication utilizing a range of templated text overlays."
]
|
c3ea9f74bc9aacc562968ddb1adc26b37e4c935c | # Dataset Card for "bw_spec_cls_80_26"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arieg/bw_spec_cls_80_26 | [
"region:us"
]
| 2023-11-09T14:31:46+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "62001", "1": "62003", "2": "62005", "3": "62007", "4": "62163", "5": "62164", "6": "62165", "7": "62180", "8": "62183", "9": "62185", "10": "62186", "11": "62187", "12": "62188", "13": "62189", "14": "62190", "15": "62191", "16": "62192", "17": "62193", "18": "62194", "19": "62195", "20": "62196", "21": "62337", "22": "62426", "23": "62436", "24": "62445", "25": "62446", "26": "62448", "27": "62449", "28": "62450", "29": "62452", "30": "62458", "31": "62525", "32": "62526", "33": "62527", "34": "62528", "35": "62529", "36": "62531", "37": "62532", "38": "62533", "39": "62534", "40": "62586", "41": "62589", "42": "62591", "43": "62592", "44": "62594", "45": "62595", "46": "62596", "47": "62655", "48": "62671", "49": "62742", "50": "62748", "51": "62749", "52": "62750", "53": "62751", "54": "62753", "55": "63043", "56": "63044", "57": "63045", "58": "63117", "59": "63191", "60": "63208", "61": "63224", "62": "63226", "63": "63287", "64": "63289", "65": "63290", "66": "63291", "67": "63292", "68": "63470", "69": "63471", "70": "63472", "71": "63626", "72": "63655", "73": "63733", "74": "63747", "75": "63755", "76": "63757", "77": "63770", "78": "63789", "79": "63803"}}}}], "splits": [{"name": "train", "num_bytes": 89137873.6, "num_examples": 1600}, {"name": "test", "num_bytes": 22127983.0, "num_examples": 400}], "download_size": 110364015, "dataset_size": 111265856.6}} | 2023-11-09T14:32:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bw_spec_cls_80_26"
More Information needed | [
"# Dataset Card for \"bw_spec_cls_80_26\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bw_spec_cls_80_26\"\n\nMore Information needed"
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| [
6,
20
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"passage: TAGS\n#region-us \n# Dataset Card for \"bw_spec_cls_80_26\"\n\nMore Information needed"
]
|
a638c23bae0ca258033b7c3ea509940c0a715ed5 | # Dataset Card for "evaluation_align_v1-public"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | male-2/evaluation_align_v1-public | [
"region:us"
]
| 2023-11-09T14:35:07+00:00 | {"dataset_info": {"features": [{"name": "Aspect", "dtype": "string"}, {"name": "Sub-Aspect", "dtype": "string"}, {"name": "Query", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "Dialogue", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 104, "num_examples": 1}], "download_size": 2816, "dataset_size": 104}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-15T05:54:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "evaluation_align_v1-public"
More Information needed | [
"# Dataset Card for \"evaluation_align_v1-public\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"evaluation_align_v1-public\"\n\nMore Information needed"
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18
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"passage: TAGS\n#region-us \n# Dataset Card for \"evaluation_align_v1-public\"\n\nMore Information needed"
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|
dd5ce8d491e0b9bd26e6780234209913ca21403f |
# Dataset Card for Evaluation run of CobraMamba/mamba-gpt-7b-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CobraMamba/mamba-gpt-7b-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CobraMamba/mamba-gpt-7b-v1](https://huggingface.co/CobraMamba/mamba-gpt-7b-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v1_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T14:34:23.926109](https://huggingface.co/datasets/open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v1_public/blob/main/results_2023-11-09T14-34-23.926109.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6286909633628079,
"acc_stderr": 0.03215522070353069,
"acc_norm": 0.6377478775248846,
"acc_norm_stderr": 0.032851877291432414,
"mc1": 0.3084455324357405,
"mc1_stderr": 0.01616803938315687,
"mc2": 0.4634199786351567,
"mc2_stderr": 0.014481061527331505,
"em": 0.2679320469798658,
"em_stderr": 0.004535526201164825,
"f1": 0.31668204697986585,
"f1_stderr": 0.004459593071277455
},
"harness|arc:challenge|25": {
"acc": 0.575938566552901,
"acc_stderr": 0.014441889627464396,
"acc_norm": 0.6126279863481229,
"acc_norm_stderr": 0.01423587248790987
},
"harness|hellaswag|10": {
"acc": 0.6354311890061741,
"acc_stderr": 0.004803253812881043,
"acc_norm": 0.8409679346743677,
"acc_norm_stderr": 0.003649585852821192
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.631578947368421,
"acc_stderr": 0.03925523381052932,
"acc_norm": 0.631578947368421,
"acc_norm_stderr": 0.03925523381052932
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.57,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.57,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
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}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v1 | [
"region:us"
]
| 2023-11-09T14:37:23+00:00 | {"pretty_name": "Evaluation run of CobraMamba/mamba-gpt-7b-v1", "dataset_summary": "Dataset automatically created during the evaluation run of model [CobraMamba/mamba-gpt-7b-v1](https://huggingface.co/CobraMamba/mamba-gpt-7b-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v1_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T14:34:23.926109](https://huggingface.co/datasets/open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v1_public/blob/main/results_2023-11-09T14-34-23.926109.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\": 0.6286909633628079,\n \"acc_stderr\": 0.03215522070353069,\n \"acc_norm\": 0.6377478775248846,\n \"acc_norm_stderr\": 0.032851877291432414,\n \"mc1\": 0.3084455324357405,\n \"mc1_stderr\": 0.01616803938315687,\n \"mc2\": 0.4634199786351567,\n \"mc2_stderr\": 0.014481061527331505,\n \"em\": 0.2679320469798658,\n \"em_stderr\": 0.004535526201164825,\n \"f1\": 0.31668204697986585,\n \"f1_stderr\": 0.004459593071277455\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.575938566552901,\n \"acc_stderr\": 0.014441889627464396,\n \"acc_norm\": 0.6126279863481229,\n \"acc_norm_stderr\": 0.01423587248790987\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6354311890061741,\n \"acc_stderr\": 0.004803253812881043,\n \"acc_norm\": 0.8409679346743677,\n \"acc_norm_stderr\": 0.003649585852821192\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\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\": 0.630057803468208,\n \"acc_stderr\": 0.0368122963339432,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.0368122963339432\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.45098039215686275,\n \"acc_stderr\": 0.04951218252396264,\n \"acc_norm\": 0.45098039215686275,\n \"acc_norm_stderr\": 0.04951218252396264\n },\n 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"2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_accounting|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_law_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_law|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_medicine_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": 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["**/details_harness|hendrycksTest-security_studies|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_hendrycksTest_sociology_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-sociology|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_hendrycksTest_us_foreign_policy_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_hendrycksTest_virology_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["**/details_harness|winogrande|5_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-09T14-34-23.926109.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_09T14_34_23.926109", "path": ["results_2023-11-09T14-34-23.926109.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T14-34-23.926109.parquet"]}]}]} | 2023-11-09T14:38:25+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of CobraMamba/mamba-gpt-7b-v1
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model CobraMamba/mamba-gpt-7b-v1 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T14:34:23.926109(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
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"## Latest results\n\nThese are the latest results from run 2023-11-09T14:34:23.926109(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
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"## Additional Information",
"### Dataset Curators",
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]
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CobraMamba/mamba-gpt-7b-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:34:23.926109(note that their might be results for other tasks in 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 CobraMamba/mamba-gpt-7b-v1## 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 CobraMamba/mamba-gpt-7b-v1 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T14:34:23.926109(note that their might be results for other tasks in 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"
]
|
9580a07bce0cd8320c36d23d0e738ef332879a7f |
# Dataset Card for Evaluation run of uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b](https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T14:37:01.184556](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b_public/blob/main/results_2023-11-09T14-37-01.184556.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6324163252907573,
"acc_stderr": 0.032217952701907616,
"acc_norm": 0.6408444136360775,
"acc_norm_stderr": 0.03289870821499382,
"mc1": 0.3525091799265606,
"mc1_stderr": 0.016724646380756547,
"mc2": 0.5252044347181257,
"mc2_stderr": 0.015164118244947575,
"em": 0.0032508389261744967,
"em_stderr": 0.0005829486708558965,
"f1": 0.08664324664429508,
"f1_stderr": 0.0017394064480495393
},
"harness|arc:challenge|25": {
"acc": 0.6083617747440273,
"acc_stderr": 0.014264122124938211,
"acc_norm": 0.643344709897611,
"acc_norm_stderr": 0.013998056902620194
},
"harness|hellaswag|10": {
"acc": 0.6489743079067914,
"acc_stderr": 0.004763155068744876,
"acc_norm": 0.8439553873730332,
"acc_norm_stderr": 0.003621559719378182
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6074074074074074,
"acc_stderr": 0.0421850621536888,
"acc_norm": 0.6074074074074074,
"acc_norm_stderr": 0.0421850621536888
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119668,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119668
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.690566037735849,
"acc_stderr": 0.028450154794118637,
"acc_norm": 0.690566037735849,
"acc_norm_stderr": 0.028450154794118637
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.037161774375660185,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.037161774375660185
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.630057803468208,
"acc_stderr": 0.036812296333943194,
"acc_norm": 0.630057803468208,
"acc_norm_stderr": 0.036812296333943194
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.04755129616062947,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.04755129616062947
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5574468085106383,
"acc_stderr": 0.03246956919789958,
"acc_norm": 0.5574468085106383,
"acc_norm_stderr": 0.03246956919789958
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5862068965517241,
"acc_stderr": 0.04104269211806232,
"acc_norm": 0.5862068965517241,
"acc_norm_stderr": 0.04104269211806232
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4021164021164021,
"acc_stderr": 0.025253032554997692,
"acc_norm": 0.4021164021164021,
"acc_norm_stderr": 0.025253032554997692
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b | [
"region:us"
]
| 2023-11-09T14:40:00+00:00 | {"pretty_name": "Evaluation run of uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b](https://huggingface.co/uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T14:37:01.184556](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-dolphin-orca-platypus-samantha-7b_public/blob/main/results_2023-11-09T14-37-01.184556.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\": 0.6324163252907573,\n \"acc_stderr\": 0.032217952701907616,\n \"acc_norm\": 0.6408444136360775,\n \"acc_norm_stderr\": 0.03289870821499382,\n \"mc1\": 0.3525091799265606,\n \"mc1_stderr\": 0.016724646380756547,\n \"mc2\": 0.5252044347181257,\n \"mc2_stderr\": 0.015164118244947575,\n \"em\": 0.0032508389261744967,\n \"em_stderr\": 0.0005829486708558965,\n \"f1\": 0.08664324664429508,\n \"f1_stderr\": 0.0017394064480495393\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.6083617747440273,\n \"acc_stderr\": 0.014264122124938211,\n \"acc_norm\": 0.643344709897611,\n \"acc_norm_stderr\": 0.013998056902620194\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6489743079067914,\n \"acc_stderr\": 0.004763155068744876,\n \"acc_norm\": 0.8439553873730332,\n \"acc_norm_stderr\": 0.003621559719378182\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6074074074074074,\n \"acc_stderr\": 0.0421850621536888,\n \"acc_norm\": 0.6074074074074074,\n \"acc_norm_stderr\": 0.0421850621536888\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119668,\n \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119668\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.690566037735849,\n \"acc_stderr\": 0.028450154794118637,\n \"acc_norm\": 0.690566037735849,\n \"acc_norm_stderr\": 0.028450154794118637\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n \"acc_stderr\": 0.037161774375660185,\n \"acc_norm\": 0.7291666666666666,\n \"acc_norm_stderr\": 0.037161774375660185\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.47,\n \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.05016135580465919\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|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_medicine|5\": {\n \"acc\": 0.630057803468208,\n \"acc_stderr\": 0.036812296333943194,\n \"acc_norm\": 0.630057803468208,\n \"acc_norm_stderr\": 0.036812296333943194\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062947,\n \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 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["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_medicine|5_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "harness_hendrycksTest_professional_psychology_5", "data_files": [{"split": "2023_11_09T14_37_01.184556", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-professional_psychology|5_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "harness_hendrycksTest_public_relations_5", "data_files": [{"split": "2023_11_09T14_37_01.184556", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-public_relations|5_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "harness_hendrycksTest_security_studies_5", "data_files": 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[{"split": "2023_11_09T14_37_01.184556", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-virology|5_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "harness_hendrycksTest_world_religions_5", "data_files": [{"split": "2023_11_09T14_37_01.184556", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|hendrycksTest-world_religions|5_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "harness_truthfulqa_mc_0", "data_files": [{"split": "2023_11_09T14_37_01.184556", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|truthfulqa:mc|0_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_11_09T14_37_01.184556", "path": ["**/details_harness|winogrande|5_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-11-09T14-37-01.184556.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_11_09T14_37_01.184556", "path": ["results_2023-11-09T14-37-01.184556.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T14-37-01.184556.parquet"]}]}]} | 2023-11-09T14:41:03+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T14:37:01.184556(note that their might be results for other tasks in 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 uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:37:01.184556(note that their might be results for other tasks in 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",
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"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"## Dataset 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 uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:37:01.184556(note that their might be results for other tasks in 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 uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T14:37:01.184556(note that their might be results for other tasks in 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"
]
|
b6a3372a60d30e8ab436676d9d40147a4893a873 |
# Description
This dataset contains airborne hyperspectral data flightline over the Washington DC Mall provided with the permission of Spectral Information Technology Application Center of Virginia who was responsible for its collection. The sensor system HYDICE used in this case measured pixel response in 210 bands in the 0.4 to 2.4 μm region of the visible and infrared spectrum. Bands in the 0.9 and 1.4 μm region where the atmosphere is opaque have been omitted from the data set, leaving 191 bands. The data set contains 1208 scan lines with 307 pixels in each scan line. It totals approximately 150 Megabytes.
# Characteristics
Washington DC Mall data set classes, labels and the number of samples.
| # | Class | Samples |
|---|----------------|---------|
| 1 | Roofs | 21419 |
| 2 | Street | 9834 |
| 3 | Grass | 22873 |
| 4 | Trees | 6882 |
| 5 | Path | 1105 |
| 6 | Water | 11063 |
| 7 | Shadow | 3061 |
# Quick look
<figure>
<img src= "assets/1771082.gif" alt="Washington DC Mall" width="300" />
<figcaption>Fake color visualization of the Washington DC Mall dataset, with bands 60, 27, 17 for red, green, blue respectively.</figcaption>
</figure>
<figure>
<img src= "assets/4264435.gif" alt="Indian Pines gt" width="300" />
<figcaption>Groundtruth of Washington DC Mall dataset.</figcaption>
</figure>
# Credits
Dataset originally available as part of the Multispec project at: https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html
Copyright (C) 1994-2020 Purdue Research Foundation.
Work leading to MultiSpec was funded in part by NASA Grants NAGW-925, NAGW-3924 and NAGW5-3975.
Supported by AmericaView (www.americaview.org)
The hyperspectral data set (dc.tif) of the Washington, DC mall area is provided with the permission of Spectral Information Technology Application Center of Virginia who was responsible for its collection.
| danaroth/washington_dc_mall | [
"license:unknown",
"region:us"
]
| 2023-11-09T14:41:28+00:00 | {"license": "unknown"} | 2023-11-10T16:15:44+00:00 | []
| []
| TAGS
#license-unknown #region-us
| Description
===========
This dataset contains airborne hyperspectral data flightline over the Washington DC Mall provided with the permission of Spectral Information Technology Application Center of Virginia who was responsible for its collection. The sensor system HYDICE used in this case measured pixel response in 210 bands in the 0.4 to 2.4 μm region of the visible and infrared spectrum. Bands in the 0.9 and 1.4 μm region where the atmosphere is opaque have been omitted from the data set, leaving 191 bands. The data set contains 1208 scan lines with 307 pixels in each scan line. It totals approximately 150 Megabytes.
Characteristics
===============
Washington DC Mall data set classes, labels and the number of samples.
#: 1, Class: Roofs, Samples: 21419
#: 2, Class: Street, Samples: 9834
#: 3, Class: Grass, Samples: 22873
#: 4, Class: Trees, Samples: 6882
#: 5, Class: Path, Samples: 1105
#: 6, Class: Water, Samples: 11063
#: 7, Class: Shadow, Samples: 3061
Quick look
==========
Fake color visualization of the Washington DC Mall dataset, with bands 60, 27, 17 for red, green, blue respectively.
Groundtruth of Washington DC Mall dataset.
Credits
=======
Dataset originally available as part of the Multispec project at: URL
Copyright (C) 1994-2020 Purdue Research Foundation.
Work leading to MultiSpec was funded in part by NASA Grants NAGW-925, NAGW-3924 and NAGW5-3975.
Supported by AmericaView (URL)
The hyperspectral data set (URL) of the Washington, DC mall area is provided with the permission of Spectral Information Technology Application Center of Virginia who was responsible for its collection.
| []
| [
"TAGS\n#license-unknown #region-us \n"
]
| [
13
]
| [
"passage: TAGS\n#license-unknown #region-us \n"
]
|
c1db061cd287c93082d519f22f4096a7cf22d9e4 | # Gebetszeiten-Datensatz für Wien
Dieser Datensatz enthält die Gebetszeiten für Wien für das gesamte Jahr. Die Zeiten sind in der lokalen Wiener Zeit angegeben und enthalten die folgenden Gebete: Fajr, Shuruq, Dhuhr, Assr, Maghrib und Ishaa.
## Struktur
Jeder Tag ist im `MM-DD`-Format angegeben und enthält ein JSON-Objekt mit den Gebetszeiten für diesen Tag.
## Nutzung
Dieser Datensatz ist für Forschung, Bildung oder persönlichen Gebrauch bestimmt. Bitte stellen Sie sicher, dass Sie die Zeiten überprüfen, bevor Sie sie für religiöse Zwecke nutzen.
## Beitrag
Wenn Sie Fehler finden oder Verbesserungen vorschlagen möchten, erstellen Sie bitte ein Issue oder einen Pull-Request im Repository.
# Prayer Times Dataset for Vienna
This dataset contains the prayer times for Vienna for the entire year. Times are provided in local Vienna time and include the following prayers: Fajr, Shuruq, Dhuhr, Assr, Maghrib, and Ishaa.
## Structure
Each day is listed in `MM-DD` format and contains a JSON object with the prayer times for that day.
## Usage
This dataset is intended for research, educational, or personal use. Please ensure to verify the times before using them for religious purposes.
## Contributions
If you find any errors or would like to suggest improvements, please create an issue or a pull request in the repository.
# مجموعة بيانات أوقات الصلاة لمدينة فيينا
تحتوي هذه المجموعة على أوقات الصلاة لمدينة فيينا للعام كامل. الأوقات مُقدمة بتوقيت فيينا المحلي وتشمل الصلوات التالية: الفجر، الشروق، الظهر، العصر، المغرب والعشاء.
## الهيكل
يُسرد كل يوم بتنسيق `MM-DD` ويحتوي على كائن JSON بأوقات الصلاة لذلك اليوم.
## الاستخدام
هذه المجموعة مُعدة للبحث، الأغراض التعليمية، أو الاستخدام الشخصي. يُرجى التأكد من التحقق من الأوقات قبل استخدامها لأغراض دينية.
## المساهمات
إذا وجدت أي أخطاء أو أردت اقتراح تحسينات، الرجاء إنشاء مُشكلة أو طلب سحب في المستودع.
### Credits:
The data comes from this source: https://islamiccentre.at/goe/
---
license: other
license_name: islamic-mit-license
license_link: LICENSE
---
| Bilgi-arayan-Aslan/Prayer-times-Vienna_Gebetszeiten-Wien-GOE | [
"region:us"
]
| 2023-11-09T14:43:53+00:00 | {} | 2023-11-09T16:22:50+00:00 | []
| []
| TAGS
#region-us
| # Gebetszeiten-Datensatz für Wien
Dieser Datensatz enthält die Gebetszeiten für Wien für das gesamte Jahr. Die Zeiten sind in der lokalen Wiener Zeit angegeben und enthalten die folgenden Gebete: Fajr, Shuruq, Dhuhr, Assr, Maghrib und Ishaa.
## Struktur
Jeder Tag ist im 'MM-DD'-Format angegeben und enthält ein JSON-Objekt mit den Gebetszeiten für diesen Tag.
## Nutzung
Dieser Datensatz ist für Forschung, Bildung oder persönlichen Gebrauch bestimmt. Bitte stellen Sie sicher, dass Sie die Zeiten überprüfen, bevor Sie sie für religiöse Zwecke nutzen.
## Beitrag
Wenn Sie Fehler finden oder Verbesserungen vorschlagen möchten, erstellen Sie bitte ein Issue oder einen Pull-Request im Repository.
# Prayer Times Dataset for Vienna
This dataset contains the prayer times for Vienna for the entire year. Times are provided in local Vienna time and include the following prayers: Fajr, Shuruq, Dhuhr, Assr, Maghrib, and Ishaa.
## Structure
Each day is listed in 'MM-DD' format and contains a JSON object with the prayer times for that day.
## Usage
This dataset is intended for research, educational, or personal use. Please ensure to verify the times before using them for religious purposes.
## Contributions
If you find any errors or would like to suggest improvements, please create an issue or a pull request in the repository.
# مجموعة بيانات أوقات الصلاة لمدينة فيينا
تحتوي هذه المجموعة على أوقات الصلاة لمدينة فيينا للعام كامل. الأوقات مُقدمة بتوقيت فيينا المحلي وتشمل الصلوات التالية: الفجر، الشروق، الظهر، العصر، المغرب والعشاء.
## الهيكل
يُسرد كل يوم بتنسيق 'MM-DD' ويحتوي على كائن JSON بأوقات الصلاة لذلك اليوم.
## الاستخدام
هذه المجموعة مُعدة للبحث، الأغراض التعليمية، أو الاستخدام الشخصي. يُرجى التأكد من التحقق من الأوقات قبل استخدامها لأغراض دينية.
## المساهمات
إذا وجدت أي أخطاء أو أردت اقتراح تحسينات، الرجاء إنشاء مُشكلة أو طلب سحب في المستودع.
### Credits:
The data comes from this source: URL
---
license: other
license_name: islamic-mit-license
license_link: LICENSE
---
| [
"# Gebetszeiten-Datensatz für Wien\n\nDieser Datensatz enthält die Gebetszeiten für Wien für das gesamte Jahr. Die Zeiten sind in der lokalen Wiener Zeit angegeben und enthalten die folgenden Gebete: Fajr, Shuruq, Dhuhr, Assr, Maghrib und Ishaa.",
"## Struktur\nJeder Tag ist im 'MM-DD'-Format angegeben und enthält ein JSON-Objekt mit den Gebetszeiten für diesen Tag.",
"## Nutzung\nDieser Datensatz ist für Forschung, Bildung oder persönlichen Gebrauch bestimmt. Bitte stellen Sie sicher, dass Sie die Zeiten überprüfen, bevor Sie sie für religiöse Zwecke nutzen.",
"## Beitrag\nWenn Sie Fehler finden oder Verbesserungen vorschlagen möchten, erstellen Sie bitte ein Issue oder einen Pull-Request im Repository.",
"# Prayer Times Dataset for Vienna\n\nThis dataset contains the prayer times for Vienna for the entire year. Times are provided in local Vienna time and include the following prayers: Fajr, Shuruq, Dhuhr, Assr, Maghrib, and Ishaa.",
"## Structure\nEach day is listed in 'MM-DD' format and contains a JSON object with the prayer times for that day.",
"## Usage\nThis dataset is intended for research, educational, or personal use. Please ensure to verify the times before using them for religious purposes.",
"## Contributions\nIf you find any errors or would like to suggest improvements, please create an issue or a pull request in the repository.",
"# مجموعة بيانات أوقات الصلاة لمدينة فيينا\n\nتحتوي هذه المجموعة على أوقات الصلاة لمدينة فيينا للعام كامل. الأوقات مُقدمة بتوقيت فيينا المحلي وتشمل الصلوات التالية: الفجر، الشروق، الظهر، العصر، المغرب والعشاء.",
"## الهيكل\nيُسرد كل يوم بتنسيق 'MM-DD' ويحتوي على كائن JSON بأوقات الصلاة لذلك اليوم.",
"## الاستخدام\nهذه المجموعة مُعدة للبحث، الأغراض التعليمية، أو الاستخدام الشخصي. يُرجى التأكد من التحقق من الأوقات قبل استخدامها لأغراض دينية.",
"## المساهمات\nإذا وجدت أي أخطاء أو أردت اقتراح تحسينات، الرجاء إنشاء مُشكلة أو طلب سحب في المستودع.",
"### Credits:\nThe data comes from this source: URL\n\n---\nlicense: other\nlicense_name: islamic-mit-license\nlicense_link: LICENSE\n---"
]
| [
"TAGS\n#region-us \n",
"# Gebetszeiten-Datensatz für Wien\n\nDieser Datensatz enthält die Gebetszeiten für Wien für das gesamte Jahr. Die Zeiten sind in der lokalen Wiener Zeit angegeben und enthalten die folgenden Gebete: Fajr, Shuruq, Dhuhr, Assr, Maghrib und Ishaa.",
"## Struktur\nJeder Tag ist im 'MM-DD'-Format angegeben und enthält ein JSON-Objekt mit den Gebetszeiten für diesen Tag.",
"## Nutzung\nDieser Datensatz ist für Forschung, Bildung oder persönlichen Gebrauch bestimmt. Bitte stellen Sie sicher, dass Sie die Zeiten überprüfen, bevor Sie sie für religiöse Zwecke nutzen.",
"## Beitrag\nWenn Sie Fehler finden oder Verbesserungen vorschlagen möchten, erstellen Sie bitte ein Issue oder einen Pull-Request im Repository.",
"# Prayer Times Dataset for Vienna\n\nThis dataset contains the prayer times for Vienna for the entire year. Times are provided in local Vienna time and include the following prayers: Fajr, Shuruq, Dhuhr, Assr, Maghrib, and Ishaa.",
"## Structure\nEach day is listed in 'MM-DD' format and contains a JSON object with the prayer times for that day.",
"## Usage\nThis dataset is intended for research, educational, or personal use. Please ensure to verify the times before using them for religious purposes.",
"## Contributions\nIf you find any errors or would like to suggest improvements, please create an issue or a pull request in the repository.",
"# مجموعة بيانات أوقات الصلاة لمدينة فيينا\n\nتحتوي هذه المجموعة على أوقات الصلاة لمدينة فيينا للعام كامل. الأوقات مُقدمة بتوقيت فيينا المحلي وتشمل الصلوات التالية: الفجر، الشروق، الظهر، العصر، المغرب والعشاء.",
"## الهيكل\nيُسرد كل يوم بتنسيق 'MM-DD' ويحتوي على كائن JSON بأوقات الصلاة لذلك اليوم.",
"## الاستخدام\nهذه المجموعة مُعدة للبحث، الأغراض التعليمية، أو الاستخدام الشخصي. يُرجى التأكد من التحقق من الأوقات قبل استخدامها لأغراض دينية.",
"## المساهمات\nإذا وجدت أي أخطاء أو أردت اقتراح تحسينات، الرجاء إنشاء مُشكلة أو طلب سحب في المستودع.",
"### Credits:\nThe data comes from this source: URL\n\n---\nlicense: other\nlicense_name: islamic-mit-license\nlicense_link: LICENSE\n---"
]
| [
6,
64,
33,
37,
29,
59,
30,
32,
32,
58,
32,
39,
33,
36
]
| [
"passage: TAGS\n#region-us \n# Gebetszeiten-Datensatz für Wien\n\nDieser Datensatz enthält die Gebetszeiten für Wien für das gesamte Jahr. Die Zeiten sind in der lokalen Wiener Zeit angegeben und enthalten die folgenden Gebete: Fajr, Shuruq, Dhuhr, Assr, Maghrib und Ishaa.## Struktur\nJeder Tag ist im 'MM-DD'-Format angegeben und enthält ein JSON-Objekt mit den Gebetszeiten für diesen Tag.## Nutzung\nDieser Datensatz ist für Forschung, Bildung oder persönlichen Gebrauch bestimmt. Bitte stellen Sie sicher, dass Sie die Zeiten überprüfen, bevor Sie sie für religiöse Zwecke nutzen.## Beitrag\nWenn Sie Fehler finden oder Verbesserungen vorschlagen möchten, erstellen Sie bitte ein Issue oder einen Pull-Request im Repository.# Prayer Times Dataset for Vienna\n\nThis dataset contains the prayer times for Vienna for the entire year. Times are provided in local Vienna time and include the following prayers: Fajr, Shuruq, Dhuhr, Assr, Maghrib, and Ishaa.## Structure\nEach day is listed in 'MM-DD' format and contains a JSON object with the prayer times for that day.## Usage\nThis dataset is intended for research, educational, or personal use. Please ensure to verify the times before using them for religious purposes.## Contributions\nIf you find any errors or would like to suggest improvements, please create an issue or a pull request in the repository.# مجموعة بيانات أوقات الصلاة لمدينة فيينا\n\nتحتوي هذه المجموعة على أوقات الصلاة لمدينة فيينا للعام كامل. الأوقات مُقدمة بتوقيت فيينا المحلي وتشمل الصلوات التالية: الفجر، الشروق، الظهر، العصر، المغرب والعشاء.## الهيكل\nيُسرد كل يوم بتنسيق 'MM-DD' ويحتوي على كائن JSON بأوقات الصلاة لذلك اليوم.## الاستخدام\nهذه المجموعة مُعدة للبحث، الأغراض التعليمية، أو الاستخدام الشخصي. يُرجى التأكد من التحقق من الأوقات قبل استخدامها لأغراض دينية.## المساهمات\nإذا وجدت أي أخطاء أو أردت اقتراح تحسينات، الرجاء إنشاء مُشكلة أو طلب سحب في المستودع."
]
|
78c7e01a7130d04b29ab55d2633bbb1fc97c50d6 |
# Dataset Card for Evaluation run of migtissera/SynthIA-7B-v1.5
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/migtissera/SynthIA-7B-v1.5
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [migtissera/SynthIA-7B-v1.5](https://huggingface.co/migtissera/SynthIA-7B-v1.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_migtissera__SynthIA-7B-v1.5_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T14:41:56.883085](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__SynthIA-7B-v1.5_public/blob/main/results_2023-11-09T14-41-56.883085.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": {
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"acc_stderr": 0.03252538162461919,
"acc_norm": 0.63804599014876,
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"mc1": 0.35128518971848227,
"mc1_stderr": 0.016711358163544403,
"mc2": 0.5131996962275648,
"mc2_stderr": 0.015337988977122931,
"em": 0.1875,
"em_stderr": 0.003997164044486006,
"f1": 0.26010591442953035,
"f1_stderr": 0.004042449995216609
},
"harness|arc:challenge|25": {
"acc": 0.5870307167235495,
"acc_stderr": 0.014388344935398324,
"acc_norm": 0.6271331058020477,
"acc_norm_stderr": 0.014131176760131172
},
"harness|hellaswag|10": {
"acc": 0.6432981477793268,
"acc_stderr": 0.0047804672709117705,
"acc_norm": 0.833698466440948,
"acc_norm_stderr": 0.0037159010850549967
},
"harness|hendrycksTest-abstract_algebra|5": {
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"harness|hendrycksTest-anatomy|5": {
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"harness|hendrycksTest-astronomy|5": {
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},
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},
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},
"harness|hendrycksTest-college_computer_science|5": {
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"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.03882310850890594,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.03882310850890594
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8187134502923976,
"acc_stderr": 0.029547741687640038,
"acc_norm": 0.8187134502923976,
"acc_norm_stderr": 0.029547741687640038
},
"harness|truthfulqa:mc|0": {
"mc1": 0.35128518971848227,
"mc1_stderr": 0.016711358163544403,
"mc2": 0.5131996962275648,
"mc2_stderr": 0.015337988977122931
},
"harness|winogrande|5": {
"acc": 0.7924230465666929,
"acc_stderr": 0.01139859341938678
},
"harness|drop|3": {
"em": 0.1875,
"em_stderr": 0.003997164044486006,
"f1": 0.26010591442953035,
"f1_stderr": 0.004042449995216609
},
"harness|gsm8k|5": {
"acc": 0.17437452615617893,
"acc_stderr": 0.010451421361976231
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | open-llm-leaderboard/details_migtissera__SynthIA-7B-v1.5 | [
"region:us"
]
| 2023-11-09T14:44:56+00:00 | {"pretty_name": "Evaluation run of migtissera/SynthIA-7B-v1.5", "dataset_summary": "Dataset automatically created during the evaluation run of model [migtissera/SynthIA-7B-v1.5](https://huggingface.co/migtissera/SynthIA-7B-v1.5) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_migtissera__SynthIA-7B-v1.5_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T14:41:56.883085](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__SynthIA-7B-v1.5_public/blob/main/results_2023-11-09T14-41-56.883085.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\": 0.6291968571108129,\n \"acc_stderr\": 0.03252538162461919,\n \"acc_norm\": 0.63804599014876,\n \"acc_norm_stderr\": 0.03323519542303871,\n \"mc1\": 0.35128518971848227,\n \"mc1_stderr\": 0.016711358163544403,\n \"mc2\": 0.5131996962275648,\n \"mc2_stderr\": 0.015337988977122931,\n \"em\": 0.1875,\n \"em_stderr\": 0.003997164044486006,\n \"f1\": 0.26010591442953035,\n \"f1_stderr\": 0.004042449995216609\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5870307167235495,\n \"acc_stderr\": 0.014388344935398324,\n \"acc_norm\": 0.6271331058020477,\n \"acc_norm_stderr\": 0.014131176760131172\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6432981477793268,\n \"acc_stderr\": 0.0047804672709117705,\n \"acc_norm\": 0.833698466440948,\n \"acc_norm_stderr\": 0.0037159010850549967\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.6644736842105263,\n \"acc_stderr\": 0.038424985593952694,\n \"acc_norm\": 0.6644736842105263,\n \"acc_norm_stderr\": 0.038424985593952694\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6944444444444444,\n \"acc_stderr\": 0.03852084696008534,\n \"acc_norm\": 0.6944444444444444,\n \"acc_norm_stderr\": 0.03852084696008534\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6473988439306358,\n \"acc_stderr\": 0.036430371689585475,\n \"acc_norm\": 0.6473988439306358,\n \"acc_norm_stderr\": 0.036430371689585475\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.03208115750788684,\n \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.03208115750788684\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n \"acc_norm_stderr\": 0.04668000738510455\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.3941798941798942,\n \"acc_stderr\": 0.02516798233389414,\n \"acc_norm\": 0.3941798941798942,\n \"acc_norm_stderr\": 0.02516798233389414\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.04403438954768176\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n \"acc_stderr\": 0.02413763242933771,\n \"acc_norm\": 0.7645161290322581,\n \"acc_norm_stderr\": 0.02413763242933771\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586818,\n \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586818\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.02463978909770944,\n \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.02463978909770944\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.7058823529411765,\n \"acc_stderr\": 0.02959732973097809,\n \"acc_norm\": 0.7058823529411765,\n \"acc_norm_stderr\": 0.02959732973097809\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658753,\n \"acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658753\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.818348623853211,\n \"acc_stderr\": 0.016530617409266875,\n \"acc_norm\": 0.818348623853211,\n \"acc_norm_stderr\": 0.016530617409266875\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5231481481481481,\n \"acc_stderr\": 0.03406315360711507,\n \"acc_norm\": 0.5231481481481481,\n \"acc_norm_stderr\": 0.03406315360711507\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7990196078431373,\n \"acc_stderr\": 0.02812597226565438,\n \"acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.02812597226565438\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7932489451476793,\n \"acc_stderr\": 0.026361651668389094,\n \"acc_norm\": 0.7932489451476793,\n \"acc_norm_stderr\": 0.026361651668389094\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596914,\n \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596914\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.03351953879521272,\n \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.03351953879521272\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n \"acc_stderr\": 0.047184714852195886,\n \"acc_norm\": 0.44642857142857145,\n \"acc_norm_stderr\": 0.047184714852195886\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n \"acc_stderr\": 0.02158649400128138,\n \"acc_norm\": 0.8760683760683761,\n \"acc_norm_stderr\": 0.02158649400128138\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n \"acc_stderr\": 0.013740797258579828,\n \"acc_norm\": 0.8199233716475096,\n \"acc_norm_stderr\": 0.013740797258579828\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.0246853168672578,\n \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.0246853168672578\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.376536312849162,\n \"acc_stderr\": 0.016204672385106596,\n \"acc_norm\": 0.376536312849162,\n \"acc_norm_stderr\": 0.016204672385106596\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7156862745098039,\n \"acc_stderr\": 0.02582916327275748,\n \"acc_norm\": 0.7156862745098039,\n \"acc_norm_stderr\": 0.02582916327275748\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n \"acc_stderr\": 0.02592237178881876,\n \"acc_norm\": 0.7041800643086816,\n \"acc_norm_stderr\": 0.02592237178881876\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303062,\n \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303062\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4589308996088657,\n \"acc_stderr\": 0.012727084826799797,\n \"acc_norm\": 0.4589308996088657,\n \"acc_norm_stderr\": 0.012727084826799797\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6580882352941176,\n \"acc_stderr\": 0.028814722422254184,\n \"acc_norm\": 0.6580882352941176,\n \"acc_norm_stderr\": 0.028814722422254184\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6584967320261438,\n \"acc_stderr\": 0.019184639328092487,\n \"acc_norm\": 0.6584967320261438,\n \"acc_norm_stderr\": 0.019184639328092487\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6938775510204082,\n \"acc_stderr\": 0.02950489645459596,\n \"acc_norm\": 0.6938775510204082,\n \"acc_norm_stderr\": 0.02950489645459596\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n \"acc_stderr\": 0.026193923544454132,\n \"acc_norm\": 0.835820895522388,\n \"acc_norm_stderr\": 0.026193923544454132\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.84,\n \"acc_stderr\": 0.03684529491774711,\n \"acc_norm\": 0.84,\n \"acc_norm_stderr\": 0.03684529491774711\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n \"acc_stderr\": 0.03882310850890594,\n \"acc_norm\": 0.536144578313253,\n \"acc_norm_stderr\": 0.03882310850890594\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8187134502923976,\n \"acc_stderr\": 0.029547741687640038,\n \"acc_norm\": 0.8187134502923976,\n \"acc_norm_stderr\": 0.029547741687640038\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35128518971848227,\n \"mc1_stderr\": 0.016711358163544403,\n \"mc2\": 0.5131996962275648,\n \"mc2_stderr\": 0.015337988977122931\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7924230465666929,\n \"acc_stderr\": 0.01139859341938678\n },\n \"harness|drop|3\": {\n \"em\": 0.1875,\n \"em_stderr\": 0.003997164044486006,\n \"f1\": 0.26010591442953035,\n \"f1_stderr\": 0.004042449995216609\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17437452615617893,\n \"acc_stderr\": 0.010451421361976231\n }\n}\n```", "repo_url": "https://huggingface.co/migtissera/SynthIA-7B-v1.5", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_09T14_41_56.883085", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-41-56.883085.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-41-56.883085.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_09T14_41_56.883085", "path": ["**/details_harness|drop|3_2023-11-09T14-41-56.883085.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T14-41-56.883085.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T14_41_56.883085", "path": ["**/details_harness|gsm8k|5_2023-11-09T14-41-56.883085.parquet"]}, {"split": "latest", 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{"config_name": "results", "data_files": [{"split": "2023_11_09T14_41_56.883085", "path": ["results_2023-11-09T14-41-56.883085.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T14-41-56.883085.parquet"]}]}]} | 2023-11-09T14:46:00+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of migtissera/SynthIA-7B-v1.5
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model migtissera/SynthIA-7B-v1.5 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T14:41:56.883085(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Evaluation run of migtissera/SynthIA-7B-v1.5",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model migtissera/SynthIA-7B-v1.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:41:56.883085(note that their might be results for other tasks in 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 migtissera/SynthIA-7B-v1.5",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model migtissera/SynthIA-7B-v1.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-11-09T14:41:56.883085(note that their might be results for other tasks in 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 migtissera/SynthIA-7B-v1.5## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model migtissera/SynthIA-7B-v1.5 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T14:41:56.883085(note that their might be results for other tasks in 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"
]
|
e07e28907982dda268343e2ddfa987289b261097 |
# Dataset Card for Evaluation run of CobraMamba/mamba-gpt-7b-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/CobraMamba/mamba-gpt-7b-v2
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [CobraMamba/mamba-gpt-7b-v2](https://huggingface.co/CobraMamba/mamba-gpt-7b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v2_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-09T14:42:44.506385](https://huggingface.co/datasets/open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v2_public/blob/main/results_2023-11-09T14-42-44.506385.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.6125048552997057,
"acc_stderr": 0.03288150582791299,
"acc_norm": 0.621215728198735,
"acc_norm_stderr": 0.03360029488770885,
"mc1": 0.30599755201958384,
"mc1_stderr": 0.016132229728155045,
"mc2": 0.466285204838536,
"mc2_stderr": 0.014482857157517471,
"em": 0.2946728187919463,
"em_stderr": 0.004668797098936446,
"f1": 0.3407151845637583,
"f1_stderr": 0.004587411171504163
},
"harness|arc:challenge|25": {
"acc": 0.5750853242320819,
"acc_stderr": 0.014445698968520769,
"acc_norm": 0.6194539249146758,
"acc_norm_stderr": 0.01418827771234981
},
"harness|hellaswag|10": {
"acc": 0.6363274248157738,
"acc_stderr": 0.004800728138792391,
"acc_norm": 0.8382792272455686,
"acc_norm_stderr": 0.00367441979935367
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.631578947368421,
"acc_stderr": 0.03925523381052932,
"acc_norm": 0.631578947368421,
"acc_norm_stderr": 0.03925523381052932
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.57,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.57,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.660377358490566,
"acc_stderr": 0.029146904747798328,
"acc_norm": 0.660377358490566,
"acc_norm_stderr": 0.029146904747798328
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7152777777777778,
"acc_stderr": 0.03773809990686934,
"acc_norm": 0.7152777777777778,
"acc_norm_stderr": 0.03773809990686934
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5895953757225434,
"acc_stderr": 0.03750757044895537,
"acc_norm": 0.5895953757225434,
"acc_norm_stderr": 0.03750757044895537
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5191489361702127,
"acc_stderr": 0.032662042990646796,
"acc_norm": 0.5191489361702127,
"acc_norm_stderr": 0.032662042990646796
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5586206896551724,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.5586206896551724,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4074074074074074,
"acc_stderr": 0.025305906241590632,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.025305906241590632
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7225806451612903,
"acc_stderr": 0.025470196835900055,
"acc_norm": 0.7225806451612903,
"acc_norm_stderr": 0.025470196835900055
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4630541871921182,
"acc_stderr": 0.035083705204426656,
"acc_norm": 0.4630541871921182,
"acc_norm_stderr": 0.035083705204426656
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.66,
"acc_stderr": 0.04760952285695237,
"acc_norm": 0.66,
"acc_norm_stderr": 0.04760952285695237
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009181,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009181
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8186528497409327,
"acc_stderr": 0.02780703236068609,
"acc_norm": 0.8186528497409327,
"acc_norm_stderr": 0.02780703236068609
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6128205128205129,
"acc_stderr": 0.024697216930878937,
"acc_norm": 0.6128205128205129,
"acc_norm_stderr": 0.024697216930878937
},
"harness|hendrycksTest-high_school_mathematics|5": {
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"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6092436974789915,
"acc_stderr": 0.03169380235712997,
"acc_norm": 0.6092436974789915,
"acc_norm_stderr": 0.03169380235712997
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2980132450331126,
"acc_stderr": 0.037345356767871984,
"acc_norm": 0.2980132450331126,
"acc_norm_stderr": 0.037345356767871984
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7779816513761468,
"acc_stderr": 0.01781884956479664,
"acc_norm": 0.7779816513761468,
"acc_norm_stderr": 0.01781884956479664
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.03407632093854051,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.03407632093854051
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7450980392156863,
"acc_stderr": 0.03058759135160425,
"acc_norm": 0.7450980392156863,
"acc_norm_stderr": 0.03058759135160425
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7763713080168776,
"acc_stderr": 0.027123298205229966,
"acc_norm": 0.7763713080168776,
"acc_norm_stderr": 0.027123298205229966
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6636771300448431,
"acc_stderr": 0.031708824268455,
"acc_norm": 0.6636771300448431,
"acc_norm_stderr": 0.031708824268455
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7480916030534351,
"acc_stderr": 0.03807387116306086,
"acc_norm": 0.7480916030534351,
"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.03749492448709698,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.03749492448709698
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7314814814814815,
"acc_stderr": 0.042844679680521934,
"acc_norm": 0.7314814814814815,
"acc_norm_stderr": 0.042844679680521934
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7361963190184049,
"acc_stderr": 0.03462419931615624,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.03462419931615624
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4642857142857143,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.4642857142857143,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7766990291262136,
"acc_stderr": 0.04123553189891431,
"acc_norm": 0.7766990291262136,
"acc_norm_stderr": 0.04123553189891431
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.023086635086841407,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.023086635086841407
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768079,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768079
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7918263090676884,
"acc_stderr": 0.014518592248904033,
"acc_norm": 0.7918263090676884,
"acc_norm_stderr": 0.014518592248904033
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.025190181327608405,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.025190181327608405
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.358659217877095,
"acc_stderr": 0.016040454426164474,
"acc_norm": 0.358659217877095,
"acc_norm_stderr": 0.016040454426164474
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7254901960784313,
"acc_stderr": 0.025553169991826528,
"acc_norm": 0.7254901960784313,
"acc_norm_stderr": 0.025553169991826528
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6913183279742765,
"acc_stderr": 0.026236965881153266,
"acc_norm": 0.6913183279742765,
"acc_norm_stderr": 0.026236965881153266
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6604938271604939,
"acc_stderr": 0.02634856441201162,
"acc_norm": 0.6604938271604939,
"acc_norm_stderr": 0.02634856441201162
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4397163120567376,
"acc_stderr": 0.029609912075594106,
"acc_norm": 0.4397163120567376,
"acc_norm_stderr": 0.029609912075594106
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.44002607561929596,
"acc_stderr": 0.012678037478574513,
"acc_norm": 0.44002607561929596,
"acc_norm_stderr": 0.012678037478574513
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6397058823529411,
"acc_stderr": 0.02916312857067073,
"acc_norm": 0.6397058823529411,
"acc_norm_stderr": 0.02916312857067073
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6372549019607843,
"acc_stderr": 0.019450768432505514,
"acc_norm": 0.6372549019607843,
"acc_norm_stderr": 0.019450768432505514
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6979591836734694,
"acc_stderr": 0.029393609319879804,
"acc_norm": 0.6979591836734694,
"acc_norm_stderr": 0.029393609319879804
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7960199004975125,
"acc_stderr": 0.02849317624532607,
"acc_norm": 0.7960199004975125,
"acc_norm_stderr": 0.02849317624532607
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.86,
"acc_stderr": 0.03487350880197771,
"acc_norm": 0.86,
"acc_norm_stderr": 0.03487350880197771
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5783132530120482,
"acc_stderr": 0.038444531817709175,
"acc_norm": 0.5783132530120482,
"acc_norm_stderr": 0.038444531817709175
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8304093567251462,
"acc_stderr": 0.02878210810540171,
"acc_norm": 0.8304093567251462,
"acc_norm_stderr": 0.02878210810540171
},
"harness|truthfulqa:mc|0": {
"mc1": 0.30599755201958384,
"mc1_stderr": 0.016132229728155045,
"mc2": 0.466285204838536,
"mc2_stderr": 0.014482857157517471
},
"harness|winogrande|5": {
"acc": 0.7845303867403315,
"acc_stderr": 0.011555295286059282
},
"harness|drop|3": {
"em": 0.2946728187919463,
"em_stderr": 0.004668797098936446,
"f1": 0.3407151845637583,
"f1_stderr": 0.004587411171504163
},
"harness|gsm8k|5": {
"acc": 0.1728582259287339,
"acc_stderr": 0.010415432246200569
}
}
```
### 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_CobraMamba__mamba-gpt-7b-v2 | [
"region:us"
]
| 2023-11-09T14:45:45+00:00 | {"pretty_name": "Evaluation run of CobraMamba/mamba-gpt-7b-v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [CobraMamba/mamba-gpt-7b-v2](https://huggingface.co/CobraMamba/mamba-gpt-7b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v2_public\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-11-09T14:42:44.506385](https://huggingface.co/datasets/open-llm-leaderboard/details_CobraMamba__mamba-gpt-7b-v2_public/blob/main/results_2023-11-09T14-42-44.506385.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\": 0.6125048552997057,\n \"acc_stderr\": 0.03288150582791299,\n \"acc_norm\": 0.621215728198735,\n \"acc_norm_stderr\": 0.03360029488770885,\n \"mc1\": 0.30599755201958384,\n \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.466285204838536,\n \"mc2_stderr\": 0.014482857157517471,\n \"em\": 0.2946728187919463,\n \"em_stderr\": 0.004668797098936446,\n \"f1\": 0.3407151845637583,\n \"f1_stderr\": 0.004587411171504163\n },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.5750853242320819,\n \"acc_stderr\": 0.014445698968520769,\n \"acc_norm\": 0.6194539249146758,\n \"acc_norm_stderr\": 0.01418827771234981\n },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6363274248157738,\n \"acc_stderr\": 0.004800728138792391,\n \"acc_norm\": 0.8382792272455686,\n \"acc_norm_stderr\": 0.00367441979935367\n },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.631578947368421,\n \"acc_stderr\": 0.03925523381052932,\n \"acc_norm\": 0.631578947368421,\n \"acc_norm_stderr\": 0.03925523381052932\n },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.029146904747798328,\n \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.029146904747798328\n },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7152777777777778,\n \"acc_stderr\": 0.03773809990686934,\n \"acc_norm\": 0.7152777777777778,\n \"acc_norm_stderr\": 0.03773809990686934\n },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5895953757225434,\n \"acc_stderr\": 0.03750757044895537,\n \"acc_norm\": 0.5895953757225434,\n \"acc_norm_stderr\": 0.03750757044895537\n },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.5191489361702127,\n \"acc_stderr\": 0.032662042990646796,\n \"acc_norm\": 0.5191489361702127,\n \"acc_norm_stderr\": 0.032662042990646796\n },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482758,\n \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482758\n },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.4074074074074074,\n \"acc_stderr\": 0.025305906241590632,\n \"acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.025305906241590632\n },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7225806451612903,\n \"acc_stderr\": 0.025470196835900055,\n \"acc_norm\": 0.7225806451612903,\n \"acc_norm_stderr\": 0.025470196835900055\n },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\": 0.4630541871921182,\n \"acc_stderr\": 0.035083705204426656,\n \"acc_norm\": 0.4630541871921182,\n \"acc_norm_stderr\": 0.035083705204426656\n },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \"acc\": 0.66,\n \"acc_stderr\": 0.04760952285695237,\n \"acc_norm\": 0.66,\n \"acc_norm_stderr\": 0.04760952285695237\n },\n \"harness|hendrycksTest-high_school_european_history|5\": {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009181,\n \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009181\n },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\": 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \"acc\": 0.8186528497409327,\n \"acc_stderr\": 0.02780703236068609,\n \"acc_norm\": 0.8186528497409327,\n \"acc_norm_stderr\": 0.02780703236068609\n },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \"acc\": 0.6128205128205129,\n \"acc_stderr\": 0.024697216930878937,\n \"acc_norm\": 0.6128205128205129,\n \"acc_norm_stderr\": 0.024697216930878937\n },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"acc\": 0.6092436974789915,\n \"acc_stderr\": 0.03169380235712997,\n \"acc_norm\": 0.6092436974789915,\n \"acc_norm_stderr\": 0.03169380235712997\n },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\": 0.2980132450331126,\n \"acc_stderr\": 0.037345356767871984,\n \"acc_norm\": 0.2980132450331126,\n \"acc_norm_stderr\": 0.037345356767871984\n },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.7779816513761468,\n \"acc_stderr\": 0.01781884956479664,\n \"acc_norm\": 0.7779816513761468,\n \"acc_norm_stderr\": 0.01781884956479664\n },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.7450980392156863,\n \"acc_stderr\": 0.03058759135160425,\n \"acc_norm\": 0.7450980392156863,\n \"acc_norm_stderr\": 0.03058759135160425\n },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\": 0.7763713080168776,\n \"acc_stderr\": 0.027123298205229966,\n \"acc_norm\": 0.7763713080168776,\n \"acc_norm_stderr\": 0.027123298205229966\n },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n \"acc_stderr\": 0.031708824268455,\n \"acc_norm\": 0.6636771300448431,\n \"acc_norm_stderr\": 0.031708824268455\n },\n \"harness|hendrycksTest-human_sexuality|5\": {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\": 0.7851239669421488,\n \"acc_stderr\": 0.03749492448709698,\n \"acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.03749492448709698\n },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.042844679680521934,\n \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.042844679680521934\n },\n \"harness|hendrycksTest-logical_fallacies|5\": {\n \"acc\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615624,\n \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615624\n },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4642857142857143,\n \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.4642857142857143,\n \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\": {\n \"acc\": 0.7766990291262136,\n \"acc_stderr\": 0.04123553189891431,\n \"acc_norm\": 0.7766990291262136,\n \"acc_norm_stderr\": 0.04123553189891431\n },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\": {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768079,\n \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768079\n },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7918263090676884,\n \"acc_stderr\": 0.014518592248904033,\n \"acc_norm\": 0.7918263090676884,\n \"acc_norm_stderr\": 0.014518592248904033\n },\n \"harness|hendrycksTest-moral_disputes|5\": {\n \"acc\": 0.6763005780346821,\n \"acc_stderr\": 0.025190181327608405,\n \"acc_norm\": 0.6763005780346821,\n \"acc_norm_stderr\": 0.025190181327608405\n },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.358659217877095,\n \"acc_stderr\": 0.016040454426164474,\n \"acc_norm\": 0.358659217877095,\n \"acc_norm_stderr\": 0.016040454426164474\n },\n \"harness|hendrycksTest-nutrition|5\": {\n \"acc\": 0.7254901960784313,\n \"acc_stderr\": 0.025553169991826528,\n \"acc_norm\": 0.7254901960784313,\n \"acc_norm_stderr\": 0.025553169991826528\n },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n \"acc_stderr\": 0.026236965881153266,\n \"acc_norm\": 0.6913183279742765,\n \"acc_norm_stderr\": 0.026236965881153266\n },\n \"harness|hendrycksTest-prehistory|5\": {\n \"acc\": 0.6604938271604939,\n \"acc_stderr\": 0.02634856441201162,\n \"acc_norm\": 0.6604938271604939,\n \"acc_norm_stderr\": 0.02634856441201162\n },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"acc\": 0.4397163120567376,\n \"acc_stderr\": 0.029609912075594106,\n \"acc_norm\": 0.4397163120567376,\n \"acc_norm_stderr\": 0.029609912075594106\n },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.44002607561929596,\n \"acc_stderr\": 0.012678037478574513,\n \"acc_norm\": 0.44002607561929596,\n \"acc_norm_stderr\": 0.012678037478574513\n },\n \"harness|hendrycksTest-professional_medicine|5\": {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.02916312857067073,\n \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.02916312857067073\n },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.019450768432505514,\n \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.019450768432505514\n },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.6979591836734694,\n \"acc_stderr\": 0.029393609319879804,\n \"acc_norm\": 0.6979591836734694,\n \"acc_norm_stderr\": 0.029393609319879804\n },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7960199004975125,\n \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197771,\n \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197771\n },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.30599755201958384,\n \"mc1_stderr\": 0.016132229728155045,\n \"mc2\": 0.466285204838536,\n \"mc2_stderr\": 0.014482857157517471\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7845303867403315,\n \"acc_stderr\": 0.011555295286059282\n },\n \"harness|drop|3\": {\n \"em\": 0.2946728187919463,\n \"em_stderr\": 0.004668797098936446,\n \"f1\": 0.3407151845637583,\n \"f1_stderr\": 0.004587411171504163\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.1728582259287339,\n \"acc_stderr\": 0.010415432246200569\n }\n}\n```", "repo_url": "https://huggingface.co/CobraMamba/mamba-gpt-7b-v2", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_arc_challenge_25", "data_files": [{"split": "2023_11_09T14_42_44.506385", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-42-44.506385.parquet"]}, {"split": "latest", "path": ["**/details_harness|arc:challenge|25_2023-11-09T14-42-44.506385.parquet"]}]}, {"config_name": "harness_drop_3", "data_files": [{"split": "2023_11_09T14_42_44.506385", "path": ["**/details_harness|drop|3_2023-11-09T14-42-44.506385.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-11-09T14-42-44.506385.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_11_09T14_42_44.506385", "path": ["**/details_harness|gsm8k|5_2023-11-09T14-42-44.506385.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-11-09T14-42-44.506385.parquet"]}]}, {"config_name": "harness_hellaswag_10", "data_files": [{"split": "2023_11_09T14_42_44.506385", "path": ["**/details_harness|hellaswag|10_2023-11-09T14-42-44.506385.parquet"]}, {"split": "latest", "path": ["**/details_harness|hellaswag|10_2023-11-09T14-42-44.506385.parquet"]}]}, {"config_name": "harness_hendrycksTest_5", "data_files": [{"split": "2023_11_09T14_42_44.506385", "path": ["**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-anatomy|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-astronomy|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-business_ethics|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-college_biology|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-college_chemistry|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-college_computer_science|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-college_mathematics|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-college_medicine|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-college_physics|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-computer_security|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-econometrics|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-09T14-42-44.506385.parquet", "**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-09T14-42-44.506385.parquet", 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{"config_name": "results", "data_files": [{"split": "2023_11_09T14_42_44.506385", "path": ["results_2023-11-09T14-42-44.506385.parquet"]}, {"split": "latest", "path": ["results_2023-11-09T14-42-44.506385.parquet"]}]}]} | 2023-11-09T14:46:50+00:00 | []
| []
| TAGS
#region-us
|
# Dataset Card for Evaluation run of CobraMamba/mamba-gpt-7b-v2
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model CobraMamba/mamba-gpt-7b-v2 on the Open LLM Leaderboard.
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-11-09T14:42:44.506385(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CobraMamba/mamba-gpt-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### Dataset Summary\n\nDataset automatically created during the evaluation run of model CobraMamba/mamba-gpt-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"### 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 CobraMamba/mamba-gpt-7b-v2## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model CobraMamba/mamba-gpt-7b-v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-11-09T14:42:44.506385(note that their might be results for other tasks in 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"
]
|
865c0a6f68b50fb544e940783b5ae754b49b5b36 | # Dataset Card for "qa-temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Back-up/qa-temp | [
"region:us"
]
| 2023-11-09T14:51:05+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "response", "struct": [{"name": "response", "dtype": "string"}]}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "instruction", "dtype": "string"}, {"name": "prompt_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 276860, "num_examples": 101}], "download_size": 144679, "dataset_size": 276860}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-11-09T14:51:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "qa-temp"
More Information needed | [
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|
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