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a5d6a712b9576700b507a386e792a2892a141450
|
# Dataset Card for "sentiment_analysis_affix_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/sentiment_analysis_affix_small
|
[
"region:us"
] |
2023-10-18T04:37:45+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "neg", "1": "pos"}}}}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21821.81818181818, "num_examples": 170}], "download_size": 18565, "dataset_size": 21821.81818181818}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T04:37:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "sentiment_analysis_affix_small"
More Information needed
|
[
"# Dataset Card for \"sentiment_analysis_affix_small\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"sentiment_analysis_affix_small\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"sentiment_analysis_affix_small\"\n\nMore Information needed"
] |
16d4d2e8ffd817cf1b9fec90c34b490385528e23
|
annotations_creators:
- other
language:
- en
language_creators:
- other
license: []
multilinguality:
- monolingual
pretty_name: Github Issues
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- github-issues
- huggingface-nlp-course
- datasets
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
- document-retrieval
|
anubhavmaity/github-issues
|
[
"region:us"
] |
2023-10-18T04:43:02+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "dtype": "string"}, {"name": "labels", "dtype": "string"}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "dtype": "string"}, {"name": "assignees", "dtype": "string"}, {"name": "milestone", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "created_at", "dtype": "string"}, {"name": "updated_at", "dtype": "string"}, {"name": "closed_at", "dtype": "string"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "float64"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "dtype": "string"}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "float64"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "float64"}, {"name": "pull_request", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 35370223, "num_examples": 6279}], "download_size": 9128830, "dataset_size": 35370223}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-20T03:00:40+00:00
|
[] |
[] |
TAGS
#region-us
|
annotations_creators:
- other
language:
- en
language_creators:
- other
license: []
multilinguality:
- monolingual
pretty_name: Github Issues
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- github-issues
- huggingface-nlp-course
- datasets
task_categories:
- text-classification
- text-retrieval
task_ids:
- multi-class-classification
- multi-label-classification
- document-retrieval
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
05fc54fbf79cca4157d1b7ce5b07ecbdaa3efc04
|
# 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]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
haurajahra/SQUAD-ID
|
[
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:id",
"license:other",
"region:us"
] |
2023-10-18T04:44:11+00:00
|
{"language": ["id"], "license": "other", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering"], "license_name": "marian-mt", "license_link": "LICENSE"}
|
2023-10-18T04:53:07+00:00
|
[] |
[
"id"
] |
TAGS
#task_categories-question-answering #size_categories-100K<n<1M #language-Indonesian #license-other #region-us
|
# 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
#### 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
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
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"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
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"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
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"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
40,
34,
4,
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[
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] |
adaa014397fb90ed2bb19e73ea9e4b02147c2e0d
|
# Dataset Card for "affixal_negation_polarity"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
joey234/affixal_negation_polarity
|
[
"region:us"
] |
2023-10-18T05:12:42+00:00
|
{"dataset_info": {"features": [{"name": "word", "dtype": "string"}, {"name": "neg_score", "dtype": "float64"}, {"name": "pos_score", "dtype": "float64"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 81446, "num_examples": 2089}], "download_size": 30395, "dataset_size": 81446}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T01:48:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "affixal_negation_polarity"
More Information needed
|
[
"# Dataset Card for \"affixal_negation_polarity\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"affixal_negation_polarity\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"affixal_negation_polarity\"\n\nMore Information needed"
] |
2238e0fb8893b342f9ffe59c09a44b5ced5c45c9
|
## このデータセットはLLMのfine-tuningにおける、**「初期化(initialize)」**を目的としたものです。
### データのレコード数は300件程度しかありません。
## 目的：非日本語で最適化されたLLMをFine-tuningで日本語を使いこなせるようにするための「DS群」
### (注意1：あくまで著者個人のテスト用です。DSを使うのは自由ですが、自己責任で・・・。)
### (注意２：代替10エポックの「初期化」のために使うことを考えています。)
## 使い方：
##(1) 以下の3種のデータセットを「シリアルに(後述)」使用します。
-1. 初期化用データセット（本DS）
-2. 著者がdatabrick-15k-jaを4分割させたものの一つ
-3. ユーザが準備した日本語のデータセット（本当に学習させたい情報）
##(2) ファインチューニングのシリアル学習の方案（自分で工夫してください。）
-1. 初期化用データセット（本DS): 10エポック程度
-2. 著者がdatabrick-15k-ja: 10エポック程度
-3. 日本語のデータセット：ご自分の納得がいくまで学習させてください。
詳しい情報は[こちらのブログ](https://jpnqeur23lmqsw.blogspot.com/2023/09/qeur23llmdss10-databricks15k.html)を参照してください。
|
QEU/QEU-initialize-300-ja
|
[
"license:apache-2.0",
"region:us"
] |
2023-10-18T06:02:02+00:00
|
{"license": "apache-2.0"}
|
2023-10-18T06:05:01+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
## このデータセットはLLMのfine-tuningにおける、「初期化(initialize)」を目的としたものです。
### データのレコード数は300件程度しかありません。
## 目的：非日本語で最適化されたLLMをFine-tuningで日本語を使いこなせるようにするための「DS群」
### (注意1：あくまで著者個人のテスト用です。DSを使うのは自由ですが、自己責任で・・・。)
### (注意２：代替10エポックの「初期化」のために使うことを考えています。)
## 使い方：
##(1) 以下の3種のデータセットを「シリアルに(後述)」使用します。
-1. 初期化用データセット（本DS）
-2. 著者がdatabrick-15k-jaを4分割させたものの一つ
-3. ユーザが準備した日本語のデータセット（本当に学習させたい情報）
##(2) ファインチューニングのシリアル学習の方案（自分で工夫してください。）
-1. 初期化用データセット（本DS): 10エポック程度
-2. 著者がdatabrick-15k-ja: 10エポック程度
-3. 日本語のデータセット：ご自分の納得がいくまで学習させてください。
詳しい情報はこちらのブログを参照してください。
|
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"## 目的：非日本語で最適化されたLLMをFine-tuningで日本語を使いこなせるようにするための「DS群」",
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14,
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[
"passage: TAGS\n#license-apache-2.0 #region-us \n## このデータセットはLLMのfine-tuningにおける、「初期化(initialize)」を目的としたものです。### データのレコード数は300件程度しかありません。## 目的：非日本語で最適化されたLLMをFine-tuningで日本語を使いこなせるようにするための「DS群」### (注意1：あくまで著者個人のテスト用です。DSを使うのは自由ですが、自己責任で・・・。)### (注意２：代替10エポックの「初期化」のために使うことを考えています。)## 使い方："
] |
f8bb414dfa488e1dc1d368e534a1e5eee0350f9a
|
# Dataset Card for "SSDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Sorenson-AILab-VSL/SSDataset
|
[
"region:us"
] |
2023-10-18T06:02:17+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1483436052.104, "num_examples": 39908}], "download_size": 1236946981, "dataset_size": 1483436052.104}}
|
2023-11-15T20:04:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SSDataset"
More Information needed
|
[
"# Dataset Card for \"SSDataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SSDataset\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SSDataset\"\n\nMore Information needed"
] |
66165eed8102da1fd0d1e1fe78d17a666c836a2f
|
# Dataset Card for Evaluation run of psmathur/model_420_preview
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/psmathur/model_420_preview
- **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_420_preview](https://huggingface.co/psmathur/model_420_preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_psmathur__model_420_preview",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T07:05:02.354385](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_420_preview/blob/main/results_2023-10-18T07-05-02.354385.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0016778523489932886,
"em_stderr": 0.0004191330178826867,
"f1": 0.06602034395973153,
"f1_stderr": 0.0013713725074901318,
"acc": 0.5827673137371175,
"acc_stderr": 0.011721630765571481
},
"harness|drop|3": {
"em": 0.0016778523489932886,
"em_stderr": 0.0004191330178826867,
"f1": 0.06602034395973153,
"f1_stderr": 0.0013713725074901318
},
"harness|gsm8k|5": {
"acc": 0.33206974981046244,
"acc_stderr": 0.012972465034361861
},
"harness|winogrande|5": {
"acc": 0.8334648776637726,
"acc_stderr": 0.0104707964967811
}
}
```
### 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_420_preview
|
[
"region:us"
] |
2023-10-18T06:05:06+00:00
|
{"pretty_name": "Evaluation run of psmathur/model_420_preview", "dataset_summary": "Dataset automatically created during the evaluation run of model [psmathur/model_420_preview](https://huggingface.co/psmathur/model_420_preview) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_psmathur__model_420_preview\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T07:05:02.354385](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_420_preview/blob/main/results_2023-10-18T07-05-02.354385.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826867,\n \"f1\": 0.06602034395973153,\n \"f1_stderr\": 0.0013713725074901318,\n \"acc\": 0.5827673137371175,\n \"acc_stderr\": 0.011721630765571481\n },\n \"harness|drop|3\": {\n \"em\": 0.0016778523489932886,\n \"em_stderr\": 0.0004191330178826867,\n \"f1\": 0.06602034395973153,\n \"f1_stderr\": 0.0013713725074901318\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.33206974981046244,\n \"acc_stderr\": 0.012972465034361861\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8334648776637726,\n \"acc_stderr\": 0.0104707964967811\n }\n}\n```", "repo_url": "https://huggingface.co/psmathur/model_420_preview", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T07_05_02.354385", "path": ["**/details_harness|drop|3_2023-10-18T07-05-02.354385.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T07-05-02.354385.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T07_05_02.354385", "path": ["**/details_harness|gsm8k|5_2023-10-18T07-05-02.354385.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T07-05-02.354385.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T07_05_02.354385", "path": ["**/details_harness|winogrande|5_2023-10-18T07-05-02.354385.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T07-05-02.354385.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T07_05_02.354385", "path": ["results_2023-10-18T07-05-02.354385.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T07-05-02.354385.parquet"]}]}]}
|
2023-10-18T06:05:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of psmathur/model_420_preview
## 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_420_preview on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T07:05:02.354385(note that their might be results for other tasks in 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_420_preview",
"## 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_420_preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T07:05:02.354385(note that their might be results for other tasks in 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 psmathur/model_420_preview",
"## 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_420_preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T07:05:02.354385(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
19,
31,
167,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
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10,
5,
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9,
8,
8,
7,
8,
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5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of psmathur/model_420_preview## 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_420_preview on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T07:05:02.354385(note that their might be results for other tasks in 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"
] |
7d55cfaab9c1f56b0df0e202516c9c5fa13ee001
|
# Dataset Card for "train1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
promptora11/train1
|
[
"region:us"
] |
2023-10-18T06:16:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 372714, "num_examples": 191}], "download_size": 241786, "dataset_size": 372714}}
|
2023-10-18T06:16:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "train1"
More Information needed
|
[
"# Dataset Card for \"train1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"train1\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"train1\"\n\nMore Information needed"
] |
c96c29b3c5640a49572e2998c8cb4ba708fc7b5c
|
### Device Name: [Valty Heater](https://snoppymart.com/valty-heater/)
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|
valtyheaterclickhere/valtyheaterclickhere
|
[
"region:us"
] |
2023-10-18T06:33:03+00:00
|
{}
|
2023-10-18T06:34:38+00:00
|
[] |
[] |
TAGS
#region-us
|
### Device Name: Valty Heater
Benefit: Easy to install and efficiently warm up the room.
Specifications: 500W Ceramic Heater
Small & Portable: LED Display & Timer
Overheated Protection Guarantee: 14-days money back guarantee
Price: $69.99
Rating: 4.8/5.0
Official Website: URL
Country for Sale: USA, CA
Availability: In Stock
Discount: Extra discounts available on the website
### Valty Heater Reviews, USA, CA
Winters can be extremely chilling for all the people who live any far from the equator. Winters are the time that people can go out in the snow, enjoy the time getting slower and also spend a lot of time with their family. But the only problem with this problem is the unbearable cold that comes along with it. At places, the temperature can even go in minus, and thus people must keep themselves warm as the body can catch flu very easily. Pneumonia and hypothermia are serious concerns related to winters too. The only way that the human body can survive without issues is if people keep themselves warm enough. People can wear a lot of winter clothes outside their home but at home, they need some kind of heater to get warm as wearing a lot of clothes at home are very uncomfortable for people. Conventional heaters or radiators take a lot of space and are very costly too. The energy consumption by such big appliances is a lot too. Thus, people try to find a better gadget that can keep them warm and is also affordable for them.
### \=> Click Here To Get Valty Heater From The Official Website!
Valty Portable Heater in US, CA, the perfect product for people who want such a gadget that is small and can heat a room efficiently in very little time. This product makes use of heat radiation by heating the air extremely. It is a small device that takes in air from one side, heats it, and then sends it out from the other side for it to circulate the room and warm the environment. It can heat the temperature to 30 degrees Celsius and can be controlled using a smart remote too. Valty Heater is affordable for people and is easy to use too.
### How is Valty Heater useful for people?
Valty Heater is a product that can be called the right innovation at the right time. People have been asking for things that they can keep with themselves at all times and are compact and can-do major works. Getting a heater which is the size of a small gadget and can be carried around anywhere is very important.
Valty Heater in CA, USA gives pleasure to people for getting their rooms heated up in 15 minutes with just a simple button touch on the console of this gadget. It has to be placed at one corner of the room and then turned on with a set temperature. It then ventilates hot air all around the room and makes sure that the complete room is warmed up. The best thing about this gadget is that it has a smart sensor that automatically turns it off as soon as the set temperature is set around the room. It is affordable for people and can be used very easily and thus has gained a lot of popularity. Valty Heater US, CA is, therefore, a very helpful product for students and corporate people who live alone and also for people who do not have heaters in their offices.
### \=> (SPECIAL OFFER) Click Here To Buy Valty Heater From The Official Website!
### Valty Heater Specifications:
The portable heater is very efficient. Check all specs below:
* 500W Ceramic Heater
* Small & Portable
* LED Display & Timer
* Overheated Protection
### What is the price of Valty Heater?
The cost of Valty Heater in USA, Canada, is very affordable. You can get single Valty Heater is $69.99 only. You can get this portable heater in bulk with discount prices. Check packages below:
2xValty Heater - $62.99/each
3xValty Heater - $55.99/each (BEST SELLER)
5xValty Heater - $48.99/each (MOST VALUED)
#### Valty Heater Official Website - URL
### What is the mechanism of Valty Heater?
Valty Heater has been made with the help of advanced machines and a lot of research overheat transfer. It is observable that hot air floats higher in the air and the colder air gets settled down and this is the reason why this gadget first sucks in air from the environment heats it and radiates it in the room with the help of its fan and then when that hot air gets higher in the room, it again sucks it in and radiates it around the room by further heating it. This way the air molecules supply thermal energy to neighbouring molecules and the whole room gets heated up very easily. The sensor used in this product helps the gadget to know what temperature is to be set around the room and when to turn it off.
### \=> Buy Valty Heater Before Stock Runs Out!
The whole outer body is made of shockproof hard plastic and the inside is made from metal. There is a copper centre in the product which gets heated up by electricity and then heats the air. Valty Heater is therefore an amazing example of how the radiation of heat can be used effectively. It is thus suggested for people and is gaining popularity in the market too.
Check Valty Heater bewertungen, Valty Heater stromverbrauch, Valty Heater test Deutsch. Besuchen Sie uns hier und holen Sie sich ein tragbares Heizgerät von Valty und lesen Sie Rezensionen in deutscher Sprache.
### How to fit Valty Heater in the room?
Valty Heater Canada, USA is a compact gadget and people can easily use it. For installing the gadget, all that a user needs is a double-sided tape or a proper mount that is available separately for the gadget. That mount or the tape has to be stuck on the wall at one corner of the room and then the user has to just put the gadget on this mounting and make sure that it does not fall or sticks out. The product can be then controlled using the console given on it or the smart remote that is provided by the makers. The connection for this device is manual and the plug has to be put in a normal supply plug. Remote works with the help of 2 AA batteries that are removable and can be changed.
### Where to buy Valty Heater?
The most selling portable heater is available at the official website of the Valty Heater. People can go and order it at their address. Many payment options are available for the users to choose from.The Valty Heater is available in USA, Canada. Besuchen Sie die offizielle deutsche Website von Valty Heater and check Valty Heater erfahrungen, Valty Heater stiftung warentest.
### EXCLUSIVE OFFER \*Now On Sale\* Click Here to Buy Valty Heater at the Best Price Online
### Disclaimer:
This is promotional content. Must consult an expert before using the device. This post contains an affiliate link and we receive a commission on every sale from this post (at no cost to you). Check the final price on the official website. Read T&C carefully before making any purchase.
|
[
"### Device Name: Valty Heater\n\n Benefit: Easy to install and efficiently warm up the room. \n\n Specifications: 500W Ceramic Heater \n\n Small & Portable: LED Display & Timer \n\n Overheated Protection Guarantee: 14-days money back guarantee \n\n Price: $69.99 \n\n Rating: 4.8/5.0 \n\n Official Website: URL\n\n Country for Sale: USA, CA \n\n Availability: In Stock \n\n Discount: Extra discounts available on the website",
"### Valty Heater Reviews, USA, CA\n\nWinters can be extremely chilling for all the people who live any far from the equator. Winters are the time that people can go out in the snow, enjoy the time getting slower and also spend a lot of time with their family. But the only problem with this problem is the unbearable cold that comes along with it. At places, the temperature can even go in minus, and thus people must keep themselves warm as the body can catch flu very easily. Pneumonia and hypothermia are serious concerns related to winters too. The only way that the human body can survive without issues is if people keep themselves warm enough. People can wear a lot of winter clothes outside their home but at home, they need some kind of heater to get warm as wearing a lot of clothes at home are very uncomfortable for people. Conventional heaters or radiators take a lot of space and are very costly too. The energy consumption by such big appliances is a lot too. Thus, people try to find a better gadget that can keep them warm and is also affordable for them.",
"### \\=> Click Here To Get Valty Heater From The Official Website!\n\nValty Portable Heater in US, CA, the perfect product for people who want such a gadget that is small and can heat a room efficiently in very little time. This product makes use of heat radiation by heating the air extremely. It is a small device that takes in air from one side, heats it, and then sends it out from the other side for it to circulate the room and warm the environment. It can heat the temperature to 30 degrees Celsius and can be controlled using a smart remote too. Valty Heater is affordable for people and is easy to use too.",
"### How is Valty Heater useful for people?\n\nValty Heater is a product that can be called the right innovation at the right time. People have been asking for things that they can keep with themselves at all times and are compact and can-do major works. Getting a heater which is the size of a small gadget and can be carried around anywhere is very important.\n\nValty Heater in CA, USA gives pleasure to people for getting their rooms heated up in 15 minutes with just a simple button touch on the console of this gadget. It has to be placed at one corner of the room and then turned on with a set temperature. It then ventilates hot air all around the room and makes sure that the complete room is warmed up. The best thing about this gadget is that it has a smart sensor that automatically turns it off as soon as the set temperature is set around the room. It is affordable for people and can be used very easily and thus has gained a lot of popularity. Valty Heater US, CA is, therefore, a very helpful product for students and corporate people who live alone and also for people who do not have heaters in their offices.",
"### \\=> (SPECIAL OFFER) Click Here To Buy Valty Heater From The Official Website!",
"### Valty Heater Specifications:\n\nThe portable heater is very efficient. Check all specs below:\n\n* 500W Ceramic Heater\n\n* Small & Portable\n\n* LED Display & Timer\n\n* Overheated Protection",
"### What is the price of Valty Heater?\n\nThe cost of Valty Heater in USA, Canada, is very affordable. You can get single Valty Heater is $69.99 only. You can get this portable heater in bulk with discount prices. Check packages below:\n\n2xValty Heater - $62.99/each\n\n3xValty Heater - $55.99/each (BEST SELLER)\n\n5xValty Heater - $48.99/each (MOST VALUED)",
"#### Valty Heater Official Website - URL",
"### What is the mechanism of Valty Heater?\n\nValty Heater has been made with the help of advanced machines and a lot of research overheat transfer. It is observable that hot air floats higher in the air and the colder air gets settled down and this is the reason why this gadget first sucks in air from the environment heats it and radiates it in the room with the help of its fan and then when that hot air gets higher in the room, it again sucks it in and radiates it around the room by further heating it. This way the air molecules supply thermal energy to neighbouring molecules and the whole room gets heated up very easily. The sensor used in this product helps the gadget to know what temperature is to be set around the room and when to turn it off.",
"### \\=> Buy Valty Heater Before Stock Runs Out!\n\nThe whole outer body is made of shockproof hard plastic and the inside is made from metal. There is a copper centre in the product which gets heated up by electricity and then heats the air. Valty Heater is therefore an amazing example of how the radiation of heat can be used effectively. It is thus suggested for people and is gaining popularity in the market too.\n\nCheck Valty Heater bewertungen, Valty Heater stromverbrauch, Valty Heater test Deutsch. Besuchen Sie uns hier und holen Sie sich ein tragbares Heizgerät von Valty und lesen Sie Rezensionen in deutscher Sprache.",
"### How to fit Valty Heater in the room?\n\nValty Heater Canada, USA is a compact gadget and people can easily use it. For installing the gadget, all that a user needs is a double-sided tape or a proper mount that is available separately for the gadget. That mount or the tape has to be stuck on the wall at one corner of the room and then the user has to just put the gadget on this mounting and make sure that it does not fall or sticks out. The product can be then controlled using the console given on it or the smart remote that is provided by the makers. The connection for this device is manual and the plug has to be put in a normal supply plug. Remote works with the help of 2 AA batteries that are removable and can be changed.",
"### Where to buy Valty Heater?\n\nThe most selling portable heater is available at the official website of the Valty Heater. People can go and order it at their address. Many payment options are available for the users to choose from.The Valty Heater is available in USA, Canada. Besuchen Sie die offizielle deutsche Website von Valty Heater and check Valty Heater erfahrungen, Valty Heater stiftung warentest.",
"### EXCLUSIVE OFFER \\*Now On Sale\\* Click Here to Buy Valty Heater at the Best Price Online",
"### Disclaimer:\n\nThis is promotional content. Must consult an expert before using the device. This post contains an affiliate link and we receive a commission on every sale from this post (at no cost to you). Check the final price on the official website. Read T&C carefully before making any purchase."
] |
[
"TAGS\n#region-us \n",
"### Device Name: Valty Heater\n\n Benefit: Easy to install and efficiently warm up the room. \n\n Specifications: 500W Ceramic Heater \n\n Small & Portable: LED Display & Timer \n\n Overheated Protection Guarantee: 14-days money back guarantee \n\n Price: $69.99 \n\n Rating: 4.8/5.0 \n\n Official Website: URL\n\n Country for Sale: USA, CA \n\n Availability: In Stock \n\n Discount: Extra discounts available on the website",
"### Valty Heater Reviews, USA, CA\n\nWinters can be extremely chilling for all the people who live any far from the equator. Winters are the time that people can go out in the snow, enjoy the time getting slower and also spend a lot of time with their family. But the only problem with this problem is the unbearable cold that comes along with it. At places, the temperature can even go in minus, and thus people must keep themselves warm as the body can catch flu very easily. Pneumonia and hypothermia are serious concerns related to winters too. The only way that the human body can survive without issues is if people keep themselves warm enough. People can wear a lot of winter clothes outside their home but at home, they need some kind of heater to get warm as wearing a lot of clothes at home are very uncomfortable for people. Conventional heaters or radiators take a lot of space and are very costly too. The energy consumption by such big appliances is a lot too. Thus, people try to find a better gadget that can keep them warm and is also affordable for them.",
"### \\=> Click Here To Get Valty Heater From The Official Website!\n\nValty Portable Heater in US, CA, the perfect product for people who want such a gadget that is small and can heat a room efficiently in very little time. This product makes use of heat radiation by heating the air extremely. It is a small device that takes in air from one side, heats it, and then sends it out from the other side for it to circulate the room and warm the environment. It can heat the temperature to 30 degrees Celsius and can be controlled using a smart remote too. Valty Heater is affordable for people and is easy to use too.",
"### How is Valty Heater useful for people?\n\nValty Heater is a product that can be called the right innovation at the right time. People have been asking for things that they can keep with themselves at all times and are compact and can-do major works. Getting a heater which is the size of a small gadget and can be carried around anywhere is very important.\n\nValty Heater in CA, USA gives pleasure to people for getting their rooms heated up in 15 minutes with just a simple button touch on the console of this gadget. It has to be placed at one corner of the room and then turned on with a set temperature. It then ventilates hot air all around the room and makes sure that the complete room is warmed up. The best thing about this gadget is that it has a smart sensor that automatically turns it off as soon as the set temperature is set around the room. It is affordable for people and can be used very easily and thus has gained a lot of popularity. Valty Heater US, CA is, therefore, a very helpful product for students and corporate people who live alone and also for people who do not have heaters in their offices.",
"### \\=> (SPECIAL OFFER) Click Here To Buy Valty Heater From The Official Website!",
"### Valty Heater Specifications:\n\nThe portable heater is very efficient. Check all specs below:\n\n* 500W Ceramic Heater\n\n* Small & Portable\n\n* LED Display & Timer\n\n* Overheated Protection",
"### What is the price of Valty Heater?\n\nThe cost of Valty Heater in USA, Canada, is very affordable. You can get single Valty Heater is $69.99 only. You can get this portable heater in bulk with discount prices. Check packages below:\n\n2xValty Heater - $62.99/each\n\n3xValty Heater - $55.99/each (BEST SELLER)\n\n5xValty Heater - $48.99/each (MOST VALUED)",
"#### Valty Heater Official Website - URL",
"### What is the mechanism of Valty Heater?\n\nValty Heater has been made with the help of advanced machines and a lot of research overheat transfer. It is observable that hot air floats higher in the air and the colder air gets settled down and this is the reason why this gadget first sucks in air from the environment heats it and radiates it in the room with the help of its fan and then when that hot air gets higher in the room, it again sucks it in and radiates it around the room by further heating it. This way the air molecules supply thermal energy to neighbouring molecules and the whole room gets heated up very easily. The sensor used in this product helps the gadget to know what temperature is to be set around the room and when to turn it off.",
"### \\=> Buy Valty Heater Before Stock Runs Out!\n\nThe whole outer body is made of shockproof hard plastic and the inside is made from metal. There is a copper centre in the product which gets heated up by electricity and then heats the air. Valty Heater is therefore an amazing example of how the radiation of heat can be used effectively. It is thus suggested for people and is gaining popularity in the market too.\n\nCheck Valty Heater bewertungen, Valty Heater stromverbrauch, Valty Heater test Deutsch. Besuchen Sie uns hier und holen Sie sich ein tragbares Heizgerät von Valty und lesen Sie Rezensionen in deutscher Sprache.",
"### How to fit Valty Heater in the room?\n\nValty Heater Canada, USA is a compact gadget and people can easily use it. For installing the gadget, all that a user needs is a double-sided tape or a proper mount that is available separately for the gadget. That mount or the tape has to be stuck on the wall at one corner of the room and then the user has to just put the gadget on this mounting and make sure that it does not fall or sticks out. The product can be then controlled using the console given on it or the smart remote that is provided by the makers. The connection for this device is manual and the plug has to be put in a normal supply plug. Remote works with the help of 2 AA batteries that are removable and can be changed.",
"### Where to buy Valty Heater?\n\nThe most selling portable heater is available at the official website of the Valty Heater. People can go and order it at their address. Many payment options are available for the users to choose from.The Valty Heater is available in USA, Canada. Besuchen Sie die offizielle deutsche Website von Valty Heater and check Valty Heater erfahrungen, Valty Heater stiftung warentest.",
"### EXCLUSIVE OFFER \\*Now On Sale\\* Click Here to Buy Valty Heater at the Best Price Online",
"### Disclaimer:\n\nThis is promotional content. Must consult an expert before using the device. This post contains an affiliate link and we receive a commission on every sale from this post (at no cost to you). Check the final price on the official website. Read T&C carefully before making any purchase."
] |
[
6,
94,
247,
145,
247,
26,
47,
111,
10,
175,
156,
174,
96,
27,
63
] |
[
"passage: TAGS\n#region-us \n### Device Name: Valty Heater\n\n Benefit: Easy to install and efficiently warm up the room. \n\n Specifications: 500W Ceramic Heater \n\n Small & Portable: LED Display & Timer \n\n Overheated Protection Guarantee: 14-days money back guarantee \n\n Price: $69.99 \n\n Rating: 4.8/5.0 \n\n Official Website: URL\n\n Country for Sale: USA, CA \n\n Availability: In Stock \n\n Discount: Extra discounts available on the website### Valty Heater Reviews, USA, CA\n\nWinters can be extremely chilling for all the people who live any far from the equator. Winters are the time that people can go out in the snow, enjoy the time getting slower and also spend a lot of time with their family. But the only problem with this problem is the unbearable cold that comes along with it. At places, the temperature can even go in minus, and thus people must keep themselves warm as the body can catch flu very easily. Pneumonia and hypothermia are serious concerns related to winters too. The only way that the human body can survive without issues is if people keep themselves warm enough. People can wear a lot of winter clothes outside their home but at home, they need some kind of heater to get warm as wearing a lot of clothes at home are very uncomfortable for people. Conventional heaters or radiators take a lot of space and are very costly too. The energy consumption by such big appliances is a lot too. Thus, people try to find a better gadget that can keep them warm and is also affordable for them.### \\=> Click Here To Get Valty Heater From The Official Website!\n\nValty Portable Heater in US, CA, the perfect product for people who want such a gadget that is small and can heat a room efficiently in very little time. This product makes use of heat radiation by heating the air extremely. It is a small device that takes in air from one side, heats it, and then sends it out from the other side for it to circulate the room and warm the environment. It can heat the temperature to 30 degrees Celsius and can be controlled using a smart remote too. Valty Heater is affordable for people and is easy to use too.",
"passage: ### How is Valty Heater useful for people?\n\nValty Heater is a product that can be called the right innovation at the right time. People have been asking for things that they can keep with themselves at all times and are compact and can-do major works. Getting a heater which is the size of a small gadget and can be carried around anywhere is very important.\n\nValty Heater in CA, USA gives pleasure to people for getting their rooms heated up in 15 minutes with just a simple button touch on the console of this gadget. It has to be placed at one corner of the room and then turned on with a set temperature. It then ventilates hot air all around the room and makes sure that the complete room is warmed up. The best thing about this gadget is that it has a smart sensor that automatically turns it off as soon as the set temperature is set around the room. It is affordable for people and can be used very easily and thus has gained a lot of popularity. Valty Heater US, CA is, therefore, a very helpful product for students and corporate people who live alone and also for people who do not have heaters in their offices.### \\=> (SPECIAL OFFER) Click Here To Buy Valty Heater From The Official Website!### Valty Heater Specifications:\n\nThe portable heater is very efficient. Check all specs below:\n\n* 500W Ceramic Heater\n\n* Small & Portable\n\n* LED Display & Timer\n\n* Overheated Protection### What is the price of Valty Heater?\n\nThe cost of Valty Heater in USA, Canada, is very affordable. You can get single Valty Heater is $69.99 only. You can get this portable heater in bulk with discount prices. Check packages below:\n\n2xValty Heater - $62.99/each\n\n3xValty Heater - $55.99/each (BEST SELLER)\n\n5xValty Heater - $48.99/each (MOST VALUED)#### Valty Heater Official Website - URL### What is the mechanism of Valty Heater?\n\nValty Heater has been made with the help of advanced machines and a lot of research overheat transfer. It is observable that hot air floats higher in the air and the colder air gets settled down and this is the reason why this gadget first sucks in air from the environment heats it and radiates it in the room with the help of its fan and then when that hot air gets higher in the room, it again sucks it in and radiates it around the room by further heating it. This way the air molecules supply thermal energy to neighbouring molecules and the whole room gets heated up very easily. The sensor used in this product helps the gadget to know what temperature is to be set around the room and when to turn it off."
] |
04d41b4ce83c43149c5ad6462262ff3b8452c06e
|
# Dataset Card for "2f525ab2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/2f525ab2
|
[
"region:us"
] |
2023-10-18T06:34:13+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 242, "num_examples": 10}], "download_size": 1429, "dataset_size": 242}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T06:34:14+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "2f525ab2"
More Information needed
|
[
"# Dataset Card for \"2f525ab2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"2f525ab2\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"2f525ab2\"\n\nMore Information needed"
] |
4e79bbc8cff3c9cd75a3c0589fb2d346818431e7
|
Published Paper Information:>>>>>>>>>>>>>>>>>>>>>>>>
If you use CBDCB dataset, please cite the following paper:
```
@article{mahmud2023cyberbullying,
title={Cyberbullying detection for low-resource languages and dialects: Review of the state of the art},
author={Mahmud, Tanjim and Ptaszynski, Michal and Eronen, Juuso and Masui, Fumito},
journal={Information Processing \& Management},
volume={60},
number={5},
pages={103454},
year={2023},
publisher={Elsevier}
}
```
|
TanjimKIT/Cyberbullying-detection-in-Chittagonian-dialect-of-Bangla-CBDCB
|
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:bn",
"license:apache-2.0",
"doi:10.57967/hf/1225",
"region:us"
] |
2023-10-18T08:01:57+00:00
|
{"language": ["bn"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "CBDCB"}
|
2023-10-25T08:26:41+00:00
|
[] |
[
"bn"
] |
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Bengali #license-apache-2.0 #doi-10.57967/hf/1225 #region-us
|
Published Paper Information:>>>>>>>>>>>>>>>>>>>>>>>>
If you use CBDCB dataset, please cite the following paper:
|
[] |
[
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Bengali #license-apache-2.0 #doi-10.57967/hf/1225 #region-us \n"
] |
[
54
] |
[
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Bengali #license-apache-2.0 #doi-10.57967/hf/1225 #region-us \n"
] |
ec9d3b627f79c25fd746a0d4d1035003609a6058
|
# Dataset Card for "data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
promptora11/dataa
|
[
"region:us"
] |
2023-10-18T08:31:37+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 450321, "num_examples": 200}], "download_size": 287175, "dataset_size": 450321}}
|
2023-10-18T08:31:40+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data"
More Information needed
|
[
"# Dataset Card for \"data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data\"\n\nMore Information needed"
] |
07cca1126fcc3311db945419f6cbd069955b8a1a
|
# Dataset Card for "data1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
promptora11/data1
|
[
"region:us"
] |
2023-10-18T08:35:31+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9403, "num_examples": 112}], "download_size": 2356, "dataset_size": 9403}}
|
2023-10-18T08:35:33+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data1"
More Information needed
|
[
"# Dataset Card for \"data1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data1\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data1\"\n\nMore Information needed"
] |
729a219f3b48679576e2878bf4ed294f2ac349f7
|
# Dataset Card for "usmle_self_assesment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HuggingSara/usmle_self_assessment
|
[
"region:us"
] |
2023-10-18T08:37:03+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "options", "struct": [{"name": "A", "dtype": "string"}, {"name": "B", "dtype": "string"}, {"name": "C", "dtype": "string"}, {"name": "D", "dtype": "string"}, {"name": "E", "dtype": "string"}, {"name": "F", "dtype": "string"}, {"name": "G", "dtype": "string"}, {"name": "H", "dtype": "string"}, {"name": "I", "dtype": "string"}]}, {"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer_idx", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 372032, "num_examples": 325}], "download_size": 213238, "dataset_size": 372032}}
|
2023-10-18T08:37:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "usmle_self_assesment"
More Information needed
|
[
"# Dataset Card for \"usmle_self_assesment\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"usmle_self_assesment\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"usmle_self_assesment\"\n\nMore Information needed"
] |
83404d3def1c4a692b100e37bb06e07cb604f84b
|
# Dataset Card for Evaluation run of h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt
- **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 [h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T10:05:46.000869](https://huggingface.co/datasets/open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt/blob/main/results_2023-10-18T10-05-46.000869.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": 0.09406459731543625,
"em_stderr": 0.0029895193407219744,
"f1": 0.1653156459731545,
"f1_stderr": 0.003297300596545349,
"acc": 0.27466456195737965,
"acc_stderr": 0.00699196443452012
},
"harness|drop|3": {
"em": 0.09406459731543625,
"em_stderr": 0.0029895193407219744,
"f1": 0.1653156459731545,
"f1_stderr": 0.003297300596545349
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5493291239147593,
"acc_stderr": 0.01398392886904024
}
}
```
### 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_h2oai__h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt
|
[
"region:us"
] |
2023-10-18T09:05:49+00:00
|
{"pretty_name": "Evaluation run of h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt", "dataset_summary": "Dataset automatically created during the evaluation run of model [h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T10:05:46.000869](https://huggingface.co/datasets/open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt/blob/main/results_2023-10-18T10-05-46.000869.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\": 0.09406459731543625,\n \"em_stderr\": 0.0029895193407219744,\n \"f1\": 0.1653156459731545,\n \"f1_stderr\": 0.003297300596545349,\n \"acc\": 0.27466456195737965,\n \"acc_stderr\": 0.00699196443452012\n },\n \"harness|drop|3\": {\n \"em\": 0.09406459731543625,\n \"em_stderr\": 0.0029895193407219744,\n \"f1\": 0.1653156459731545,\n \"f1_stderr\": 0.003297300596545349\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5493291239147593,\n \"acc_stderr\": 0.01398392886904024\n }\n}\n```", "repo_url": "https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T10_05_46.000869", "path": ["**/details_harness|drop|3_2023-10-18T10-05-46.000869.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T10-05-46.000869.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T10_05_46.000869", "path": ["**/details_harness|gsm8k|5_2023-10-18T10-05-46.000869.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T10-05-46.000869.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T10_05_46.000869", "path": ["**/details_harness|winogrande|5_2023-10-18T10-05-46.000869.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T10-05-46.000869.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T10_05_46.000869", "path": ["results_2023-10-18T10-05-46.000869.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T10-05-46.000869.parquet"]}]}]}
|
2023-10-18T09:05:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T10:05:46.000869(note that their might be results for other tasks in 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 h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt",
"## 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 h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T10:05:46.000869(note that their might be results for other tasks in 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 h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt",
"## 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 h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T10:05:46.000869(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
41,
31,
189,
65,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt## 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 h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T10:05:46.000869(note that their might be results for other tasks in 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"
] |
2645218cd45ba45e9d0178f7e9fd98380f2cb95d
|
# Dataset Card for "daily_dialog_gloss_FINAL"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/daily_dialog_gloss_FINAL
|
[
"region:us"
] |
2023-10-18T09:06:00+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gloss", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11195623, "num_examples": 77350}], "download_size": 7028108, "dataset_size": 11195623}}
|
2023-10-18T09:06:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "daily_dialog_gloss_FINAL"
More Information needed
|
[
"# Dataset Card for \"daily_dialog_gloss_FINAL\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"daily_dialog_gloss_FINAL\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"daily_dialog_gloss_FINAL\"\n\nMore Information needed"
] |
fc88c6425932cd25031ec930a819beb5d7fbd6c1
|
The datasets used in this competition come from many open-source databases and two manually written files, which We wrote by ourselves.
## manually written files
#### helpless_answer.txt
When human ask a meaningless question, the model will give a helpless reply.
We create this kind of chats by creating valid questions and answers from "helpless_answer.txt".
#### identity_chat.txt
The self-identity of our model should not be the same as its origin base model or other well-known models.
|
SalarMaca/nips2023_dataset
|
[
"license:mit",
"region:us"
] |
2023-10-18T09:14:15+00:00
|
{"license": "mit"}
|
2023-10-26T08:32:20+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
The datasets used in this competition come from many open-source databases and two manually written files, which We wrote by ourselves.
## manually written files
#### helpless_answer.txt
When human ask a meaningless question, the model will give a helpless reply.
We create this kind of chats by creating valid questions and answers from "helpless_answer.txt".
#### identity_chat.txt
The self-identity of our model should not be the same as its origin base model or other well-known models.
|
[
"## manually written files",
"#### helpless_answer.txt \nWhen human ask a meaningless question, the model will give a helpless reply.\nWe create this kind of chats by creating valid questions and answers from \"helpless_answer.txt\".",
"#### identity_chat.txt\nThe self-identity of our model should not be the same as its origin base model or other well-known models."
] |
[
"TAGS\n#license-mit #region-us \n",
"## manually written files",
"#### helpless_answer.txt \nWhen human ask a meaningless question, the model will give a helpless reply.\nWe create this kind of chats by creating valid questions and answers from \"helpless_answer.txt\".",
"#### identity_chat.txt\nThe self-identity of our model should not be the same as its origin base model or other well-known models."
] |
[
11,
5,
50,
33
] |
[
"passage: TAGS\n#license-mit #region-us \n## manually written files#### helpless_answer.txt \nWhen human ask a meaningless question, the model will give a helpless reply.\nWe create this kind of chats by creating valid questions and answers from \"helpless_answer.txt\".#### identity_chat.txt\nThe self-identity of our model should not be the same as its origin base model or other well-known models."
] |
be5ec0a7f53abe820f023927c84e000c7a9a5591
|
# Dataset Card for "gender-DEI-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
rehanbrr/gender-DEI-data
|
[
"region:us"
] |
2023-10-18T09:25:08+00:00
|
{"dataset_info": {"features": [{"name": "doi", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "chunk_id", "dtype": "string"}, {"name": "chunk", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 235089, "num_examples": 156}], "download_size": 130544, "dataset_size": 235089}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T09:25:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "gender-DEI-data"
More Information needed
|
[
"# Dataset Card for \"gender-DEI-data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"gender-DEI-data\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"gender-DEI-data\"\n\nMore Information needed"
] |
7ff1020c506c2572d8a8df16812a25717e7bb813
|
# Dataset Card for "prm800k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
SUSTech/prm800k
|
[
"region:us"
] |
2023-10-18T09:45:15+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "solution", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13892738, "num_examples": 16830}, {"name": "test", "num_bytes": 914286, "num_examples": 976}], "download_size": 6870174, "dataset_size": 14807024}}
|
2023-12-03T09:56:34+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prm800k"
More Information needed
|
[
"# Dataset Card for \"prm800k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prm800k\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prm800k\"\n\nMore Information needed"
] |
57767c3475e2e3db2d8a006368e9a3b55f67dfd0
|
# Dataset Card for "minipile"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pietrolesci/minipile
|
[
"region:us"
] |
2023-10-18T10:07:49+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "test", "path": "data/test-*"}, {"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}, {"config_name": "pietrolesci__gpt2-minipile", "data_files": [{"split": "test", "path": "pietrolesci__gpt2-minipile/test-*"}, {"split": "train", "path": "pietrolesci__gpt2-minipile/train-*"}, {"split": "validation", "path": "pietrolesci__gpt2-minipile/validation-*"}]}], "dataset_info": [{"config_name": "default", "features": [{"name": "text", "dtype": "string"}, {"name": "uid", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 58638191, "num_examples": 10000}, {"name": "train", "num_bytes": 5914108510, "num_examples": 1000000}, {"name": "validation", "num_bytes": 2783386, "num_examples": 500}], "download_size": 3181931245, "dataset_size": 5975530087}, {"config_name": "pietrolesci__gpt2-minipile", "features": [{"name": "input_ids", "sequence": "int32"}, {"name": "uid", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 60100040, "num_examples": 14630}, {"name": "train", "num_bytes": 6105868288, "num_examples": 1486336}, {"name": "validation", "num_bytes": 2834520, "num_examples": 690}], "download_size": 2991905647, "dataset_size": 6168802848}]}
|
2023-10-18T10:32:37+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "minipile"
More Information needed
|
[
"# Dataset Card for \"minipile\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"minipile\"\n\nMore Information needed"
] |
[
6,
13
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"minipile\"\n\nMore Information needed"
] |
dd2cece3d246de8e78039760925d3fb6eb5cc657
|
# Dataset Card for "nadsoft-jo-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Ahmed007/nadsoft-jo-data
|
[
"region:us"
] |
2023-10-18T10:17:12+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 608242960.1156812, "num_examples": 4539}, {"name": "test", "num_bytes": 67671893.9183188, "num_examples": 505}], "download_size": 661582950, "dataset_size": 675914854.0339999}}
|
2023-10-18T10:17:39+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "nadsoft-jo-data"
More Information needed
|
[
"# Dataset Card for \"nadsoft-jo-data\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"nadsoft-jo-data\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"nadsoft-jo-data\"\n\nMore Information needed"
] |
3cef516e0292024afcc98bfcfe2e303f88c13f9f
|
# Dataset Card for esme
The dataset contains problem descriptions and code in python language.
This dataset is taken from [subinsoman/esme](https://huggingface.co/datasets/subin/esme), which adds a prompt column in alpaca style. Refer to the source [here](https://huggingface.co/datasets/subin/esme).
|
subinsoman/esme
|
[
"task_categories:question-answering",
"task_categories:text2text-generation",
"task_categories:text-generation",
"size_categories:1K<n<10K",
"code",
"region:us"
] |
2023-10-18T11:52:57+00:00
|
{"size_categories": ["1K<n<10K"], "task_categories": ["question-answering", "text2text-generation", "text-generation"], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["code"]}
|
2023-10-19T04:23:07+00:00
|
[] |
[] |
TAGS
#task_categories-question-answering #task_categories-text2text-generation #task_categories-text-generation #size_categories-1K<n<10K #code #region-us
|
# Dataset Card for esme
The dataset contains problem descriptions and code in python language.
This dataset is taken from subinsoman/esme, which adds a prompt column in alpaca style. Refer to the source here.
|
[
"# Dataset Card for esme\n\nThe dataset contains problem descriptions and code in python language.\nThis dataset is taken from subinsoman/esme, which adds a prompt column in alpaca style. Refer to the source here."
] |
[
"TAGS\n#task_categories-question-answering #task_categories-text2text-generation #task_categories-text-generation #size_categories-1K<n<10K #code #region-us \n",
"# Dataset Card for esme\n\nThe dataset contains problem descriptions and code in python language.\nThis dataset is taken from subinsoman/esme, which adds a prompt column in alpaca style. Refer to the source here."
] |
[
56,
56
] |
[
"passage: TAGS\n#task_categories-question-answering #task_categories-text2text-generation #task_categories-text-generation #size_categories-1K<n<10K #code #region-us \n# Dataset Card for esme\n\nThe dataset contains problem descriptions and code in python language.\nThis dataset is taken from subinsoman/esme, which adds a prompt column in alpaca style. Refer to the source here."
] |
4915d33dd260b082487e620694fdbce0619b1e22
|
# Dataset Card for BUSTER
BUSiness Transaction Entity Recognition dataset.
BUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of
3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,
plus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system.
|
expertai/BUSTER
|
[
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"finance",
"region:us"
] |
2023-10-18T12:03:49+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "pretty_name": "buster", "tags": ["finance"], "configs": [{"config_name": "default", "data_files": [{"split": "FOLD_1", "path": "data/FOLD_1-*"}, {"split": "FOLD_2", "path": "data/FOLD_2-*"}, {"split": "FOLD_3", "path": "data/FOLD_3-*"}, {"split": "FOLD_4", "path": "data/FOLD_4-*"}, {"split": "FOLD_5", "path": "data/FOLD_5-*"}, {"split": "SILVER", "path": "data/SILVER-*"}]}], "dataset_info": {"features": [{"name": "document_id", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}], "splits": [{"name": "FOLD_1", "num_bytes": 13597946, "num_examples": 753}, {"name": "FOLD_2", "num_bytes": 13477878, "num_examples": 759}, {"name": "FOLD_3", "num_bytes": 13602552, "num_examples": 758}, {"name": "FOLD_4", "num_bytes": 13834760, "num_examples": 755}, {"name": "FOLD_5", "num_bytes": 13632431, "num_examples": 754}, {"name": "SILVER", "num_bytes": 108914416, "num_examples": 6196}], "download_size": 0, "dataset_size": 177059983}}
|
2023-12-22T09:15:39+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #finance #region-us
|
# Dataset Card for BUSTER
BUSiness Transaction Entity Recognition dataset.
BUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of
3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,
plus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system.
|
[
"# Dataset Card for BUSTER\nBUSiness Transaction Entity Recognition dataset. \n\nBUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of \n3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,\nplus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system."
] |
[
"TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #finance #region-us \n",
"# Dataset Card for BUSTER\nBUSiness Transaction Entity Recognition dataset. \n\nBUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of \n3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,\nplus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system."
] |
[
45,
102
] |
[
"passage: TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #finance #region-us \n# Dataset Card for BUSTER\nBUSiness Transaction Entity Recognition dataset. \n\nBUSTER is an Entity Recognition (ER) benchmark for entities related to business transactions. It consists of a gold corpus of \n3779 manually annotated documents on financial transactions that were randomly divided into 5 folds,\nplus an additional silver corpus of 6196 automatically annotated documents that were created by the model-optimized RoBERTa system."
] |
f8ffc0085c9c773d98de7efe56b3b2df4c2a0083
|
# Dataset Card for "WikiMedical_sentence_similarity"
WikiMedical_sentence_similarity is an adapted and ready-to-use sentence similarity dataset based on [this dataset](https://huggingface.co/datasets/gamino/wiki_medical_terms).
The preprocessing followed three steps:
- Each text is splitted into sentences of 256 tokens (nltk tokenizer)
- Each sentence is paired with a positive pair if found, and a negative one. Negative one are drawn randomly in the whole dataset.
- Train and test split correspond to 70%/30%
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
nuvocare/WikiMedical_sentence_similarity
|
[
"region:us"
] |
2023-10-18T12:05:08+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text1", "dtype": "string"}, {"name": "text2", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "-1", "1": "1"}}}}], "splits": [{"name": "train", "num_bytes": 150266647.47592032, "num_examples": 50712}, {"name": "test", "num_bytes": 64403801.52407967, "num_examples": 21735}], "download_size": 129675237, "dataset_size": 214670449.0}}
|
2023-10-18T12:19:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "WikiMedical_sentence_similarity"
WikiMedical_sentence_similarity is an adapted and ready-to-use sentence similarity dataset based on this dataset.
The preprocessing followed three steps:
- Each text is splitted into sentences of 256 tokens (nltk tokenizer)
- Each sentence is paired with a positive pair if found, and a negative one. Negative one are drawn randomly in the whole dataset.
- Train and test split correspond to 70%/30%
More Information needed
|
[
"# Dataset Card for \"WikiMedical_sentence_similarity\"\n\nWikiMedical_sentence_similarity is an adapted and ready-to-use sentence similarity dataset based on this dataset.\n\nThe preprocessing followed three steps:\n- Each text is splitted into sentences of 256 tokens (nltk tokenizer)\n- Each sentence is paired with a positive pair if found, and a negative one. Negative one are drawn randomly in the whole dataset.\n- Train and test split correspond to 70%/30%\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"WikiMedical_sentence_similarity\"\n\nWikiMedical_sentence_similarity is an adapted and ready-to-use sentence similarity dataset based on this dataset.\n\nThe preprocessing followed three steps:\n- Each text is splitted into sentences of 256 tokens (nltk tokenizer)\n- Each sentence is paired with a positive pair if found, and a negative one. Negative one are drawn randomly in the whole dataset.\n- Train and test split correspond to 70%/30%\n\nMore Information needed"
] |
[
6,
122
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"WikiMedical_sentence_similarity\"\n\nWikiMedical_sentence_similarity is an adapted and ready-to-use sentence similarity dataset based on this dataset.\n\nThe preprocessing followed three steps:\n- Each text is splitted into sentences of 256 tokens (nltk tokenizer)\n- Each sentence is paired with a positive pair if found, and a negative one. Negative one are drawn randomly in the whole dataset.\n- Train and test split correspond to 70%/30%\n\nMore Information needed"
] |
68e7fb5e30da4ac6a72a359abebb9c76d3070df1
|
# Dataset Card for "guanaco-llama2-chinese-1ka"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
elliotthwang/guanaco-llama2-chinese-1ka
|
[
"region:us"
] |
2023-10-18T12:33:01+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1348677, "num_examples": 1000}], "download_size": 811412, "dataset_size": 1348677}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T12:33:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "guanaco-llama2-chinese-1ka"
More Information needed
|
[
"# Dataset Card for \"guanaco-llama2-chinese-1ka\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"guanaco-llama2-chinese-1ka\"\n\nMore Information needed"
] |
[
6,
21
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"guanaco-llama2-chinese-1ka\"\n\nMore Information needed"
] |
2371eb7c120dc2a5dde1abe018be983d0cf8c51b
|
# Dataset Card for "codealpaca-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
antolin/codealpaca-filtered
|
[
"region:us"
] |
2023-10-18T12:54:18+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "valid", "path": "data/valid-*"}]}], "dataset_info": {"features": [{"name": "nl", "dtype": "string"}, {"name": "cmd", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 611759.1279592449, "num_examples": 2194}, {"name": "test", "num_bytes": 175106.98831285586, "num_examples": 628}, {"name": "valid", "num_bytes": 87553.49415642793, "num_examples": 314}], "download_size": 447020, "dataset_size": 874419.6104285286}}
|
2023-10-20T11:39:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "codealpaca-filtered"
More Information needed
|
[
"# Dataset Card for \"codealpaca-filtered\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"codealpaca-filtered\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"codealpaca-filtered\"\n\nMore Information needed"
] |
1101ff5b59194f3f184b7ce972c5e4f90c541818
|
# Dataset Card for "mistral_vs_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
argilla/mistral_vs_llama2
|
[
"region:us"
] |
2023-10-18T13:02:11+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "activate_my_card", "1": "age_limit", "2": "apple_pay_or_google_pay", "3": "atm_support", "4": "automatic_top_up", "5": "balance_not_updated_after_bank_transfer", "6": "balance_not_updated_after_cheque_or_cash_deposit", "7": "beneficiary_not_allowed", "8": "cancel_transfer", "9": "card_about_to_expire", "10": "card_acceptance", "11": "card_arrival", "12": "card_delivery_estimate", "13": "card_linking", "14": "card_not_working", "15": "card_payment_fee_charged", "16": "card_payment_not_recognised", "17": "card_payment_wrong_exchange_rate", "18": "card_swallowed", "19": "cash_withdrawal_charge", "20": "cash_withdrawal_not_recognised", "21": "change_pin", "22": "compromised_card", "23": "contactless_not_working", "24": "country_support", "25": "declined_card_payment", "26": "declined_cash_withdrawal", "27": "declined_transfer", "28": "direct_debit_payment_not_recognised", "29": "disposable_card_limits", "30": "edit_personal_details", "31": "exchange_charge", "32": "exchange_rate", "33": "exchange_via_app", "34": "extra_charge_on_statement", "35": "failed_transfer", "36": "fiat_currency_support", "37": "get_disposable_virtual_card", "38": "get_physical_card", "39": "getting_spare_card", "40": "getting_virtual_card", "41": "lost_or_stolen_card", "42": "lost_or_stolen_phone", "43": "order_physical_card", "44": "passcode_forgotten", "45": "pending_card_payment", "46": "pending_cash_withdrawal", "47": "pending_top_up", "48": "pending_transfer", "49": "pin_blocked", "50": "receiving_money", "51": "Refund_not_showing_up", "52": "request_refund", "53": "reverted_card_payment?", "54": "supported_cards_and_currencies", "55": "terminate_account", "56": "top_up_by_bank_transfer_charge", "57": "top_up_by_card_charge", "58": "top_up_by_cash_or_cheque", "59": "top_up_failed", "60": "top_up_limits", "61": "top_up_reverted", "62": "topping_up_by_card", "63": "transaction_charged_twice", "64": "transfer_fee_charged", "65": "transfer_into_account", "66": "transfer_not_received_by_recipient", "67": "transfer_timing", "68": "unable_to_verify_identity", "69": "verify_my_identity", "70": "verify_source_of_funds", "71": "verify_top_up", "72": "virtual_card_not_working", "73": "visa_or_mastercard", "74": "why_verify_identity", "75": "wrong_amount_of_cash_received", "76": "wrong_exchange_rate_for_cash_withdrawal"}}}}, {"name": "response", "dtype": "string"}, {"name": "response_mistral", "dtype": "string"}, {"name": "responses", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 348336, "num_examples": 100}], "download_size": 164598, "dataset_size": 348336}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T13:02:13+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "mistral_vs_llama2"
More Information needed
|
[
"# Dataset Card for \"mistral_vs_llama2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"mistral_vs_llama2\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"mistral_vs_llama2\"\n\nMore Information needed"
] |
1d00e56d505e2db9d16fcfa5209d9d70b78c6d5e
|
# Dataset Card for "tokenized_gpt2_context_len_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/tokenized_gpt2_context_len_64
|
[
"region:us"
] |
2023-10-18T13:40:59+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 8074990564, "num_examples": 80462898}], "download_size": 3552230822, "dataset_size": 8074990564}}
|
2023-10-18T13:49:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "tokenized_gpt2_context_len_64"
More Information needed
|
[
"# Dataset Card for \"tokenized_gpt2_context_len_64\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"tokenized_gpt2_context_len_64\"\n\nMore Information needed"
] |
[
6,
24
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"tokenized_gpt2_context_len_64\"\n\nMore Information needed"
] |
77a7794ad7cfdfb05dd9bb26278066d4c5f9c787
|
# Parquet_Files
# Cross Language (CL) Datasets
Four datasets of language pair translations originating from CORDIS Project News (https://elrc-share.eu/)
```
Structured as follows:
| Field | Description |
| --------------- | ----------------------------------------------------------------------- |
| de/es/fr/it | Non-English transcripts of sentences |
| en | English translations of sentences |
120k rows (approx. per parquet file)
```
# Characters.parquet
One large dataset of synthetic characters, information regarding them and their outputs. Created by combining other synthetic character datasets which can be found at: https://shorturl.at/gjIJ5, https://shorturl.at/DFQT6 & https://shorturl.at/tHX68.
```
Structured as follows:
| Field | Description |
| -------------- | --------------------------------------------------------------- |
| name | Character name |
| categories | Categories or genres associated with the character (array) |
| personalities | Personality traits and characteristics of the character (array) |
| description | Description of the character |
| conversation | Conversational interactions involving the character (array) |
17.6k rows
```
# Flores7Lang.parquet
Seven languages merged from the Flores-200 Dataset (https://github.com/facebookresearch/flores/blob/main/flores200/README.md)
```
Structured as follows:
| Field | Description |
| --------------- | ----------------------------------------------------- |
| deu | Sentence translated into German |
| eng | Sentence translated into English |
| epo | Sentence translated into Esperanto |
| fra | Sentence translated into French |
| ita | Sentence translated into Italian |
| spa | Sentence translated into Spanish |
| tur | Sentence translated into Turkish |
2.1k rows
```
# Fraud_detection.parquet
A synthetic financial dataset intended for Fraud detection tasks (https://www.kaggle.com/datasets/ealaxi/paysim1).
```
Structured as follows:
| Field | Description |
| --------------- | ------------------------------------------------------------- |
| Step | Maps a unit of time in the real world (1 step = 1 hour) |
| Type | CASH-IN, CASH-OUT, DEBIT, PAYMENT, or TRANSFER |
| Amount | Amount of the transaction in local currency |
| nameOrig | Customer who initiated the transaction |
| oldbalanceOrg | Initial balance before the transaction |
| newbalanceOrig | Customer's balance after the transaction |
| nameDest | Recipient ID of the transaction |
| oldbalanceDest | Initial recipient balance before the transaction |
| newbalanceDest | Recipient's balance after the transaction |
| isFraud | Identifies a fraudulent transaction (1) or non-fraudulent (0) |
2.27m rows
```
|
iix/Parquet_FIles
|
[
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:en",
"license:mit",
"code",
"region:us"
] |
2023-10-18T13:46:47+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification", "text-generation"], "pretty_name": "*", "tags": ["code"]}
|
2023-10-27T13:32:52+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-classification #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #code #region-us
|
# Parquet_Files
# Cross Language (CL) Datasets
Four datasets of language pair translations originating from CORDIS Project News (URL
# Characters.parquet
One large dataset of synthetic characters, information regarding them and their outputs. Created by combining other synthetic character datasets which can be found at: URL URL & URL
# Flores7Lang.parquet
Seven languages merged from the Flores-200 Dataset (URL
# Fraud_detection.parquet
A synthetic financial dataset intended for Fraud detection tasks (URL
|
[
"# Parquet_Files",
"# Cross Language (CL) Datasets\n\nFour datasets of language pair translations originating from CORDIS Project News (URL",
"# Characters.parquet\n\nOne large dataset of synthetic characters, information regarding them and their outputs. Created by combining other synthetic character datasets which can be found at: URL URL & URL",
"# Flores7Lang.parquet\n\nSeven languages merged from the Flores-200 Dataset (URL",
"# Fraud_detection.parquet\n\nA synthetic financial dataset intended for Fraud detection tasks (URL"
] |
[
"TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #code #region-us \n",
"# Parquet_Files",
"# Cross Language (CL) Datasets\n\nFour datasets of language pair translations originating from CORDIS Project News (URL",
"# Characters.parquet\n\nOne large dataset of synthetic characters, information regarding them and their outputs. Created by combining other synthetic character datasets which can be found at: URL URL & URL",
"# Flores7Lang.parquet\n\nSeven languages merged from the Flores-200 Dataset (URL",
"# Fraud_detection.parquet\n\nA synthetic financial dataset intended for Fraud detection tasks (URL"
] |
[
51,
6,
27,
49,
21,
26
] |
[
"passage: TAGS\n#task_categories-text-classification #task_categories-text-generation #size_categories-1M<n<10M #language-English #license-mit #code #region-us \n# Parquet_Files# Cross Language (CL) Datasets\n\nFour datasets of language pair translations originating from CORDIS Project News (URL# Characters.parquet\n\nOne large dataset of synthetic characters, information regarding them and their outputs. Created by combining other synthetic character datasets which can be found at: URL URL & URL# Flores7Lang.parquet\n\nSeven languages merged from the Flores-200 Dataset (URL# Fraud_detection.parquet\n\nA synthetic financial dataset intended for Fraud detection tasks (URL"
] |
af91b666accaffbb57b9796f35fea33035d815a1
|
# Dataset Card for "processed_gpt2_context_len_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yardeny/processed_gpt2_context_len_64
|
[
"region:us"
] |
2023-10-18T13:54:49+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 7604038432.0, "num_examples": 23183044}], "download_size": 3576830919, "dataset_size": 7604038432.0}}
|
2023-10-18T14:09:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "processed_gpt2_context_len_64"
More Information needed
|
[
"# Dataset Card for \"processed_gpt2_context_len_64\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_gpt2_context_len_64\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_gpt2_context_len_64\"\n\nMore Information needed"
] |
68f781c7b49757cd0a8176ac40304814fc4113e5
|
sshsbamboogroove crawled dataset
---
license: apache-2.0
---
|
lukekim420/sshsbamboobot
|
[
"language:ko",
"region:us"
] |
2023-10-18T14:09:08+00:00
|
{"language": ["ko"]}
|
2023-10-18T14:11:31+00:00
|
[] |
[
"ko"
] |
TAGS
#language-Korean #region-us
|
sshsbamboogroove crawled dataset
---
license: apache-2.0
---
|
[] |
[
"TAGS\n#language-Korean #region-us \n"
] |
[
11
] |
[
"passage: TAGS\n#language-Korean #region-us \n"
] |
944a72e45a892d2b8b3c376f159cef6f687d9cec
|
# Benin, Madrid Image Dataset
## Description
This dataset comprises images sourced from the [beninmadrid Instagram page](https://www.instagram.com/beninmadrid/) and is intended to serve as a challenging and intriguing dataset for testing visual language models and large multimodal language models. The images in this dataset are characterized by their unique artistic style and complexity, which can provide a robust test for the capabilities of modern AI models.
## Usage
This dataset is intended for research purposes, specifically the evaluation of visual and multimodal language models.
## Structure
- Each entry in the dataset is an image without any annotation or category.
## License
This dataset is made available under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
|
taesiri/beninmadrid
|
[
"task_categories:image-to-text",
"size_categories:1K<n<10K",
"license:cc-by-4.0",
"art",
"region:us"
] |
2023-10-18T14:18:20+00:00
|
{"license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["image-to-text"], "pretty_name": "Benin, Madrid", "dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "validation", "num_bytes": 918214940.525, "num_examples": 9105}], "download_size": 1407147767, "dataset_size": 918214940.525}, "tags": ["art"]}
|
2023-10-18T14:27:18+00:00
|
[] |
[] |
TAGS
#task_categories-image-to-text #size_categories-1K<n<10K #license-cc-by-4.0 #art #region-us
|
# Benin, Madrid Image Dataset
## Description
This dataset comprises images sourced from the beninmadrid Instagram page and is intended to serve as a challenging and intriguing dataset for testing visual language models and large multimodal language models. The images in this dataset are characterized by their unique artistic style and complexity, which can provide a robust test for the capabilities of modern AI models.
## Usage
This dataset is intended for research purposes, specifically the evaluation of visual and multimodal language models.
## Structure
- Each entry in the dataset is an image without any annotation or category.
## License
This dataset is made available under a Creative Commons Attribution 4.0 International License.
|
[
"# Benin, Madrid Image Dataset",
"## Description\nThis dataset comprises images sourced from the beninmadrid Instagram page and is intended to serve as a challenging and intriguing dataset for testing visual language models and large multimodal language models. The images in this dataset are characterized by their unique artistic style and complexity, which can provide a robust test for the capabilities of modern AI models.",
"## Usage\nThis dataset is intended for research purposes, specifically the evaluation of visual and multimodal language models.",
"## Structure\n- Each entry in the dataset is an image without any annotation or category.",
"## License\nThis dataset is made available under a Creative Commons Attribution 4.0 International License."
] |
[
"TAGS\n#task_categories-image-to-text #size_categories-1K<n<10K #license-cc-by-4.0 #art #region-us \n",
"# Benin, Madrid Image Dataset",
"## Description\nThis dataset comprises images sourced from the beninmadrid Instagram page and is intended to serve as a challenging and intriguing dataset for testing visual language models and large multimodal language models. The images in this dataset are characterized by their unique artistic style and complexity, which can provide a robust test for the capabilities of modern AI models.",
"## Usage\nThis dataset is intended for research purposes, specifically the evaluation of visual and multimodal language models.",
"## Structure\n- Each entry in the dataset is an image without any annotation or category.",
"## License\nThis dataset is made available under a Creative Commons Attribution 4.0 International License."
] |
[
41,
8,
78,
25,
21,
17
] |
[
"passage: TAGS\n#task_categories-image-to-text #size_categories-1K<n<10K #license-cc-by-4.0 #art #region-us \n# Benin, Madrid Image Dataset## Description\nThis dataset comprises images sourced from the beninmadrid Instagram page and is intended to serve as a challenging and intriguing dataset for testing visual language models and large multimodal language models. The images in this dataset are characterized by their unique artistic style and complexity, which can provide a robust test for the capabilities of modern AI models.## Usage\nThis dataset is intended for research purposes, specifically the evaluation of visual and multimodal language models.## Structure\n- Each entry in the dataset is an image without any annotation or category.## License\nThis dataset is made available under a Creative Commons Attribution 4.0 International License."
] |
37a821f09375bf26f1393ef78ef66d373f0540a8
|
# Dataset Card for Evaluation run of MBZUAI/lamini-neo-1.3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/MBZUAI/lamini-neo-1.3b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [MBZUAI/lamini-neo-1.3b](https://huggingface.co/MBZUAI/lamini-neo-1.3b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T15:21:46.431388](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b/blob/main/results_2023-10-18T15-21-46.431388.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": 0.01363255033557047,
"em_stderr": 0.001187538155241294,
"f1": 0.09467806208053722,
"f1_stderr": 0.0019692719599384927,
"acc": 0.28331537189746364,
"acc_stderr": 0.007502296729483641
},
"harness|drop|3": {
"em": 0.01363255033557047,
"em_stderr": 0.001187538155241294,
"f1": 0.09467806208053722,
"f1_stderr": 0.0019692719599384927
},
"harness|gsm8k|5": {
"acc": 0.001516300227445034,
"acc_stderr": 0.0010717793485492619
},
"harness|winogrande|5": {
"acc": 0.5651144435674822,
"acc_stderr": 0.01393281411041802
}
}
```
### 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_MBZUAI__lamini-neo-1.3b
|
[
"region:us"
] |
2023-10-18T14:21:50+00:00
|
{"pretty_name": "Evaluation run of MBZUAI/lamini-neo-1.3b", "dataset_summary": "Dataset automatically created during the evaluation run of model [MBZUAI/lamini-neo-1.3b](https://huggingface.co/MBZUAI/lamini-neo-1.3b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T15:21:46.431388](https://huggingface.co/datasets/open-llm-leaderboard/details_MBZUAI__lamini-neo-1.3b/blob/main/results_2023-10-18T15-21-46.431388.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\": 0.01363255033557047,\n \"em_stderr\": 0.001187538155241294,\n \"f1\": 0.09467806208053722,\n \"f1_stderr\": 0.0019692719599384927,\n \"acc\": 0.28331537189746364,\n \"acc_stderr\": 0.007502296729483641\n },\n \"harness|drop|3\": {\n \"em\": 0.01363255033557047,\n \"em_stderr\": 0.001187538155241294,\n \"f1\": 0.09467806208053722,\n \"f1_stderr\": 0.0019692719599384927\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.001516300227445034,\n \"acc_stderr\": 0.0010717793485492619\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5651144435674822,\n \"acc_stderr\": 0.01393281411041802\n }\n}\n```", "repo_url": "https://huggingface.co/MBZUAI/lamini-neo-1.3b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T15_21_46.431388", "path": ["**/details_harness|drop|3_2023-10-18T15-21-46.431388.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T15-21-46.431388.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T15_21_46.431388", "path": ["**/details_harness|gsm8k|5_2023-10-18T15-21-46.431388.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T15-21-46.431388.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T15_21_46.431388", "path": ["**/details_harness|winogrande|5_2023-10-18T15-21-46.431388.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T15-21-46.431388.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T15_21_46.431388", "path": ["results_2023-10-18T15-21-46.431388.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T15-21-46.431388.parquet"]}]}]}
|
2023-10-18T14:22:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of MBZUAI/lamini-neo-1.3b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model MBZUAI/lamini-neo-1.3b on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T15:21:46.431388(note that their might be results for other tasks in 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 MBZUAI/lamini-neo-1.3b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model MBZUAI/lamini-neo-1.3b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T15:21:46.431388(note that their might be results for other tasks in 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 MBZUAI/lamini-neo-1.3b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model MBZUAI/lamini-neo-1.3b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T15:21:46.431388(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
20,
31,
168,
66,
10,
4,
6,
6,
5,
5,
5,
7,
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10,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of MBZUAI/lamini-neo-1.3b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model MBZUAI/lamini-neo-1.3b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T15:21:46.431388(note that their might be results for other tasks in 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"
] |
b725b493bc9e2aaccf222cd5df0b1546db1acf0a
|
# Dataset Card for "turkiye_finance_qa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
umarigan/turkiye_finance_qa
|
[
"task_categories:question-answering",
"size_categories:n<1K",
"language:tr",
"finance",
"region:us"
] |
2023-10-18T14:38:28+00:00
|
{"language": ["tr"], "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "Finance", "dataset_info": {"features": [{"name": "soru", "dtype": "string"}, {"name": "cevap", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 171936, "num_examples": 428}], "download_size": 82421, "dataset_size": 171936}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["finance"]}
|
2023-10-18T15:04:56+00:00
|
[] |
[
"tr"
] |
TAGS
#task_categories-question-answering #size_categories-n<1K #language-Turkish #finance #region-us
|
# Dataset Card for "turkiye_finance_qa"
More Information needed
|
[
"# Dataset Card for \"turkiye_finance_qa\"\n\nMore Information needed"
] |
[
"TAGS\n#task_categories-question-answering #size_categories-n<1K #language-Turkish #finance #region-us \n",
"# Dataset Card for \"turkiye_finance_qa\"\n\nMore Information needed"
] |
[
37,
17
] |
[
"passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-Turkish #finance #region-us \n# Dataset Card for \"turkiye_finance_qa\"\n\nMore Information needed"
] |
612eebbc8778788c571cf5346ca5870e33cd1380
|
<p align="center">
<img src="https://github.com/hitz-zentroa/This-is-not-a-Dataset/raw/main/assets/tittle.png" style="height: 250px;">
</p>
<h3 align="center">"A Large Negation Benchmark to Challenge Large Language Models"</h3>
<p align="justify">
We introduce a large semi-automatically generated dataset of ~400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms that we use to evaluate LLMs.
</p>
- 📖 Paper: [This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (EMNLP'23)](http://arxiv.org/abs/2310.15941)
- 💻 Baseline Code and the Official Scorer: [https://github.com/hitz-zentroa/This-is-not-a-Dataset](https://github.com/hitz-zentroa/This-is-not-a-Dataset)
# Data explanation
- **pattern_id** (int): The ID of the pattern,in range [1,11]
- **pattern** (str): The name of the pattern
- **test_id** (int): For each pattern we use a set of templates to instanciate the triples. Examples are grouped in triples by test id
- **negation_type** (str): Affirmation, verbal, non-verbal
- **semantic_type** (str): None (for affirmative sentences), analytic, synthetic
- **syntactic_scope** (str): None (for affirmative sentences), clausal, subclausal
- **isDistractor** (bool): We use distractors (randonly selectec synsets) to generate false kwoledge.
- **<span style="color:green">sentence</span>** (str): The sentence. <ins>This is the input of the model</ins>
- **<span style="color:green">label</span>** (bool): The label of the example, True if the statement is true, False otherwise. <ins>This is the target of the model</ins>
If you want to run experiments with this dataset, please, use the [Official Scorer](https://github.com/hitz-zentroa/This-is-not-a-Dataset#scorer) to ensure reproducibility and fairness.
# Citation
```bibtex
@inproceedings{garcia-ferrero-etal-2023-dataset,
title = "This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models",
author = "Garc{\'\i}a-Ferrero, Iker and
Altuna, Bego{\~n}a and
Alvez, Javier and
Gonzalez-Dios, Itziar and
Rigau, German",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.531",
doi = "10.18653/v1/2023.emnlp-main.531",
pages = "8596--8615",
abstract = "Although large language models (LLMs) have apparently acquired a certain level of grammatical knowledge and the ability to make generalizations, they fail to interpret negation, a crucial step in Natural Language Processing. We try to clarify the reasons for the sub-optimal performance of LLMs understanding negation. We introduce a large semi-automatically generated dataset of circa 400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms. We have used our dataset with the largest available open LLMs in a zero-shot approach to grasp their generalization and inference capability and we have also fine-tuned some of the models to assess whether the understanding of negation can be trained. Our findings show that, while LLMs are proficient at classifying affirmative sentences, they struggle with negative sentences and lack a deep understanding of negation, often relying on superficial cues. Although fine-tuning the models on negative sentences improves their performance, the lack of generalization in handling negation is persistent, highlighting the ongoing challenges of LLMs regarding negation understanding and generalization. The dataset and code are publicly available.",
}
```
|
HiTZ/This-is-not-a-dataset
|
[
"task_categories:text-classification",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"commonsense",
"negation",
"LLMs",
"LLM",
"arxiv:2310.15941",
"region:us"
] |
2023-10-18T14:55:47+00:00
|
{"language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "paperswithcode_id": "this-is-not-a-dataset", "pretty_name": "This is NOT a Dataset", "dataset_info": {"features": [{"name": "pattern_id", "dtype": "int64"}, {"name": "pattern", "dtype": "string"}, {"name": "test_id", "dtype": "int64"}, {"name": "negation_type", "dtype": "string"}, {"name": "semantic_type", "dtype": "string"}, {"name": "syntactic_scope", "dtype": "string"}, {"name": "isDistractor", "dtype": "bool"}, {"name": "label", "dtype": "bool"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41264658, "num_examples": 268505}, {"name": "validation", "num_bytes": 3056321, "num_examples": 22514}, {"name": "test", "num_bytes": 12684749, "num_examples": 90281}], "download_size": 6311034, "dataset_size": 57005728}, "tags": ["commonsense", "negation", "LLMs", "LLM"]}
|
2024-02-08T10:14:26+00:00
|
[
"2310.15941"
] |
[
"en"
] |
TAGS
#task_categories-text-classification #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #commonsense #negation #LLMs #LLM #arxiv-2310.15941 #region-us
|
<p align="center">
<img src="URL style="height: 250px;">
</p>
<h3 align="center">"A Large Negation Benchmark to Challenge Large Language Models"</h3>
<p align="justify">
We introduce a large semi-automatically generated dataset of ~400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms that we use to evaluate LLMs.
</p>
- Paper: This is not a Dataset: A Large Negation Benchmark to Challenge Large Language Models (EMNLP'23)
- Baseline Code and the Official Scorer: URL
# Data explanation
- pattern_id (int): The ID of the pattern,in range [1,11]
- pattern (str): The name of the pattern
- test_id (int): For each pattern we use a set of templates to instanciate the triples. Examples are grouped in triples by test id
- negation_type (str): Affirmation, verbal, non-verbal
- semantic_type (str): None (for affirmative sentences), analytic, synthetic
- syntactic_scope (str): None (for affirmative sentences), clausal, subclausal
- isDistractor (bool): We use distractors (randonly selectec synsets) to generate false kwoledge.
- <span style="color:green">sentence</span> (str): The sentence. <ins>This is the input of the model</ins>
- <span style="color:green">label</span> (bool): The label of the example, True if the statement is true, False otherwise. <ins>This is the target of the model</ins>
If you want to run experiments with this dataset, please, use the Official Scorer to ensure reproducibility and fairness.
|
[
"# Data explanation\n\n- pattern_id (int): The ID of the pattern,in range [1,11]\n- pattern (str): The name of the pattern\n- test_id (int): For each pattern we use a set of templates to instanciate the triples. Examples are grouped in triples by test id\n- negation_type (str): Affirmation, verbal, non-verbal\n- semantic_type (str): None (for affirmative sentences), analytic, synthetic\n- syntactic_scope (str): None (for affirmative sentences), clausal, subclausal\n- isDistractor (bool): We use distractors (randonly selectec synsets) to generate false kwoledge.\n- <span style=\"color:green\">sentence</span> (str): The sentence. <ins>This is the input of the model</ins>\n- <span style=\"color:green\">label</span> (bool): The label of the example, True if the statement is true, False otherwise. <ins>This is the target of the model</ins>\n\nIf you want to run experiments with this dataset, please, use the Official Scorer to ensure reproducibility and fairness."
] |
[
"TAGS\n#task_categories-text-classification #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #commonsense #negation #LLMs #LLM #arxiv-2310.15941 #region-us \n",
"# Data explanation\n\n- pattern_id (int): The ID of the pattern,in range [1,11]\n- pattern (str): The name of the pattern\n- test_id (int): For each pattern we use a set of templates to instanciate the triples. Examples are grouped in triples by test id\n- negation_type (str): Affirmation, verbal, non-verbal\n- semantic_type (str): None (for affirmative sentences), analytic, synthetic\n- syntactic_scope (str): None (for affirmative sentences), clausal, subclausal\n- isDistractor (bool): We use distractors (randonly selectec synsets) to generate false kwoledge.\n- <span style=\"color:green\">sentence</span> (str): The sentence. <ins>This is the input of the model</ins>\n- <span style=\"color:green\">label</span> (bool): The label of the example, True if the statement is true, False otherwise. <ins>This is the target of the model</ins>\n\nIf you want to run experiments with this dataset, please, use the Official Scorer to ensure reproducibility and fairness."
] |
[
80,
270
] |
[
"passage: TAGS\n#task_categories-text-classification #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-apache-2.0 #commonsense #negation #LLMs #LLM #arxiv-2310.15941 #region-us \n# Data explanation\n\n- pattern_id (int): The ID of the pattern,in range [1,11]\n- pattern (str): The name of the pattern\n- test_id (int): For each pattern we use a set of templates to instanciate the triples. Examples are grouped in triples by test id\n- negation_type (str): Affirmation, verbal, non-verbal\n- semantic_type (str): None (for affirmative sentences), analytic, synthetic\n- syntactic_scope (str): None (for affirmative sentences), clausal, subclausal\n- isDistractor (bool): We use distractors (randonly selectec synsets) to generate false kwoledge.\n- <span style=\"color:green\">sentence</span> (str): The sentence. <ins>This is the input of the model</ins>\n- <span style=\"color:green\">label</span> (bool): The label of the example, True if the statement is true, False otherwise. <ins>This is the target of the model</ins>\n\nIf you want to run experiments with this dataset, please, use the Official Scorer to ensure reproducibility and fairness."
] |
007b9ee677fa47269bbbc8975c4252b25ba362f4
|
# Dataset Card for "3d24f339"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
result-kand2-sdxl-wuerst-karlo/3d24f339
|
[
"region:us"
] |
2023-10-18T15:03:54+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 168, "num_examples": 10}], "download_size": 1326, "dataset_size": 168}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T15:03:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "3d24f339"
More Information needed
|
[
"# Dataset Card for \"3d24f339\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"3d24f339\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"3d24f339\"\n\nMore Information needed"
] |
ec999e0ae9a3aed4775f3bc0825929a8cd466ea6
|
# Dataset Card for "eval_tag_nq_test_v12_first_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tyzhu/eval_tag_nq_test_v12_first_1
|
[
"region:us"
] |
2023-10-18T15:07:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "inputs", "dtype": "string"}, {"name": "targets", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "null"}, {"name": "text", "sequence": "string"}]}, {"name": "id", "dtype": "string"}, {"name": "titles", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3310, "num_examples": 10}, {"name": "validation", "num_bytes": 1306262, "num_examples": 3610}], "download_size": 0, "dataset_size": 1309572}}
|
2023-10-18T15:09:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eval_tag_nq_test_v12_first_1"
More Information needed
|
[
"# Dataset Card for \"eval_tag_nq_test_v12_first_1\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eval_tag_nq_test_v12_first_1\"\n\nMore Information needed"
] |
[
6,
26
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eval_tag_nq_test_v12_first_1\"\n\nMore Information needed"
] |
6858e89ad3388677956bec0b90a3dee69444e93a
|
# Dataset Card for "arc-portuguese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ceia-nlp/arc-portuguese
|
[
"region:us"
] |
2023-10-18T15:10:42+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "question", "sequence": "string"}, {"name": "choices", "sequence": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}]}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 370596, "num_examples": 1119}, {"name": "test", "num_bytes": 399448, "num_examples": 1172}, {"name": "validation", "num_bytes": 102919, "num_examples": 299}], "download_size": 477678, "dataset_size": 872963}}
|
2023-10-18T15:11:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "arc-portuguese"
More Information needed
|
[
"# Dataset Card for \"arc-portuguese\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"arc-portuguese\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"arc-portuguese\"\n\nMore Information needed"
] |
ca7a4f8731a27785fd35cfb51dd90bd30c021d46
|
# Dataset Card for "selenium-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ajax-law/selenium-dataset
|
[
"region:us"
] |
2023-10-18T15:23:50+00:00
|
{"dataset_info": {"features": [{"name": "file_name", "dtype": "string"}, {"name": "image_id", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "width", "dtype": "int64"}, {"name": "annotations", "list": [{"name": "bbox", "sequence": "float64"}, {"name": "bbox_mode", "dtype": "int64"}, {"name": "category_id", "dtype": "int64"}, {"name": "segmentation", "sequence": {"sequence": {"sequence": "float64"}}}]}, {"name": "image_object", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 654562556.8, "num_examples": 3200}], "download_size": 710193405, "dataset_size": 654562556.8}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-20T15:29:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "selenium-dataset"
More Information needed
|
[
"# Dataset Card for \"selenium-dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"selenium-dataset\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"selenium-dataset\"\n\nMore Information needed"
] |
a20f3780e0e7febbecec6a6a7deda718c0ae900f
|
<div align="center">
<img width="640" alt="sargishunanyan/thermo-classification" src="https://huggingface.co/datasets/sargishunanyan/thermo-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Thermostat', 'Housing', 'Insert']
```
### Number of Images
```json
{'valid': 102, 'test': 52, 'train': 372}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("sargishunanyan/thermo-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0/dataset/3](https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0/dataset/3?ref=roboflow2huggingface)
### Citation
```
@misc{ proj-2-qmdk0_dataset,
title = { proj 2 Dataset },
type = { Open Source Dataset },
author = { Yolo },
howpublished = { \\url{ https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 } },
url = { https://universe.roboflow.com/yolo-po0ro/proj-2-qmdk0 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { oct },
note = { visited on 2023-10-18 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on October 8, 2023 at 7:58 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit https://github.com/roboflow/notebooks
To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com
The dataset includes 526 images.
Car-parts are annotated in folder format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
|
sargishunanyan/thermo-classification
|
[
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"region:us"
] |
2023-10-18T15:27:45+00:00
|
{"task_categories": ["image-classification"], "tags": ["roboflow", "roboflow2huggingface"]}
|
2023-10-18T15:32:26+00:00
|
[] |
[] |
TAGS
#task_categories-image-classification #roboflow #roboflow2huggingface #region-us
|
<div align="center">
<img width="640" alt="sargishunanyan/thermo-classification" src="URL
</div>
### Dataset Labels
### Number of Images
### How to Use
- Install datasets:
- Load the dataset:
### Roboflow Dataset Page
URL
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via URL on October 8, 2023 at 7:58 AM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand and search unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
For state of the art Computer Vision training notebooks you can use with this dataset,
visit URL
To find over 100k other datasets and pre-trained models, visit URL
The dataset includes 526 images.
Car-parts are annotated in folder format.
The following pre-processing was applied to each image:
No image augmentation techniques were applied.
|
[
"### Dataset Labels",
"### Number of Images",
"### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:",
"### Roboflow Dataset Page\nURL",
"### License\nCC BY 4.0",
"### Dataset Summary\nThis dataset was exported via URL on October 8, 2023 at 7:58 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 526 images.\nCar-parts are annotated in folder format.\n\nThe following pre-processing was applied to each image:\n\nNo image augmentation techniques were applied."
] |
[
"TAGS\n#task_categories-image-classification #roboflow #roboflow2huggingface #region-us \n",
"### Dataset Labels",
"### Number of Images",
"### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:",
"### Roboflow Dataset Page\nURL",
"### License\nCC BY 4.0",
"### Dataset Summary\nThis dataset was exported via URL on October 8, 2023 at 7:58 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 526 images.\nCar-parts are annotated in folder format.\n\nThe following pre-processing was applied to each image:\n\nNo image augmentation techniques were applied."
] |
[
27,
5,
5,
18,
8,
6,
174
] |
[
"passage: TAGS\n#task_categories-image-classification #roboflow #roboflow2huggingface #region-us \n### Dataset Labels### Number of Images### How to Use\n\n- Install datasets:\n\n\n\n- Load the dataset:### Roboflow Dataset Page\nURL### License\nCC BY 4.0### Dataset Summary\nThis dataset was exported via URL on October 8, 2023 at 7:58 AM GMT\n\nRoboflow is an end-to-end computer vision platform that helps you\n* collaborate with your team on computer vision projects\n* collect & organize images\n* understand and search unstructured image data\n* annotate, and create datasets\n* export, train, and deploy computer vision models\n* use active learning to improve your dataset over time\n\nFor state of the art Computer Vision training notebooks you can use with this dataset,\nvisit URL\n\nTo find over 100k other datasets and pre-trained models, visit URL\n\nThe dataset includes 526 images.\nCar-parts are annotated in folder format.\n\nThe following pre-processing was applied to each image:\n\nNo image augmentation techniques were applied."
] |
593c72d346f786fc6286a357f3c789dcc39813ad
|
# Dataset Card for "SampleDataset2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
asgaardlab/SampleDataset2
|
[
"region:us"
] |
2023-10-18T15:49:40+00:00
|
{"dataset_info": {"features": [{"name": "Buggy Image", "dtype": "image"}, {"name": "Correct Image", "dtype": "image"}, {"name": "Segmentation Image (Bug)", "dtype": "image"}, {"name": "Segmentation Image (Correct)", "dtype": "image"}, {"name": "Description", "dtype": "string"}, {"name": "Tag", "dtype": "string"}, {"name": "Objects JSON (Bug)", "dtype": "string"}, {"name": "Objects JSON (Correct)", "dtype": "string"}, {"name": "Victim Name", "dtype": "string"}, {"name": "Victim Color", "sequence": "int64"}], "splits": [{"name": "validation", "num_bytes": 404705601.0, "num_examples": 751}], "download_size": 379820267, "dataset_size": 404705601.0}, "configs": [{"config_name": "default", "data_files": [{"split": "validation", "path": "data/validation-*"}]}]}
|
2023-10-18T15:49:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SampleDataset2"
More Information needed
|
[
"# Dataset Card for \"SampleDataset2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SampleDataset2\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SampleDataset2\"\n\nMore Information needed"
] |
36ab57cbc69b2c30617732d868625a1ddd2bf817
|
# Dataset Card for Evaluation run of psmathur/model_007_13b_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/psmathur/model_007_13b_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_13b_v2](https://huggingface.co/psmathur/model_007_13b_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_psmathur__model_007_13b_v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T16:51:13.661301](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_007_13b_v2/blob/main/results_2023-10-18T16-51-13.661301.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": 0.36671560402684567,
"em_stderr": 0.004935188438972311,
"f1": 0.4397388842281892,
"f1_stderr": 0.0047230877436521415,
"acc": 0.386065655680172,
"acc_stderr": 0.007612365428746324
},
"harness|drop|3": {
"em": 0.36671560402684567,
"em_stderr": 0.004935188438972311,
"f1": 0.4397388842281892,
"f1_stderr": 0.0047230877436521415
},
"harness|gsm8k|5": {
"acc": 0.013646702047005308,
"acc_stderr": 0.0031957470754807745
},
"harness|winogrande|5": {
"acc": 0.7584846093133386,
"acc_stderr": 0.012028983782011874
}
}
```
### 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_13b_v2
|
[
"region:us"
] |
2023-10-18T15:51:17+00:00
|
{"pretty_name": "Evaluation run of psmathur/model_007_13b_v2", "dataset_summary": "Dataset automatically created during the evaluation run of model [psmathur/model_007_13b_v2](https://huggingface.co/psmathur/model_007_13b_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_psmathur__model_007_13b_v2\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T16:51:13.661301](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_007_13b_v2/blob/main/results_2023-10-18T16-51-13.661301.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\": 0.36671560402684567,\n \"em_stderr\": 0.004935188438972311,\n \"f1\": 0.4397388842281892,\n \"f1_stderr\": 0.0047230877436521415,\n \"acc\": 0.386065655680172,\n \"acc_stderr\": 0.007612365428746324\n },\n \"harness|drop|3\": {\n \"em\": 0.36671560402684567,\n \"em_stderr\": 0.004935188438972311,\n \"f1\": 0.4397388842281892,\n \"f1_stderr\": 0.0047230877436521415\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.013646702047005308,\n \"acc_stderr\": 0.0031957470754807745\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7584846093133386,\n \"acc_stderr\": 0.012028983782011874\n }\n}\n```", "repo_url": "https://huggingface.co/psmathur/model_007_13b_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_10_18T16_51_13.661301", "path": ["**/details_harness|drop|3_2023-10-18T16-51-13.661301.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T16-51-13.661301.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T16_51_13.661301", "path": ["**/details_harness|gsm8k|5_2023-10-18T16-51-13.661301.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T16-51-13.661301.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T16_51_13.661301", "path": ["**/details_harness|winogrande|5_2023-10-18T16-51-13.661301.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T16-51-13.661301.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T16_51_13.661301", "path": ["results_2023-10-18T16-51-13.661301.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T16-51-13.661301.parquet"]}]}]}
|
2023-10-18T15:51:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of psmathur/model_007_13b_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_13b_v2 on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T16:51:13.661301(note that their might be results for other tasks in 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_007_13b_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_13b_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T16:51:13.661301(note that their might be results for other tasks in 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 psmathur/model_007_13b_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_13b_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T16:51:13.661301(note that their might be results for other tasks in 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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22,
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66,
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5,
5,
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7,
4,
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10,
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8,
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] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of psmathur/model_007_13b_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_13b_v2 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T16:51:13.661301(note that their might be results for other tasks in 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"
] |
dc0985773726320c1c04ba8adc74b43b1a7bfd55
|
# gnomAD variants and GPN-MSA predictions
For more information check out our [paper](https://doi.org/10.1101/2023.10.10.561776) and [repository](https://github.com/songlab-cal/gpn).
## Querying specific variants or genes
- Install the latest [tabix](https://www.htslib.org/doc/tabix.html):
In your current conda environment (might be slow):
```bash
conda install -c bioconda -c conda-forge htslib=1.18
```
or in a new conda environment:
```bash
conda create -n tabix -c bioconda -c conda-forge htslib=1.18
conda activate tabix
```
- Query a specific region (e.g. BRCA1), from the remote file:
```bash
tabix https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz 17:43,044,295-43,125,364
```
The output has the following columns:
| chrom | pos | ref | alt | GPN-MSA score |
and would start like this:
```tsv
17 43044304 T G -5.10
17 43044309 A G -3.27
17 43044315 T A -6.84
17 43044320 T C -6.19
17 43044322 G T -5.29
17 43044326 T G -3.22
17 43044342 T C -4.10
17 43044346 C T -2.06
17 43044351 C T -0.33
17 43044352 G A 2.05
```
- If you want to do many queries you might want to first download the files locally
```bash
wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz
wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/scores.tsv.bgz.tbi
```
and then score:
```bash
tabix scores.tsv.bgz 17:43,044,295-43,125,364
```
## Large-scale analysis
`test.parquet` contains coordinates, scores, plus allele frequency and consequences.
Download:
```
wget https://huggingface.co/datasets/songlab/gnomad/resolve/main/test.parquet
```
Load into a Pandas dataframe:
```python
df = pd.read_parquet("test.parquet")
```
|
songlab/gnomad
|
[
"license:mit",
"dna",
"variant-effect-prediction",
"biology",
"genomics",
"region:us"
] |
2023-10-18T17:02:34+00:00
|
{"license": "mit", "tags": ["dna", "variant-effect-prediction", "biology", "genomics"]}
|
2024-01-27T18:21:40+00:00
|
[] |
[] |
TAGS
#license-mit #dna #variant-effect-prediction #biology #genomics #region-us
|
# gnomAD variants and GPN-MSA predictions
For more information check out our paper and repository.
## Querying specific variants or genes
- Install the latest tabix:
In your current conda environment (might be slow):
or in a new conda environment:
- Query a specific region (e.g. BRCA1), from the remote file:
The output has the following columns:
| chrom | pos | ref | alt | GPN-MSA score |
and would start like this:
- If you want to do many queries you might want to first download the files locally
and then score:
## Large-scale analysis
'test.parquet' contains coordinates, scores, plus allele frequency and consequences.
Download:
Load into a Pandas dataframe:
|
[
"# gnomAD variants and GPN-MSA predictions\nFor more information check out our paper and repository.",
"## Querying specific variants or genes\n\n- Install the latest tabix: \n In your current conda environment (might be slow):\n \n or in a new conda environment:\n \n- Query a specific region (e.g. BRCA1), from the remote file: \n \n The output has the following columns: \n | chrom | pos | ref | alt | GPN-MSA score | \n and would start like this: \n \n- If you want to do many queries you might want to first download the files locally\n \n and then score:",
"## Large-scale analysis\n'test.parquet' contains coordinates, scores, plus allele frequency and consequences.\nDownload:\n\nLoad into a Pandas dataframe:"
] |
[
"TAGS\n#license-mit #dna #variant-effect-prediction #biology #genomics #region-us \n",
"# gnomAD variants and GPN-MSA predictions\nFor more information check out our paper and repository.",
"## Querying specific variants or genes\n\n- Install the latest tabix: \n In your current conda environment (might be slow):\n \n or in a new conda environment:\n \n- Query a specific region (e.g. BRCA1), from the remote file: \n \n The output has the following columns: \n | chrom | pos | ref | alt | GPN-MSA score | \n and would start like this: \n \n- If you want to do many queries you might want to first download the files locally\n \n and then score:",
"## Large-scale analysis\n'test.parquet' contains coordinates, scores, plus allele frequency and consequences.\nDownload:\n\nLoad into a Pandas dataframe:"
] |
[
27,
26,
117,
40
] |
[
"passage: TAGS\n#license-mit #dna #variant-effect-prediction #biology #genomics #region-us \n# gnomAD variants and GPN-MSA predictions\nFor more information check out our paper and repository.## Querying specific variants or genes\n\n- Install the latest tabix: \n In your current conda environment (might be slow):\n \n or in a new conda environment:\n \n- Query a specific region (e.g. BRCA1), from the remote file: \n \n The output has the following columns: \n | chrom | pos | ref | alt | GPN-MSA score | \n and would start like this: \n \n- If you want to do many queries you might want to first download the files locally\n \n and then score:## Large-scale analysis\n'test.parquet' contains coordinates, scores, plus allele frequency and consequences.\nDownload:\n\nLoad into a Pandas dataframe:"
] |
cff4aa4716aaed71df83a345f7ef8798025493e0
|
# Dataset Card for Evaluation run of bigscience/bloomz-3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloomz-3b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [bigscience/bloomz-3b](https://huggingface.co/bigscience/bloomz-3b) 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 9 run(s). Each run can be found as a specific 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_bigscience__bloomz-3b",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T12:49:47.225318](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-3b/blob/main/results_2023-12-04T12-49-47.225318.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": 0.0,
"acc_stderr": 0.0
},
"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_bigscience__bloomz-3b
|
[
"region:us"
] |
2023-10-18T17:31:34+00:00
|
{"pretty_name": "Evaluation run of bigscience/bloomz-3b", "dataset_summary": "Dataset automatically created during the evaluation run of model [bigscience/bloomz-3b](https://huggingface.co/bigscience/bloomz-3b) 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 9 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to 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_bigscience__bloomz-3b\",\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-12-04T12:49:47.225318](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-3b/blob/main/results_2023-12-04T12-49-47.225318.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\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```", "repo_url": "https://huggingface.co/bigscience/bloomz-3b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T18_31_31.280992", "path": ["**/details_harness|drop|3_2023-10-18T18-31-31.280992.parquet"]}, {"split": "2023_10_19T00_43_52.715798", "path": ["**/details_harness|drop|3_2023-10-19T00-43-52.715798.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T00-43-52.715798.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T18_31_31.280992", "path": ["**/details_harness|gsm8k|5_2023-10-18T18-31-31.280992.parquet"]}, {"split": "2023_10_19T00_43_52.715798", "path": ["**/details_harness|gsm8k|5_2023-10-19T00-43-52.715798.parquet"]}, {"split": "2023_12_03T14_46_56.234562", "path": ["**/details_harness|gsm8k|5_2023-12-03T14-46-56.234562.parquet"]}, {"split": "2023_12_03T14_47_06.758540", "path": ["**/details_harness|gsm8k|5_2023-12-03T14-47-06.758540.parquet"]}, {"split": "2023_12_03T15_49_10.601580", "path": ["**/details_harness|gsm8k|5_2023-12-03T15-49-10.601580.parquet"]}, {"split": "2023_12_03T15_49_33.770750", "path": ["**/details_harness|gsm8k|5_2023-12-03T15-49-33.770750.parquet"]}, {"split": "2023_12_04T09_39_46.898855", "path": ["**/details_harness|gsm8k|5_2023-12-04T09-39-46.898855.parquet"]}, {"split": "2023_12_04T09_39_46.973011", "path": ["**/details_harness|gsm8k|5_2023-12-04T09-39-46.973011.parquet"]}, {"split": "2023_12_04T12_49_47.225318", "path": ["**/details_harness|gsm8k|5_2023-12-04T12-49-47.225318.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-12-04T12-49-47.225318.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T18_31_31.280992", "path": ["**/details_harness|winogrande|5_2023-10-18T18-31-31.280992.parquet"]}, {"split": "2023_10_19T00_43_52.715798", "path": ["**/details_harness|winogrande|5_2023-10-19T00-43-52.715798.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-19T00-43-52.715798.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T18_31_31.280992", "path": ["results_2023-10-18T18-31-31.280992.parquet"]}, {"split": "2023_10_19T00_43_52.715798", "path": ["results_2023-10-19T00-43-52.715798.parquet"]}, {"split": "2023_12_03T14_46_56.234562", "path": ["results_2023-12-03T14-46-56.234562.parquet"]}, {"split": "2023_12_03T14_47_06.758540", "path": ["results_2023-12-03T14-47-06.758540.parquet"]}, {"split": "2023_12_03T15_49_10.601580", "path": ["results_2023-12-03T15-49-10.601580.parquet"]}, {"split": "2023_12_03T15_49_33.770750", "path": ["results_2023-12-03T15-49-33.770750.parquet"]}, {"split": "2023_12_04T09_39_46.898855", "path": ["results_2023-12-04T09-39-46.898855.parquet"]}, {"split": "2023_12_04T09_39_46.973011", "path": ["results_2023-12-04T09-39-46.973011.parquet"]}, {"split": "2023_12_04T12_49_47.225318", "path": ["results_2023-12-04T12-49-47.225318.parquet"]}, {"split": "latest", "path": ["results_2023-12-04T12-49-47.225318.parquet"]}]}]}
|
2023-12-04T12:49:52+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of bigscience/bloomz-3b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model bigscience/bloomz-3b 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 9 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-12-04T12:49:47.225318(note that their might be results for other tasks in 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 bigscience/bloomz-3b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model bigscience/bloomz-3b 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 9 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T12:49:47.225318(note that their might be results for other tasks in 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 bigscience/bloomz-3b",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model bigscience/bloomz-3b 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 9 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-12-04T12:49:47.225318(note that their might be results for other tasks in 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 bigscience/bloomz-3b## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model bigscience/bloomz-3b 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 9 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-12-04T12:49:47.225318(note that their might be results for other tasks in 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"
] |
498c8233bc57162bf1d60c8d423fcab79e02b00d
|
# Computed Tomography (CT) of the Chest
The dataset consists of CT chest scans for people with **aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis**. Each scan represents a detailed image of a patient's chest taken using **CT (Computed Tomography)**. The data are presented in 2 different formats: **.jpg and .dcm**.
The dataset of CT chest scans is valuable for research in **radiology, and oncology**. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for **automated detection, diagnosis, and classification** of these conditions.
### Types of diseases and conditions in the dataset:
- **Aortic aneurysm**
- **Broken ribs**
- **Cancer**
- **COVID-19**
- **Signs of IHD**
- **Tuberculosis**
# Get the dataset
### This is just an example of the data
Leave a request on [**https://trainingdata.pro/data-market**](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ct-of-the-ches) to discuss your requirements, learn about the price and buy the dataset.
# Content
### The folder "files" includes 6 folders:
- corresponding to name of the disease/condition and including ct scans of people with this disease/condition (**aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis**)
- including scans in 2 different formats: **.jpg and .dcm**.
### File with the extension .csv includes the following information for each media file:
- **dcm**: link to access the .dcm file,
- **jpg**: link to access the .jpg file,
- **type**: name of the disease/condition on the ct
# Medical data might be collected in accordance with your requirements.
## [TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=ct-of-the-chest) provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
TrainingData's GitHub: **https://github.com/trainingdata-pro**
|
TrainingDataPro/computed-tomography-ct-of-the-chest
|
[
"task_categories:image-classification",
"task_categories:image-to-image",
"language:en",
"license:cc-by-nc-nd-4.0",
"medical",
"biology",
"code",
"region:us"
] |
2023-10-18T17:41:21+00:00
|
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification", "image-to-image"], "tags": ["medical", "biology", "code"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "type", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 77921516.0, "num_examples": 300}], "download_size": 77913057, "dataset_size": 77921516.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-11-09T10:20:54+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #medical #biology #code #region-us
|
# Computed Tomography (CT) of the Chest
The dataset consists of CT chest scans for people with aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis. Each scan represents a detailed image of a patient's chest taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm.
The dataset of CT chest scans is valuable for research in radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.
![](URL
### Types of diseases and conditions in the dataset:
- Aortic aneurysm
- Broken ribs
- Cancer
- COVID-19
- Signs of IHD
- Tuberculosis
# Get the dataset
### This is just an example of the data
Leave a request on URL to discuss your requirements, learn about the price and buy the dataset.
# Content
### The folder "files" includes 6 folders:
- corresponding to name of the disease/condition and including ct scans of people with this disease/condition (aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis)
- including scans in 2 different formats: .jpg and .dcm.
### File with the extension .csv includes the following information for each media file:
- dcm: link to access the .dcm file,
- jpg: link to access the .jpg file,
- type: name of the disease/condition on the ct
# Medical data might be collected in accordance with your requirements.
## TrainingData provides high-quality data annotation tailored to your needs
More datasets in TrainingData's Kaggle account: URL
TrainingData's GitHub: URL
|
[
"# Computed Tomography (CT) of the Chest\n\nThe dataset consists of CT chest scans for people with aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis. Each scan represents a detailed image of a patient's chest taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm. \n\nThe dataset of CT chest scans is valuable for research in radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.\n\n![](URL",
"### Types of diseases and conditions in the dataset:\n- Aortic aneurysm\n- Broken ribs\n- Cancer\n- COVID-19\n- Signs of IHD\n- Tuberculosis",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Content",
"### The folder \"files\" includes 6 folders:\n- corresponding to name of the disease/condition and including ct scans of people with this disease/condition (aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis)\n- including scans in 2 different formats: .jpg and .dcm.",
"### File with the extension .csv includes the following information for each media file:\n\n- dcm: link to access the .dcm file,\n- jpg: link to access the .jpg file, \n- type: name of the disease/condition on the ct",
"# Medical data might be collected in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
[
"TAGS\n#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #medical #biology #code #region-us \n",
"# Computed Tomography (CT) of the Chest\n\nThe dataset consists of CT chest scans for people with aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis. Each scan represents a detailed image of a patient's chest taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm. \n\nThe dataset of CT chest scans is valuable for research in radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.\n\n![](URL",
"### Types of diseases and conditions in the dataset:\n- Aortic aneurysm\n- Broken ribs\n- Cancer\n- COVID-19\n- Signs of IHD\n- Tuberculosis",
"# Get the dataset",
"### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.",
"# Content",
"### The folder \"files\" includes 6 folders:\n- corresponding to name of the disease/condition and including ct scans of people with this disease/condition (aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis)\n- including scans in 2 different formats: .jpg and .dcm.",
"### File with the extension .csv includes the following information for each media file:\n\n- dcm: link to access the .dcm file,\n- jpg: link to access the .jpg file, \n- type: name of the disease/condition on the ct",
"# Medical data might be collected in accordance with your requirements.",
"## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
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[
"passage: TAGS\n#task_categories-image-classification #task_categories-image-to-image #language-English #license-cc-by-nc-nd-4.0 #medical #biology #code #region-us \n# Computed Tomography (CT) of the Chest\n\nThe dataset consists of CT chest scans for people with aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis. Each scan represents a detailed image of a patient's chest taken using CT (Computed Tomography). The data are presented in 2 different formats: .jpg and .dcm. \n\nThe dataset of CT chest scans is valuable for research in radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.\n\n![](URL### Types of diseases and conditions in the dataset:\n- Aortic aneurysm\n- Broken ribs\n- Cancer\n- COVID-19\n- Signs of IHD\n- Tuberculosis# Get the dataset### This is just an example of the data\n\nLeave a request on URL to discuss your requirements, learn about the price and buy the dataset.# Content### The folder \"files\" includes 6 folders:\n- corresponding to name of the disease/condition and including ct scans of people with this disease/condition (aortic aneurysm, broken ribs, cancer, COVID-19, signs of IHD and tuberculosis)\n- including scans in 2 different formats: .jpg and .dcm.### File with the extension .csv includes the following information for each media file:\n\n- dcm: link to access the .dcm file,\n- jpg: link to access the .jpg file, \n- type: name of the disease/condition on the ct# Medical data might be collected in accordance with your requirements.## TrainingData provides high-quality data annotation tailored to your needs\n\nMore datasets in TrainingData's Kaggle account: URL\n\nTrainingData's GitHub: URL"
] |
99b6d8f8f3e32dc68874b7b90d253470185484d6
|
# Dataset Card for "helm-charts-uniform"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
peterbeamish/helm-charts-uniform
|
[
"region:us"
] |
2023-10-18T17:45:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "chart_name", "dtype": "string"}, {"name": "templates", "sequence": "string"}, {"name": "values", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2972740, "num_examples": 137}, {"name": "test", "num_bytes": 3044774, "num_examples": 138}], "download_size": 1781817, "dataset_size": 6017514}}
|
2023-10-19T00:14:36+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "helm-charts-uniform"
More Information needed
|
[
"# Dataset Card for \"helm-charts-uniform\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"helm-charts-uniform\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"helm-charts-uniform\"\n\nMore Information needed"
] |
ba661a007a9d99026b989d1b7e6eeda0eda6ccd5
|
# Dataset Card for "bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pkr7098/bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000
|
[
"region:us"
] |
2023-10-18T17:46:48+00:00
|
{"dataset_info": {"config_name": "truncate-512", "features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 23600541600, "num_examples": 6555706}, {"name": "validation", "num_bytes": 317304000, "num_examples": 88140}], "download_size": 310440269, "dataset_size": 23917845600}, "configs": [{"config_name": "truncate-512", "data_files": [{"split": "train", "path": "truncate-512/train-*"}, {"split": "validation", "path": "truncate-512/validation-*"}]}]}
|
2023-10-18T18:19:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000"
More Information needed
|
[
"# Dataset Card for \"bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000\"\n\nMore Information needed"
] |
[
6,
35
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"bert-base-uncased-bookcorpus-wiki-2022030-en-vocab_size-32000\"\n\nMore Information needed"
] |
874675e0f461dc50b20ae854267fbb0396dd277f
|
# Dataset Card for "commonsense-dialogues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
chrisgru/commonsense-dialogues
|
[
"region:us"
] |
2023-10-18T17:49:54+00:00
|
{"dataset_info": {"features": [{"name": "system", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13583083, "num_examples": 20176}, {"name": "test", "num_bytes": 1415597, "num_examples": 2158}, {"name": "validation", "num_bytes": 1443693, "num_examples": 2157}], "download_size": 6854218, "dataset_size": 16442373}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
|
2023-10-18T17:50:50+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "commonsense-dialogues"
More Information needed
|
[
"# Dataset Card for \"commonsense-dialogues\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"commonsense-dialogues\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"commonsense-dialogues\"\n\nMore Information needed"
] |
d4c6218cee9c2504a7a032cac3cdb169df8a33d4
|
# Dataset Card for VibraVox
### Dataset Summary
The [VibraVox dataset](https://vibravox.cnam.fr) is a general purpose dataset of french speech captured with body-conduction transducers.
This dataset can be used for various audio machine learning tasks : **Automatic Speech Recognition (ASR)** (Speech-to-Text , Speech-to-Phoneme), **Audio Bandwidth Extension (BWE)**, **Speaker identification** / recognition , **Voice cloning** , etc ...
<!--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
VibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled for release in early 2024. It includes speech recorded simultaneously using multiple body-conducted microphones :
- two in-ear microphones
- a bone conduction microphones
- a throat piezoelectric transducer
- a forehead miniature accelerometer
- a headset reference microphone located in front of the mouth.
Those sensors are described and documented in [the dataset creation](#dataset-creation) section.
The VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.
VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications.
Unlike traditional microphones, which rely on airborne sound waves, body-conduction microphones capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction microphones have been available for decades, their limited bandwidth has restricted their widespread usage. However, thanks to two tracks of improvements, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments.
### Example usage
VibraVox contains labelled data for 11 configurations tailored for specific tasks, mainly oriented towards **Automatic Speech Recognition** and **Bandwidth Extension**.
#### ASR Datasets
The ASR dataset configurations contain mono audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 6 different kinds of microphones.
Recording was carried out simultaneously on all 6 sensors. The audio files were sampled at 48 kHz and encoded as .wav PCM32 files.
To load a specific configuration simply use the following command :
```python
from datasets import load_dataset
config_name = "asr_mouth_headworn_reference_microphone"
vibravox_asr = load_dataset("Cnam-LMSSC/vibravox", config_name)
```
```config_name``` can be any of the following : ``` "asr_mouth_headworn_reference_microphone" ``` (full bandwidth microphone),``` "asr_in-ear_comply_foam_microphone" ``` (body conduction), ``` "asr_in-ear_rigid_earpiece_microphone" ``` (body conduction), ``` "asr_forehead_miniature_accelerometer" ``` (body-conduction, high frequency response), ``` "asr_temple_contact_microphone" ``` (body-conduction, low SNR), ``` "asr_throat_piezoelectric_sensor" ``` (body-conduction, high distorsion rate).
#### BWE Datasets
The BWE dataset configurations contain stereo audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 5 different kinds of body conduction microphones (first channel), and a standard reference microphone (second channel).
Recording was carried out simultaneously on all 6 sensors, therefore the 5 BWE configurations only differ on the band-limited sensor used, and the reference microphone channel is the same as the data found in the ``` "ASR_Mouth_headworn_reference_microphone" ``` configuration.
The **stereo** audio files are sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the ```sensor_id``` feature of the dataset.
To load a specific configuration simply use the following command :
```python
from datasets import load_dataset
config_name = "bwe_in-ear_comply_foam_microphone"
vibravox_bwe = load_dataset("Cnam-LMSSC/vibravox", config_name)
```
```config_name``` can be any of the following : ``` "bwe_in-ear_comply_foam_microphone" ``` (body conduction), ``` "bwe_in-ear_rigid_earpiece_microphone" ``` (body conduction), ``` "bwe_forehead_miniature_accelerometer" ``` (body-conduction, high frequency response), ``` "bwe_temple_contact_microphone" ``` (body-conduction, low SNR), ``` "bwe_throat_piezoelectric_sensor" ``` (body-conduction, high distorsion rate).
To load all the sensors in a single dataset (intersection of validated audio for each sensor), use "bwe_all" config name, which contains **6-channel** audios sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the ```sensor_id``` feature of the dataset.
```python
vibravox_bwe_all = load_dataset("Cnam-LMSSC/vibravox", "bwe_all_sensors")
```
### Dataset Structure
#### Data Instances
**ASR datasets :**
```python
{
'audio': {
'path': '/home/zinc/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/SiS_00004369_throat.wav', 'array': array([-1.23381615e-04, -9.16719437e-05, -1.23262405e-04, ...,
-1.40666962e-05, -2.26497650e-05, 8.22544098e-06]), 'sampling_rate': 48000},
'audio_length': 5.5399791667,
'transcription': "Le courant de sortie est toujours la valeur absolue du courant d'entrée.",
'text': 'le courant de sortie est toujours la valeur absolue du courant d entrée',
'phonemes': 'lə kuʁɑ̃ də sɔʁti ɛ tuʒuʁ la valœʁ absoly dy kuʁɑ̃ dɑ̃tʁe',
'num_channels': 1,
'sensor_id': ['Larynx piezoelectric transducer'],
'speaker_id': '039',
'gender': 'male',
'is_speech': True,
'is_noisy': False,
'split': 'train',
'sentence_id': '00004369'
}
```
**BWE datasets :**
```python
{'audio': {'path': '/home/zinc/.cache/huggingface/datasets/downloads/extracted/56cdda80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c632f/SiS_00012330_inearde_stereo.wav', 'array': array([[-7.68899918e-04, -8.36610794e-04, -8.05854797e-04, ...,
1.35087967e-03, 1.31452084e-03, 1.27232075e-03],
[-3.21865082e-06, 8.18967819e-05, 8.13007355e-05, ...,
7.52210617e-05, 1.05500221e-04, 1.66416168e-04]]),
'sampling_rate': 48000},
'audio_length': 6.1,
'transcription': 'En programmation informatique, une assertion est une expression qui doit être évaluée à vrai.', 'text': 'en programmation informatique une assertion est une expression qui doit être évaluée a vrai',
'is_gold_transcript': True,
'num_channels': 2,
'sensor_id': ['In-ear rigid earpiece microphone', 'Mouth headworn reference microphone'],
'speaker_id': '092',
'gender': 'female',
'is_speech': True,
'is_noisy': False,
'split': 'train',
'sentence_id': '00012330'}
```
#### Data Fields
**Common Data Fields for all datasets :**
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded (mono) audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `audio_length` (float32) - the audio length in seconds.
* `transcription` (string) - audio segment text (cased and with punctuation preserved)
* `num_channels` (int) - the number of audio channels in audio
* `sensor_id` (string) - a list of sensors used in this audio, ordered by channel number
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker (```male```or ```female```)
* `is_speech` (bool) - wether the audio contains speech audio (```True```) or if the speaker remains silent (```False```)
* `is_noisy` (bool) - wether the audio contains external environmental noise (```True```) or if the speaker is in a quiet recoring room (```False```)
* `split`(string) - split (can be "train", "val", or "test")
* `sentence_id` (string) - id of the pronounced sentence
**Extra Data Fields for ASR datasets :**
* `text` (string) - audio segment normalized text (lower cased, no punctuation, diacritics replaced by standard 26 french alphabet letters, plus 3 accented characters : é,è,ê and ç -- which hold phonetic significance -- and the space character, which corresponds to 31 possible characters : ``` [' ', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'ç', 'è', 'é', 'ê'] ```).
* `phonemes` (string) - audio segment phonemized text (using exclusively the strict french IPA (33) characters : ``` [' ', 'a', 'b', 'd', 'e', 'f', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 's', 't', 'u', 'v', 'w', 'y', 'z', 'ø', 'ŋ', 'œ', 'ɑ', 'ɔ', 'ə', 'ɛ', 'ɡ', 'ɲ', 'ʁ', 'ʃ', 'ʒ', '̃'] ``` ).
**Extra Data Fields for BWE datasets :**
* `is_gold_transcript` (bool) - wether the transcription has been validated using an external Whisper-large ASR process to ensure the speech matches the transcription(```True```) or if the obtained WER during automated post-processing is too high to consider the audio and the transcription matches (```False```).
#### Data Splits
Almost all configs contain data in three splits: train, validation and test (with a standard 80 / 10 / 10 repartition, without overlapping any speaker in each split).
Noise-only configs (Physiological noise and environmental noise, with ``` is_speech = False ```), which are intended for data augmentation only contain a train split.
Speech in noise config `SiN` configuration (intended for extreme tests) contains only a test split.
### Data statistics
The dataset has been prepared for the different available sensors, for two main tasks : Automatic Speech Recognition (Speech-to-Text and Speech-to-Phonemes) and speech Bandwidth extension.
The **ASR configs** of VibraVox contain labelled audio (transcribed with text, normalized text, and phonemic transcription on the strict french IPA alphabet) data for 6 sensors :
| Sensor | Transcribed Hours | Download size | Number of transcribed Speakers | Gender repartition M/F (in audio length) | Transcribed Tokens (normalized text, w/o spaces) | Transcribed Tokens (phonemes, w/o spaces) | Config name |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| Reference microphone (headset) | 17.85 | 11.63 GiB | 121 | 41.9 / 58.1 | 0.81 M | 0.65 M | ``` asr_mouth_headworn_reference_microphone ``` |
| In-ear microphone Type 1 (Comply Foam) | 17.55 | 11.42 GiB | 121 | 42.5 / 57.5 | 0.8 M | 0.64 M | ``` asr_in-ear_comply_foam_microphone ``` |
| In-ear microphone Type 2 (rigid earpiece) | 17.46 | 11.37 GiB | 120 | 42.4 / 57.6 | 0.79 M | 0.64 M | ``` asr_in-ear_rigid_earpiece_microphone ``` |
| Forehead (miniature accelerometer) | 16.72 | 10.89 GiB | 111 | 41.6 / 58.4 | 0.76 M | 0.61 M | ``` asr_forehead_miniature_accelerometer ``` |
| Temple contact microphone | 16.0 | 10.44 GiB | 119 | 45.3 / 54.7 | 0.73 M | 0.59 M | ``` asr_temple_contact_microphone ``` |
| Larynx piezoelectric sensor | 17.85 | 11.63 GiB | 121 | 41.9 / 58.1 | 0.81 M | 0.65 M | ``` asr_throat_piezoelectric_sensor ``` |
The **BWE configs** of VibraVox contain labelled audio (transcribed with text) data for 5 body-conduction sensors. Each audio file for BWE configs is a **stereo** wavfile (the first channel corresponds to the body-conduction sensor, the second channel to the reference aligned audio, captured with the reference headworn microphone).
| Sensor | Recorded Hours | Download size | Number of Speakers | Gender repartition M/F (in audio length) | Config name |
|:---:|:---:|:---:|:---:|:---:|:---:|
| In-ear microphone Type 1 (Comply Foam) | 20.4 | 27.05 GiB | 121 | 46.6 / 53.4 | ``` bwe_in-ear_comply_foam_microphone ``` |
| In-ear microphone Type 2 (rigid earpiece) | 20.31 | 26.92 GiB | 120 | 46.4 / 53.6 | ``` bwe_in-ear_rigid_earpiece_microphone ``` |
| Forehead (miniature accelerometer) | 19.17 | 25.57 GiB | 111 | 45.6 / 54.4 | ``` bwe_forehead_miniature_accelerometer ``` |
| Temple contact microphone | 18.75 | 24.92 GiB | 119 | 49.5 / 50.5 | ``` bwe_temple_contact_microphone ``` |
| Larynx piezoelectric sensor | 20.7 | 27.49 GiB | 121 | 45.9 / 54.1 | ``` bwe_throat_piezoelectric_sensor ``` |
| All sensors | 16.81 | 67.27 GiB | 108 | 50.1 / 49.9 | ``` bwe_all_sensors ``` |
VibraVox's **external noise configs** contain audio data for all 6 sensors. In these configurations, the speakers remain **silent** (``is_speech = False``), and **environmental noise is generated** around them using a using a [5th order ambisonic 3D sound spatializer](https://vibravox.cnam.fr/documentation/hardware/sphere/index.html) ( ``is_noisy = True``). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used for **realistic data-augmentation** using noise captured by body-conduction sensors, with the inherent attenuation of each sensors on different device wearers.
| Sensor | Recorded Hours | Download size | Number of individuals | Gender repartition M/F (in audio length) | Config name |
|:---:|:---:|:---:|:---:|:---:|:---:|
| Reference microphone (headset) | | | | | ``` env_noise_mouth_headworn_reference_microphone ``` |
| In-ear microphone Type 1 (Comply Foam) | | | | | ``` env_noisein-ear_comply_foam_microphone ``` |
| In-ear microphone Type 2 (rigid earpiece) | | | | | ``` env_noise_in-ear_rigid_earpiece_microphone ``` |
| Forehead (miniature accelerometer) | | | | | ``` env_noise_forehead_miniature_accelerometer ``` |
| Temple contact microphone | | | | | ``` env_noise_temple_contact_microphone ``` |
| Larynx piezoelectric sensor | | | | | ``` env_noise_throat_piezoelectric_sensor ``` |
VibraVox's **physiological noise configs** contain audio data for all 6 sensors. In these configurations, the speakers remain **silent** (``is_speech = False``), and **no extraneous noise is generated** around them ( ``is_noisy = False``). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used to generate synthetic datasets with realistic physiological (and sensor-inherent) noise captured by body-conduction sensors.
| Sensor | Recorded Hours | Download size | Number of individuals | Gender repartition M/F (in audio length) | Config name |
|:---:|:---:|:---:|:---:|:---:|:---:|
| Reference microphone (headset) | | | | | ``` phys_noise_mouth_headworn_reference_microphone ``` |
| In-ear microphone Type 1 (Comply Foam) | | | | | ``` phys_noise_in-ear_comply_foam_microphone ``` |
| In-ear microphone Type 2 (rigid earpiece) | | | | | ``` phys_noise_in-ear_rigid_earpiece_microphone ``` |
| Forehead (miniature accelerometer) | | | | | ``` phys_noise_forehead_miniature_accelerometer ``` |
| Temple contact microphone | | | | | ``` phys_noise_temple_contact_microphone ``` |
| Larynx piezoelectric sensor | | | | | ``` phys_noise_throat_piezoelectric_sensor ``` |
### Links and details :
- **Homepage:** [https://vibravox.cnam.fr](https://vibravox.cnam.fr)
- **Point of Contact:** [Eric Bavu](https://acoustique.cnam.fr/contacts/bavu/en/#contact)
- **Curated by:** AVA Team of the [LMSSC Research Laboratory](https://lmssc.cnam.fr)
- **Funded by:** French [Agence Nationale Pour la Recherche / AHEAD Project](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-5aac4914c7/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=fa352121b44b60bf6a5917180d5205e6)
- **Language:** French
- **License:** Creative Commons Attributions 4.0
<!--- **Repository:** [Needs More Information]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
- **Leaderboard:** [Needs More Information] -->
<!---- **Shared by [optional]:** [More Information Needed] -->
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
* bandwidth-extension : **TODO : explain**
## Dataset Creation
### Textual source data
The text data is collected from the french Wikipedia **(CITE SOURCE FILE IN COMMONVOICE)**
#### Audio Data Collection
| **Sensor** | **Image** | **Transducer** | **Online documentation** |
|:---------------------------|:---------------------|:-------------|:----------------------------------------------------------------------------------------------------------------------|
| Reference microphone (headset) |  | Shure WH20 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/airborne/index.html) |
| In-ear microphone Type 1 (Comply Foam) |  | STMicroelectronics MP34DT01 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/soft_inear/index.html) |
| In-ear microphone Type 2 (rigid earpiece) |  | Knowles SPH1642HT5H | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/rigid_inear/index.html) |
| Forehead (miniature accelerometer) | | Knowles BU23173-000 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/forehead/index.html) |
| Temple contact microphone |  | AKG C411 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/temple/index.html) |
| Larynx piezoelectric sensor |  | iXRadio XVTM822D-D35 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/throat/index.html) |
#### Data 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]
<!--The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation.
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment.
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).-->
#### Who are the source language producers?
Speakers are **TODO : explain**
<!-- 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]
#### Personal and Sensitive Information
The VibraVox dataset does not contain any data that might be considered as 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.).
A [consent form](https://vibravox.cnam.fr/documentation/consent/index.html) has been signed by each participant to the VibraVox dataset. This consent form has been approved by the Cnam lawyer. All [Cnil](https://www.cnil.fr/en) requirements have been checked, including the right to oblivion.
**TODO : describe the anonymization process.**
### Recommendations, Bias, Risks, and Limitations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## 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]
## Dataset Card Authors
Éric Bavu (https://huggingface.co/zinc75)
## Dataset Card Contact
[Eric Bavu](https://acoustique.cnam.fr/contacts/bavu/en/#contact)
|
Cnam-LMSSC/vibravox
|
[
"task_categories:audio-to-audio",
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:text-to-speech",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:fr",
"license:cc-by-4.0",
"region:us"
] |
2023-10-18T18:15:20+00:00
|
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"bwe_throat_piezoelectric_sensor/test-*"}]}]}
|
2023-12-20T20:29:14+00:00
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[] |
[
"fr"
] |
TAGS
#task_categories-audio-to-audio #task_categories-automatic-speech-recognition #task_categories-audio-classification #task_categories-text-to-speech #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #language-French #license-cc-by-4.0 #region-us
|
Dataset Card for VibraVox
=========================
### Dataset Summary
!image/png
The VibraVox dataset is a general purpose dataset of french speech captured with body-conduction transducers.
This dataset can be used for various audio machine learning tasks : Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme), Audio Bandwidth Extension (BWE), Speaker identification / recognition , Voice cloning , etc ...
Dataset Details
---------------
### Dataset Description
VibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled for release in early 2024. It includes speech recorded simultaneously using multiple body-conducted microphones :
* two in-ear microphones
* a bone conduction microphones
* a throat piezoelectric transducer
* a forehead miniature accelerometer
* a headset reference microphone located in front of the mouth.
Those sensors are described and documented in the dataset creation section.
The VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.
VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications.
Unlike traditional microphones, which rely on airborne sound waves, body-conduction microphones capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction microphones have been available for decades, their limited bandwidth has restricted their widespread usage. However, thanks to two tracks of improvements, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments.
### Example usage
VibraVox contains labelled data for 11 configurations tailored for specific tasks, mainly oriented towards Automatic Speech Recognition and Bandwidth Extension.
#### ASR Datasets
The ASR dataset configurations contain mono audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 6 different kinds of microphones.
Recording was carried out simultaneously on all 6 sensors. The audio files were sampled at 48 kHz and encoded as .wav PCM32 files.
To load a specific configuration simply use the following command :
can be any of the following : (full bandwidth microphone), (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).
#### BWE Datasets
The BWE dataset configurations contain stereo audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 5 different kinds of body conduction microphones (first channel), and a standard reference microphone (second channel).
Recording was carried out simultaneously on all 6 sensors, therefore the 5 BWE configurations only differ on the band-limited sensor used, and the reference microphone channel is the same as the data found in the configuration.
The stereo audio files are sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.
To load a specific configuration simply use the following command :
can be any of the following : (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).
To load all the sensors in a single dataset (intersection of validated audio for each sensor), use "bwe\_all" config name, which contains 6-channel audios sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.
### Dataset Structure
#### Data Instances
ASR datasets :
BWE datasets :
#### Data Fields
Common Data Fields for all datasets :
* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded (mono) audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* 'audio\_length' (float32) - the audio length in seconds.
* 'transcription' (string) - audio segment text (cased and with punctuation preserved)
* 'num\_channels' (int) - the number of audio channels in audio
* 'sensor\_id' (string) - a list of sensors used in this audio, ordered by channel number
* 'speaker\_id' (string) - id of speaker
* 'gender' (string) - gender of speaker (or )
* 'is\_speech' (bool) - wether the audio contains speech audio () or if the speaker remains silent ()
* 'is\_noisy' (bool) - wether the audio contains external environmental noise () or if the speaker is in a quiet recoring room ()
* 'split'(string) - split (can be "train", "val", or "test")
* 'sentence\_id' (string) - id of the pronounced sentence
Extra Data Fields for ASR datasets :
* 'text' (string) - audio segment normalized text (lower cased, no punctuation, diacritics replaced by standard 26 french alphabet letters, plus 3 accented characters : é,è,ê and ç -- which hold phonetic significance -- and the space character, which corresponds to 31 possible characters : ).
* 'phonemes' (string) - audio segment phonemized text (using exclusively the strict french IPA (33) characters : ).
Extra Data Fields for BWE datasets :
* 'is\_gold\_transcript' (bool) - wether the transcription has been validated using an external Whisper-large ASR process to ensure the speech matches the transcription() or if the obtained WER during automated post-processing is too high to consider the audio and the transcription matches ().
#### Data Splits
Almost all configs contain data in three splits: train, validation and test (with a standard 80 / 10 / 10 repartition, without overlapping any speaker in each split).
Noise-only configs (Physiological noise and environmental noise, with ), which are intended for data augmentation only contain a train split.
Speech in noise config 'SiN' configuration (intended for extreme tests) contains only a test split.
### Data statistics
The dataset has been prepared for the different available sensors, for two main tasks : Automatic Speech Recognition (Speech-to-Text and Speech-to-Phonemes) and speech Bandwidth extension.
The ASR configs of VibraVox contain labelled audio (transcribed with text, normalized text, and phonemic transcription on the strict french IPA alphabet) data for 6 sensors :
The BWE configs of VibraVox contain labelled audio (transcribed with text) data for 5 body-conduction sensors. Each audio file for BWE configs is a stereo wavfile (the first channel corresponds to the body-conduction sensor, the second channel to the reference aligned audio, captured with the reference headworn microphone).
VibraVox's external noise configs contain audio data for all 6 sensors. In these configurations, the speakers remain silent (''is\_speech = False''), and environmental noise is generated around them using a using a 5th order ambisonic 3D sound spatializer ( ''is\_noisy = True''). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used for realistic data-augmentation using noise captured by body-conduction sensors, with the inherent attenuation of each sensors on different device wearers.
VibraVox's physiological noise configs contain audio data for all 6 sensors. In these configurations, the speakers remain silent (''is\_speech = False''), and no extraneous noise is generated around them ( ''is\_noisy = False''). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used to generate synthetic datasets with realistic physiological (and sensor-inherent) noise captured by body-conduction sensors.
### Links and details :
* Homepage: URL
* Point of Contact: Eric Bavu
* Curated by: AVA Team of the LMSSC Research Laboratory
* Funded by: French Agence Nationale Pour la Recherche / AHEAD Project
* Language: French
* License: Creative Commons Attributions 4.0
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
* bandwidth-extension : TODO : explain
Dataset Creation
----------------
### Textual source data
The text data is collected from the french Wikipedia (CITE SOURCE FILE IN COMMONVOICE)
#### Audio Data Collection
#### Data Processing
#### Who are the source language producers?
Speakers are TODO : explain
#### Personal and Sensitive Information
The VibraVox dataset does not contain any data that might be considered as 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.).
A consent form has been signed by each participant to the VibraVox dataset. This consent form has been approved by the Cnam lawyer. All Cnil requirements have been checked, including the right to oblivion.
TODO : describe the anonymization process.
### Recommendations, Bias, Risks, and Limitations
[optional]
BibTeX:
APA:
Dataset Card Authors
--------------------
Éric Bavu (URL
Dataset Card Contact
--------------------
Eric Bavu
|
[
"### Dataset Summary\n\n\n!image/png\n\n\nThe VibraVox dataset is a general purpose dataset of french speech captured with body-conduction transducers.\nThis dataset can be used for various audio machine learning tasks : Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme), Audio Bandwidth Extension (BWE), Speaker identification / recognition , Voice cloning , etc ...\n\n\nDataset Details\n---------------",
"### Dataset Description\n\n\nVibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled for release in early 2024. It includes speech recorded simultaneously using multiple body-conducted microphones :\n\n\n* two in-ear microphones\n* a bone conduction microphones\n* a throat piezoelectric transducer\n* a forehead miniature accelerometer\n* a headset reference microphone located in front of the mouth.\n\n\nThose sensors are described and documented in the dataset creation section.\n\n\nThe VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.\nVibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications.\nUnlike traditional microphones, which rely on airborne sound waves, body-conduction microphones capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction microphones have been available for decades, their limited bandwidth has restricted their widespread usage. However, thanks to two tracks of improvements, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments.",
"### Example usage\n\n\nVibraVox contains labelled data for 11 configurations tailored for specific tasks, mainly oriented towards Automatic Speech Recognition and Bandwidth Extension.",
"#### ASR Datasets\n\n\nThe ASR dataset configurations contain mono audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 6 different kinds of microphones.\nRecording was carried out simultaneously on all 6 sensors. The audio files were sampled at 48 kHz and encoded as .wav PCM32 files.\n\n\nTo load a specific configuration simply use the following command :\n\n\ncan be any of the following : (full bandwidth microphone), (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).",
"#### BWE Datasets\n\n\nThe BWE dataset configurations contain stereo audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 5 different kinds of body conduction microphones (first channel), and a standard reference microphone (second channel).\nRecording was carried out simultaneously on all 6 sensors, therefore the 5 BWE configurations only differ on the band-limited sensor used, and the reference microphone channel is the same as the data found in the configuration.\n\n\nThe stereo audio files are sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.\n\n\nTo load a specific configuration simply use the following command :\n\n\ncan be any of the following : (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).\n\n\nTo load all the sensors in a single dataset (intersection of validated audio for each sensor), use \"bwe\\_all\" config name, which contains 6-channel audios sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.",
"### Dataset Structure",
"#### Data Instances\n\n\nASR datasets :\n\n\nBWE datasets :",
"#### Data Fields\n\n\nCommon Data Fields for all datasets :\n\n\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded (mono) audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'audio\\_length' (float32) - the audio length in seconds.\n* 'transcription' (string) - audio segment text (cased and with punctuation preserved)\n* 'num\\_channels' (int) - the number of audio channels in audio\n* 'sensor\\_id' (string) - a list of sensors used in this audio, ordered by channel number\n* 'speaker\\_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (or )\n* 'is\\_speech' (bool) - wether the audio contains speech audio () or if the speaker remains silent ()\n* 'is\\_noisy' (bool) - wether the audio contains external environmental noise () or if the speaker is in a quiet recoring room ()\n* 'split'(string) - split (can be \"train\", \"val\", or \"test\")\n* 'sentence\\_id' (string) - id of the pronounced sentence\n\n\nExtra Data Fields for ASR datasets :\n\n\n* 'text' (string) - audio segment normalized text (lower cased, no punctuation, diacritics replaced by standard 26 french alphabet letters, plus 3 accented characters : é,è,ê and ç -- which hold phonetic significance -- and the space character, which corresponds to 31 possible characters : ).\n* 'phonemes' (string) - audio segment phonemized text (using exclusively the strict french IPA (33) characters : ).\n\n\nExtra Data Fields for BWE datasets :\n\n\n* 'is\\_gold\\_transcript' (bool) - wether the transcription has been validated using an external Whisper-large ASR process to ensure the speech matches the transcription() or if the obtained WER during automated post-processing is too high to consider the audio and the transcription matches ().",
"#### Data Splits\n\n\nAlmost all configs contain data in three splits: train, validation and test (with a standard 80 / 10 / 10 repartition, without overlapping any speaker in each split).\n\n\nNoise-only configs (Physiological noise and environmental noise, with ), which are intended for data augmentation only contain a train split.\n\n\nSpeech in noise config 'SiN' configuration (intended for extreme tests) contains only a test split.",
"### Data statistics\n\n\nThe dataset has been prepared for the different available sensors, for two main tasks : Automatic Speech Recognition (Speech-to-Text and Speech-to-Phonemes) and speech Bandwidth extension.\n\n\nThe ASR configs of VibraVox contain labelled audio (transcribed with text, normalized text, and phonemic transcription on the strict french IPA alphabet) data for 6 sensors :\n\n\n\nThe BWE configs of VibraVox contain labelled audio (transcribed with text) data for 5 body-conduction sensors. Each audio file for BWE configs is a stereo wavfile (the first channel corresponds to the body-conduction sensor, the second channel to the reference aligned audio, captured with the reference headworn microphone).\n\n\n\nVibraVox's external noise configs contain audio data for all 6 sensors. In these configurations, the speakers remain silent (''is\\_speech = False''), and environmental noise is generated around them using a using a 5th order ambisonic 3D sound spatializer ( ''is\\_noisy = True''). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used for realistic data-augmentation using noise captured by body-conduction sensors, with the inherent attenuation of each sensors on different device wearers.\n\n\n\nVibraVox's physiological noise configs contain audio data for all 6 sensors. In these configurations, the speakers remain silent (''is\\_speech = False''), and no extraneous noise is generated around them ( ''is\\_noisy = False''). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used to generate synthetic datasets with realistic physiological (and sensor-inherent) noise captured by body-conduction sensors.",
"### Links and details :\n\n\n* Homepage: URL\n* Point of Contact: Eric Bavu\n* Curated by: AVA Team of the LMSSC Research Laboratory\n* Funded by: French Agence Nationale Pour la Recherche / AHEAD Project\n* Language: French\n* License: Creative Commons Attributions 4.0",
"### Supported Tasks and Leaderboards\n\n\n* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).\n* bandwidth-extension : TODO : explain\n\n\nDataset Creation\n----------------",
"### Textual source data\n\n\nThe text data is collected from the french Wikipedia (CITE SOURCE FILE IN COMMONVOICE)",
"#### Audio Data Collection",
"#### Data Processing",
"#### Who are the source language producers?\n\n\nSpeakers are TODO : explain",
"#### Personal and Sensitive Information\n\n\nThe VibraVox dataset does not contain any data that might be considered as 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.).\n\n\nA consent form has been signed by each participant to the VibraVox dataset. This consent form has been approved by the Cnam lawyer. All Cnil requirements have been checked, including the right to oblivion.\n\n\nTODO : describe the anonymization process.",
"### Recommendations, Bias, Risks, and Limitations\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nDataset Card Authors\n--------------------\n\n\nÉric Bavu (URL\n\n\nDataset Card Contact\n--------------------\n\n\nEric Bavu"
] |
[
"TAGS\n#task_categories-audio-to-audio #task_categories-automatic-speech-recognition #task_categories-audio-classification #task_categories-text-to-speech #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #language-French #license-cc-by-4.0 #region-us \n",
"### Dataset Summary\n\n\n!image/png\n\n\nThe VibraVox dataset is a general purpose dataset of french speech captured with body-conduction transducers.\nThis dataset can be used for various audio machine learning tasks : Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme), Audio Bandwidth Extension (BWE), Speaker identification / recognition , Voice cloning , etc ...\n\n\nDataset Details\n---------------",
"### Dataset Description\n\n\nVibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled for release in early 2024. It includes speech recorded simultaneously using multiple body-conducted microphones :\n\n\n* two in-ear microphones\n* a bone conduction microphones\n* a throat piezoelectric transducer\n* a forehead miniature accelerometer\n* a headset reference microphone located in front of the mouth.\n\n\nThose sensors are described and documented in the dataset creation section.\n\n\nThe VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.\nVibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications.\nUnlike traditional microphones, which rely on airborne sound waves, body-conduction microphones capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction microphones have been available for decades, their limited bandwidth has restricted their widespread usage. However, thanks to two tracks of improvements, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments.",
"### Example usage\n\n\nVibraVox contains labelled data for 11 configurations tailored for specific tasks, mainly oriented towards Automatic Speech Recognition and Bandwidth Extension.",
"#### ASR Datasets\n\n\nThe ASR dataset configurations contain mono audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 6 different kinds of microphones.\nRecording was carried out simultaneously on all 6 sensors. The audio files were sampled at 48 kHz and encoded as .wav PCM32 files.\n\n\nTo load a specific configuration simply use the following command :\n\n\ncan be any of the following : (full bandwidth microphone), (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).",
"#### BWE Datasets\n\n\nThe BWE dataset configurations contain stereo audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 5 different kinds of body conduction microphones (first channel), and a standard reference microphone (second channel).\nRecording was carried out simultaneously on all 6 sensors, therefore the 5 BWE configurations only differ on the band-limited sensor used, and the reference microphone channel is the same as the data found in the configuration.\n\n\nThe stereo audio files are sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.\n\n\nTo load a specific configuration simply use the following command :\n\n\ncan be any of the following : (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).\n\n\nTo load all the sensors in a single dataset (intersection of validated audio for each sensor), use \"bwe\\_all\" config name, which contains 6-channel audios sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.",
"### Dataset Structure",
"#### Data Instances\n\n\nASR datasets :\n\n\nBWE datasets :",
"#### Data Fields\n\n\nCommon Data Fields for all datasets :\n\n\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded (mono) audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'audio\\_length' (float32) - the audio length in seconds.\n* 'transcription' (string) - audio segment text (cased and with punctuation preserved)\n* 'num\\_channels' (int) - the number of audio channels in audio\n* 'sensor\\_id' (string) - a list of sensors used in this audio, ordered by channel number\n* 'speaker\\_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (or )\n* 'is\\_speech' (bool) - wether the audio contains speech audio () or if the speaker remains silent ()\n* 'is\\_noisy' (bool) - wether the audio contains external environmental noise () or if the speaker is in a quiet recoring room ()\n* 'split'(string) - split (can be \"train\", \"val\", or \"test\")\n* 'sentence\\_id' (string) - id of the pronounced sentence\n\n\nExtra Data Fields for ASR datasets :\n\n\n* 'text' (string) - audio segment normalized text (lower cased, no punctuation, diacritics replaced by standard 26 french alphabet letters, plus 3 accented characters : é,è,ê and ç -- which hold phonetic significance -- and the space character, which corresponds to 31 possible characters : ).\n* 'phonemes' (string) - audio segment phonemized text (using exclusively the strict french IPA (33) characters : ).\n\n\nExtra Data Fields for BWE datasets :\n\n\n* 'is\\_gold\\_transcript' (bool) - wether the transcription has been validated using an external Whisper-large ASR process to ensure the speech matches the transcription() or if the obtained WER during automated post-processing is too high to consider the audio and the transcription matches ().",
"#### Data Splits\n\n\nAlmost all configs contain data in three splits: train, validation and test (with a standard 80 / 10 / 10 repartition, without overlapping any speaker in each split).\n\n\nNoise-only configs (Physiological noise and environmental noise, with ), which are intended for data augmentation only contain a train split.\n\n\nSpeech in noise config 'SiN' configuration (intended for extreme tests) contains only a test split.",
"### Data statistics\n\n\nThe dataset has been prepared for the different available sensors, for two main tasks : Automatic Speech Recognition (Speech-to-Text and Speech-to-Phonemes) and speech Bandwidth extension.\n\n\nThe ASR configs of VibraVox contain labelled audio (transcribed with text, normalized text, and phonemic transcription on the strict french IPA alphabet) data for 6 sensors :\n\n\n\nThe BWE configs of VibraVox contain labelled audio (transcribed with text) data for 5 body-conduction sensors. Each audio file for BWE configs is a stereo wavfile (the first channel corresponds to the body-conduction sensor, the second channel to the reference aligned audio, captured with the reference headworn microphone).\n\n\n\nVibraVox's external noise configs contain audio data for all 6 sensors. In these configurations, the speakers remain silent (''is\\_speech = False''), and environmental noise is generated around them using a using a 5th order ambisonic 3D sound spatializer ( ''is\\_noisy = True''). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used for realistic data-augmentation using noise captured by body-conduction sensors, with the inherent attenuation of each sensors on different device wearers.\n\n\n\nVibraVox's physiological noise configs contain audio data for all 6 sensors. In these configurations, the speakers remain silent (''is\\_speech = False''), and no extraneous noise is generated around them ( ''is\\_noisy = False''). Wearers of the devices are free to move their bodies and faces, and can swallow and breathe naturally. This configuration can be conveniently used to generate synthetic datasets with realistic physiological (and sensor-inherent) noise captured by body-conduction sensors.",
"### Links and details :\n\n\n* Homepage: URL\n* Point of Contact: Eric Bavu\n* Curated by: AVA Team of the LMSSC Research Laboratory\n* Funded by: French Agence Nationale Pour la Recherche / AHEAD Project\n* Language: French\n* License: Creative Commons Attributions 4.0",
"### Supported Tasks and Leaderboards\n\n\n* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).\n* bandwidth-extension : TODO : explain\n\n\nDataset Creation\n----------------",
"### Textual source data\n\n\nThe text data is collected from the french Wikipedia (CITE SOURCE FILE IN COMMONVOICE)",
"#### Audio Data Collection",
"#### Data Processing",
"#### Who are the source language producers?\n\n\nSpeakers are TODO : explain",
"#### Personal and Sensitive Information\n\n\nThe VibraVox dataset does not contain any data that might be considered as 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.).\n\n\nA consent form has been signed by each participant to the VibraVox dataset. This consent form has been approved by the Cnam lawyer. All Cnil requirements have been checked, including the right to oblivion.\n\n\nTODO : describe the anonymization process.",
"### Recommendations, Bias, Risks, and Limitations\n\n\n[optional]\n\n\nBibTeX:\n\n\nAPA:\n\n\nDataset Card Authors\n--------------------\n\n\nÉric Bavu (URL\n\n\nDataset Card Contact\n--------------------\n\n\nEric Bavu"
] |
[
142,
107,
316,
42,
156,
303,
7,
18,
546,
107,
470,
63,
93,
31,
5,
5,
17,
138,
49
] |
[
"passage: TAGS\n#task_categories-audio-to-audio #task_categories-automatic-speech-recognition #task_categories-audio-classification #task_categories-text-to-speech #task_ids-speaker-identification #annotations_creators-expert-generated #language_creators-crowdsourced #language_creators-expert-generated #multilinguality-monolingual #size_categories-100K<n<1M #language-French #license-cc-by-4.0 #region-us \n### Dataset Summary\n\n\n!image/png\n\n\nThe VibraVox dataset is a general purpose dataset of french speech captured with body-conduction transducers.\nThis dataset can be used for various audio machine learning tasks : Automatic Speech Recognition (ASR) (Speech-to-Text , Speech-to-Phoneme), Audio Bandwidth Extension (BWE), Speaker identification / recognition , Voice cloning , etc ...\n\n\nDataset Details\n---------------",
"passage: ### Dataset Description\n\n\nVibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled for release in early 2024. It includes speech recorded simultaneously using multiple body-conducted microphones :\n\n\n* two in-ear microphones\n* a bone conduction microphones\n* a throat piezoelectric transducer\n* a forehead miniature accelerometer\n* a headset reference microphone located in front of the mouth.\n\n\nThose sensors are described and documented in the dataset creation section.\n\n\nThe VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.\nVibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications.\nUnlike traditional microphones, which rely on airborne sound waves, body-conduction microphones capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction microphones have been available for decades, their limited bandwidth has restricted their widespread usage. However, thanks to two tracks of improvements, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments.### Example usage\n\n\nVibraVox contains labelled data for 11 configurations tailored for specific tasks, mainly oriented towards Automatic Speech Recognition and Bandwidth Extension.#### ASR Datasets\n\n\nThe ASR dataset configurations contain mono audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 6 different kinds of microphones.\nRecording was carried out simultaneously on all 6 sensors. The audio files were sampled at 48 kHz and encoded as .wav PCM32 files.\n\n\nTo load a specific configuration simply use the following command :\n\n\ncan be any of the following : (full bandwidth microphone), (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).#### BWE Datasets\n\n\nThe BWE dataset configurations contain stereo audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 5 different kinds of body conduction microphones (first channel), and a standard reference microphone (second channel).\nRecording was carried out simultaneously on all 6 sensors, therefore the 5 BWE configurations only differ on the band-limited sensor used, and the reference microphone channel is the same as the data found in the configuration.\n\n\nThe stereo audio files are sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.\n\n\nTo load a specific configuration simply use the following command :\n\n\ncan be any of the following : (body conduction), (body conduction), (body-conduction, high frequency response), (body-conduction, low SNR), (body-conduction, high distorsion rate).\n\n\nTo load all the sensors in a single dataset (intersection of validated audio for each sensor), use \"bwe\\_all\" config name, which contains 6-channel audios sampled at 48 kHz and encoded as .wav PCM32 files. The label of the sensors on each channel is given in the feature of the dataset.",
"passage: ### Dataset Structure#### Data Instances\n\n\nASR datasets :\n\n\nBWE datasets :",
"passage: #### Data Fields\n\n\nCommon Data Fields for all datasets :\n\n\n* 'audio' (datasets.Audio) - a dictionary containing the path to the audio, the decoded (mono) audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).\n* 'audio\\_length' (float32) - the audio length in seconds.\n* 'transcription' (string) - audio segment text (cased and with punctuation preserved)\n* 'num\\_channels' (int) - the number of audio channels in audio\n* 'sensor\\_id' (string) - a list of sensors used in this audio, ordered by channel number\n* 'speaker\\_id' (string) - id of speaker\n* 'gender' (string) - gender of speaker (or )\n* 'is\\_speech' (bool) - wether the audio contains speech audio () or if the speaker remains silent ()\n* 'is\\_noisy' (bool) - wether the audio contains external environmental noise () or if the speaker is in a quiet recoring room ()\n* 'split'(string) - split (can be \"train\", \"val\", or \"test\")\n* 'sentence\\_id' (string) - id of the pronounced sentence\n\n\nExtra Data Fields for ASR datasets :\n\n\n* 'text' (string) - audio segment normalized text (lower cased, no punctuation, diacritics replaced by standard 26 french alphabet letters, plus 3 accented characters : é,è,ê and ç -- which hold phonetic significance -- and the space character, which corresponds to 31 possible characters : ).\n* 'phonemes' (string) - audio segment phonemized text (using exclusively the strict french IPA (33) characters : ).\n\n\nExtra Data Fields for BWE datasets :\n\n\n* 'is\\_gold\\_transcript' (bool) - wether the transcription has been validated using an external Whisper-large ASR process to ensure the speech matches the transcription() or if the obtained WER during automated post-processing is too high to consider the audio and the transcription matches ().#### Data Splits\n\n\nAlmost all configs contain data in three splits: train, validation and test (with a standard 80 / 10 / 10 repartition, without overlapping any speaker in each split).\n\n\nNoise-only configs (Physiological noise and environmental noise, with ), which are intended for data augmentation only contain a train split.\n\n\nSpeech in noise config 'SiN' configuration (intended for extreme tests) contains only a test split."
] |
e6950a2cbf3f8b769b5bd2d7f976a161932cd85e
|
# Dataset Card for Evaluation run of TehVenom/Pygmalion-Vicuna-1.1-7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TehVenom/Pygmalion-Vicuna-1.1-7b
- **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 [TehVenom/Pygmalion-Vicuna-1.1-7b](https://huggingface.co/TehVenom/Pygmalion-Vicuna-1.1-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_TehVenom__Pygmalion-Vicuna-1.1-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T19:33:50.994672](https://huggingface.co/datasets/open-llm-leaderboard/details_TehVenom__Pygmalion-Vicuna-1.1-7b/blob/main/results_2023-10-18T19-33-50.994672.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.02506291946308725,
"em_stderr": 0.0016008246934367497,
"f1": 0.07974098154362427,
"f1_stderr": 0.0019711203542724593,
"acc": 0.39098944274470687,
"acc_stderr": 0.00963637181216511
},
"harness|drop|3": {
"em": 0.02506291946308725,
"em_stderr": 0.0016008246934367497,
"f1": 0.07974098154362427,
"f1_stderr": 0.0019711203542724593
},
"harness|gsm8k|5": {
"acc": 0.0621683093252464,
"acc_stderr": 0.006651035644531721
},
"harness|winogrande|5": {
"acc": 0.7198105761641673,
"acc_stderr": 0.012621707979798499
}
}
```
### 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_TehVenom__Pygmalion-Vicuna-1.1-7b
|
[
"region:us"
] |
2023-10-18T18:33:55+00:00
|
{"pretty_name": "Evaluation run of TehVenom/Pygmalion-Vicuna-1.1-7b", "dataset_summary": "Dataset automatically created during the evaluation run of model [TehVenom/Pygmalion-Vicuna-1.1-7b](https://huggingface.co/TehVenom/Pygmalion-Vicuna-1.1-7b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_TehVenom__Pygmalion-Vicuna-1.1-7b\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T19:33:50.994672](https://huggingface.co/datasets/open-llm-leaderboard/details_TehVenom__Pygmalion-Vicuna-1.1-7b/blob/main/results_2023-10-18T19-33-50.994672.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.02506291946308725,\n \"em_stderr\": 0.0016008246934367497,\n \"f1\": 0.07974098154362427,\n \"f1_stderr\": 0.0019711203542724593,\n \"acc\": 0.39098944274470687,\n \"acc_stderr\": 0.00963637181216511\n },\n \"harness|drop|3\": {\n \"em\": 0.02506291946308725,\n \"em_stderr\": 0.0016008246934367497,\n \"f1\": 0.07974098154362427,\n \"f1_stderr\": 0.0019711203542724593\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0621683093252464,\n \"acc_stderr\": 0.006651035644531721\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7198105761641673,\n \"acc_stderr\": 0.012621707979798499\n }\n}\n```", "repo_url": "https://huggingface.co/TehVenom/Pygmalion-Vicuna-1.1-7b", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T19_33_50.994672", "path": ["**/details_harness|drop|3_2023-10-18T19-33-50.994672.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T19-33-50.994672.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T19_33_50.994672", "path": ["**/details_harness|gsm8k|5_2023-10-18T19-33-50.994672.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T19-33-50.994672.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T19_33_50.994672", "path": ["**/details_harness|winogrande|5_2023-10-18T19-33-50.994672.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T19-33-50.994672.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T19_33_50.994672", "path": ["results_2023-10-18T19-33-50.994672.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T19-33-50.994672.parquet"]}]}]}
|
2023-10-18T18:34:03+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of TehVenom/Pygmalion-Vicuna-1.1-7b
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model TehVenom/Pygmalion-Vicuna-1.1-7b on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T19:33:50.994672(note that their might be results for other tasks in 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 TehVenom/Pygmalion-Vicuna-1.1-7b",
"## 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 TehVenom/Pygmalion-Vicuna-1.1-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T19:33:50.994672(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"TAGS\n#region-us \n",
"# Dataset Card for Evaluation run of TehVenom/Pygmalion-Vicuna-1.1-7b",
"## 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 TehVenom/Pygmalion-Vicuna-1.1-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T19:33:50.994672(note that their might be results for other tasks in 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",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of TehVenom/Pygmalion-Vicuna-1.1-7b## 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 TehVenom/Pygmalion-Vicuna-1.1-7b on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T19:33:50.994672(note that their might be results for other tasks in 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"
] |
208377f1bbfafa6459febfba2a57c07421070b5c
|
# Dataset Card for "seizure_eeg_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
JLB-JLB/seizure_eeg_eval
|
[
"region:us"
] |
2023-10-18T18:45:16+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "epoch", "dtype": "int64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bckg", "1": "No Event", "2": "seiz"}}}}], "splits": [{"name": "train", "num_bytes": 3322082528.975, "num_examples": 114035}], "download_size": 3418833182, "dataset_size": 3322082528.975}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T18:50:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "seizure_eeg_eval"
More Information needed
|
[
"# Dataset Card for \"seizure_eeg_eval\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"seizure_eeg_eval\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"seizure_eeg_eval\"\n\nMore Information needed"
] |
cfa0c0f91d223ac47acbf582c96bbf5bef5d7c0e
|
This is a training data file for [Self-RAG](https://selfrag.github.io/) that generates outputs to diverse user queries as well as reflection tokens to call the retrieval system adaptively and criticize its own output and retrieved passages.
Self-RAG is trained on our 150k diverse instruction-output pairs with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback.
At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences. See full descriptions in [our paper](https://arxiv.org/abs/2310.11511) and [code](https://github.com/AkariAsai/self-rag).
## Citation and contact
If you use this model, please cite our work:
```
@article{asai2023selfrag,
author = {Asai, Akari and Wu, Zeqiu and Wang, Yizhong and Sil, Avirup and Hajishirzi, Hannaneh},
title = {{Self-RAG}: Learning to Retrieve, Generate, and Critique through Self-Reflection},
year = {2023},
journal = { arXiv preprint arXiv:2310.11511 },
URL = {https://arxiv.org/abs/2310.11511}
}
```
|
selfrag/selfrag_train_data
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"arxiv:2310.11511",
"region:us"
] |
2023-10-18T18:55:39+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"]}
|
2023-10-31T19:37:22+00:00
|
[
"2310.11511"
] |
[
"en"
] |
TAGS
#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-mit #arxiv-2310.11511 #region-us
|
This is a training data file for Self-RAG that generates outputs to diverse user queries as well as reflection tokens to call the retrieval system adaptively and criticize its own output and retrieved passages.
Self-RAG is trained on our 150k diverse instruction-output pairs with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback.
At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences. See full descriptions in our paper and code.
and contact
If you use this model, please cite our work:
|
[] |
[
"TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-mit #arxiv-2310.11511 #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-100K<n<1M #language-English #license-mit #arxiv-2310.11511 #region-us \n"
] |
fb4b247bf424aa46982df67f9e0034cd93ea020f
|
# Dataset Card for "embeddings-network"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ppxscal/embeddings-network
|
[
"region:us"
] |
2023-10-18T19:00:59+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "submitter", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "journal-ref", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "report-no", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "versions", "dtype": "string"}, {"name": "update_date", "dtype": "string"}, {"name": "authors_parsed", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 389578224, "num_examples": 2318918}], "download_size": 0, "dataset_size": 389578224}}
|
2023-11-07T19:21:04+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "embeddings-network"
More Information needed
|
[
"# Dataset Card for \"embeddings-network\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"embeddings-network\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"embeddings-network\"\n\nMore Information needed"
] |
b45a3ee164f3507d72031d9f09d524d137da0bbf
|
# Dataset Card for {{ pretty_name | default("Dataset Name", true) }}
<!-- Provide a quick summary of the dataset. -->
{{ dataset_summary | default("", true) }}
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
{{ dataset_description | default("", true) }}
- **Curated by:** {{ curators | default("[More Information Needed]", true)}}
- **Funded by [optional]:** {{ funded_by | default("[More Information Needed]", true)}}
- **Shared by [optional]:** {{ shared_by | default("[More Information Needed]", true)}}
- **Language(s) (NLP):** {{ language | default("[More Information Needed]", true)}}
- **License:** {{ license | default("[More Information Needed]", true)}}
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** {{ repo | default("[More Information Needed]", true)}}
- **Paper [optional]:** {{ paper | default("[More Information Needed]", true)}}
- **Demo [optional]:** {{ demo | default("[More Information Needed]", true)}}
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
{{ direct_use | default("[More Information Needed]", true)}}
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
{{ out_of_scope_use | default("[More Information Needed]", true)}}
## 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. -->
{{ dataset_structure | default("[More Information Needed]", true)}}
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
{{ curation_rationale_section | default("[More Information Needed]", true)}}
### 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. -->
{{ data_collection_and_processing_section | default("[More Information Needed]", true)}}
#### 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. -->
{{ source_data_producers_section | default("[More Information Needed]", true)}}
### 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. -->
{{ annotation_process_section | default("[More Information Needed]", true)}}
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
{{ who_are_annotators_section | default("[More Information Needed]", true)}}
#### 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. -->
{{ personal_and_sensitive_information | default("[More Information Needed]", true)}}
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
{{ bias_risks_limitations | default("[More Information Needed]", true)}}
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
{{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}
## 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:**
{{ citation_bibtex | default("[More Information Needed]", true)}}
**APA:**
{{ citation_apa | default("[More Information Needed]", true)}}
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
{{ glossary | default("[More Information Needed]", true)}}
## More Information [optional]
{{ more_information | default("[More Information Needed]", true)}}
## Dataset Card Authors [optional]
{{ dataset_card_authors | default("[More Information Needed]", true)}}
## Dataset Card Contact
{{ dataset_card_contact | default("[More Information Needed]", true)}}
|
ICILS/isco_esco_occupations_taxonomy
|
[
"task_categories:text-classification",
"task_ids:multi-class-classification",
"region:us"
] |
2023-10-18T19:04:58+00:00
|
{"task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "pretty_name": "ISCO-ESCO Occupations Taxonomy", "dataset_info": [{"config_name": "isco_occupations", "features": [{"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "0", "1": "01", "2": "011", "3": "0110", "4": "02", "5": "021", "6": "0210", "7": "03", "8": "031", "9": "0310", "10": "1", "11": "11", "12": "111", "13": "1111", "14": "1112", "15": "1113", "16": "1114", "17": "112", "18": "1120", "19": "12", "20": "121", "21": "1211", "22": "1212", "23": "1213", "24": "1219", "25": "122", "26": "1221", "27": "1222", "28": "1223", "29": "13", "30": "131", "31": "1311", "32": "1312", "33": "132", "34": "1321", "35": "1322", "36": "1323", "37": "1324", "38": "133", "39": "1330", "40": "134", "41": "1341", "42": "1342", "43": "1343", "44": "1344", "45": "1345", "46": "1346", "47": "1349", "48": "14", "49": "141", "50": "1411", "51": "1412", "52": "142", "53": 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"splits": {"train_split": "train", "eval_split": "validation"}, "col_mapping": {"text": "text", "label": "labels"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}]}]}
|
2023-10-19T10:06:40+00:00
|
[] |
[] |
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #region-us
|
# Dataset Card for {{ pretty_name | default("Dataset Name", true) }}
{{ dataset_summary | default("", true) }}
## Dataset Details
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- License: {{ license | default("", true)}}
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- Paper [optional]: {{ paper | default("", true)}}
- Demo [optional]: {{ demo | default("", true)}}
## Uses
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## Dataset Creation
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#### Personal and Sensitive Information
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## Bias, Risks, and Limitations
{{ bias_risks_limitations | default("", true)}}
### Recommendations
{{ bias_recommendations | default("Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.", true)}}
[optional]
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APA:
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[
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[
"passage: TAGS\n#task_categories-text-classification #task_ids-multi-class-classification #region-us \n# Dataset Card for {{ pretty_name | default(\"Dataset Name\", true) }}\n\n\n\n{{ dataset_summary | default(\"\", true) }}## Dataset Details### Dataset Description\n\n\n\n{{ dataset_description | default(\"\", true) }}\n\n- Curated by: {{ curators | default(\"\", true)}}\n- Funded by [optional]: {{ funded_by | default(\"\", true)}}\n- Shared by [optional]: {{ shared_by | default(\"\", true)}}\n- Language(s) (NLP): {{ language | default(\"\", true)}}\n- License: {{ license | default(\"\", true)}}### Dataset Sources [optional]\n\n\n\n- Repository: {{ repo | default(\"\", true)}}\n- Paper [optional]: {{ paper | default(\"\", true)}}\n- Demo [optional]: {{ demo | default(\"\", true)}}## Uses### Direct Use\n\n\n\n{{ direct_use | default(\"\", true)}}### Out-of-Scope Use\n\n\n\n{{ out_of_scope_use | default(\"\", true)}}## Dataset Structure\n\n\n\n{{ dataset_structure | default(\"\", true)}}## Dataset Creation### Curation Rationale\n\n\n\n{{ curation_rationale_section | default(\"\", true)}}### Source Data#### Data Collection and Processing\n\n\n\n{{ data_collection_and_processing_section | default(\"\", true)}}#### Who are the source data producers?\n\n\n\n{{ source_data_producers_section | default(\"\", true)}}### Annotations [optional]#### Annotation process\n\n\n\n{{ annotation_process_section | default(\"\", true)}}#### Who are the annotators?\n\n\n\n{{ who_are_annotators_section | default(\"\", true)}}#### Personal and Sensitive Information\n\n\n\n{{ personal_and_sensitive_information | default(\"\", true)}}"
] |
204148248280bdfd627f5303556a727159d61e25
|
# Dataset Card for "processed_licibert_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Santp98/processed_licibert_dataset
|
[
"region:us"
] |
2023-10-18T19:06:06+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 777380400.0, "num_examples": 215939}], "download_size": 200499848, "dataset_size": 777380400.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T23:27:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "processed_licibert_dataset"
More Information needed
|
[
"# Dataset Card for \"processed_licibert_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"processed_licibert_dataset\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"processed_licibert_dataset\"\n\nMore Information needed"
] |
7dffd4961b37338d88f5cfc1cceac957dec058a9
|
# Dataset Card for MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain
## Table of Contents
- [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)
- [Annotations](#annotations)
- [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:**
- **Repository:**
- **Paper:** [MultiLegalPile](https://arxiv.org/abs/2306.02069)
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
The Multi_Legal_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
It spans over 24 languages and five legal text types. This version only contains data permissively licensed for commercial use.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt,
ro, sk, sl, sv
## Dataset Structure
It is structured in the following format:
type -> language -> jurisdiction.jsonl.xz
type is one of the following:
- caselaw
- contracts
- legislation
- other
- legal_mc4
`legal_mc4` is a subset of the other type but is listed separately so it can be easily excluded since it is less
permissively licensed than the other types.
Use the dataset like this:
```python
from datasets import load_dataset
config = 'en_contracts' # {language}_{type}
dataset = load_dataset('joelito/Multi_Legal_Pile', config, split='train', streaming=True)
```
'config' is a combination of language and text_type, e.g. 'en_contracts' or 'de_caselaw'.
To load all the languages or all the text_types, use 'all' instead of the language or text_type (e.g., '
all_legislation').
### Data Instances
The file format is jsonl.xz and there is one split available ("train").
The complete dataset (689GB) consists of four large subsets:
- Native Multi Legal Pile (112GB)
- Eurlex Resources (179GB)
- Legal MC4 (106GB)
- Pile of Law (292GB)
#### Native Multilingual Legal Pile data
| | Language | Text Type | Jurisdiction | Source | Size (MB) | Words | Documents | Words/Document | URL | License |
|---:|:-----------|:------------|:---------------|:-----------------------------------|------------:|------------:|------------:|-----------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|
| 0 | bg | legislation | Bulgaria | MARCELL | 8015 | 308946116 | 82777 | 3732 | https://elrc-share.eu/repository/browse/marcell-bulgarian-legislative-subcorpus-v2/946267fe8d8711eb9c1a00155d026706d2c9267e5cdf4d75b5f02168f01906c6/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 1 | cs | caselaw | Czechia | CzCDC Constitutional Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| 2 | cs | caselaw | Czechia | CzCDC Supreme Administrative Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| 3 | cs | caselaw | Czechia | CzCDC Supreme Court | 11151 | 574336489 | 296652 | 1936 | https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-3052 | [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) |
| 4 | da | caselaw | Denmark | DDSC | 3469 | 210730560 | 89702 | 2349 | https://huggingface.co/DDSC | [CC BY 4.0 and other, depending on the dataset](https://creativecommons.org/licenses/by-nc/4.0/) |
| 5 | da | legislation | Denmark | DDSC | 10736 | 653153146 | 265868 | 2456 | https://huggingface.co/DDSC | [CC BY 4.0 and other, depending on the dataset](https://creativecommons.org/licenses/by-nc/4.0/) |
| 6 | de | caselaw | Germany | openlegaldata | 31527 | 1785439383 | 596800 | 2991 | https://de.openlegaldata.io/ | [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/) |
| 7 | de | caselaw | Switzerland | entscheidsuche | 31527 | 1785439383 | 596800 | 2991 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) |
| 8 | de | legislation | Germany | openlegaldata | 8934 | 512840663 | 276034 | 1857 | https://de.openlegaldata.io/ | [ODbL-1.0](https://opendatacommons.org/licenses/odbl/1-0/) |
| 9 | de | legislation | Switzerland | lexfind | 8934 | 512840663 | 276034 | 1857 | https://www.lexfind.ch/fe/de/search | No information provided |
| 10 | fr | caselaw | Switzerland | entscheidsuche | 18313 | 1170335690 | 435569 | 2686 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) |
| 11 | fr | caselaw | Belgium | jurportal | 18313 | 1170335690 | 435569 | 2686 | https://juportal.be/home/welkom | [See description](https://juportal.be/home/disclaimer) |
| 12 | fr | caselaw | France | CASS | 18313 | 1170335690 | 435569 | 2686 | https://echanges.dila.gouv.fr/OPENDATA/CASS/ | [Open Licence 2.0](https://echanges.dila.gouv.fr/OPENDATA/CASS/DILA_CASS_Presentation_20170824.pdf) |
| 13 | fr | caselaw | Luxembourg | judoc | 18313 | 1170335690 | 435569 | 2686 | https://justice.public.lu/fr.html | [See description](https://justice.public.lu/fr/support/aspects-legaux/conditions-generales.html) |
| 14 | it | caselaw | Switzerland | entscheidsuche | 6483 | 406520336 | 156630 | 2595 | https://entscheidsuche.ch/ | [See description](https://entscheidsuche.ch/dataUsage) |
| 15 | en | legislation | Switzerland | lexfind | 36587 | 2537696894 | 657805 | 3857 | https://www.lexfind.ch/fe/de/search | No information provided |
| 16 | en | legislation | UK | uk-lex | 36587 | 2537696894 | 657805 | 3857 | https://zenodo.org/record/6355465 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode) |
| 17 | fr | legislation | Switzerland | lexfind | 9297 | 600170792 | 243313 | 2466 | https://www.lexfind.ch/fe/fr/search | No information provided |
| 18 | fr | legislation | Belgium | ejustice | 9297 | 600170792 | 243313 | 2466 | https://www.ejustice.just.fgov.be/cgi/welcome.pl | No information provided |
| 19 | it | legislation | Switzerland | lexfind | 8332 | 542579039 | 227968 | 2380 | https://www.lexfind.ch/fe/it/search | No information provided |
| 20 | nl | legislation | Belgium | ejustice | 8484 | 550788527 | 232204 | 2372 | https://www.ejustice.just.fgov.be/cgi/welcome.pl | No information provided |
| 21 | hu | legislation | Hungary | MARCELL | 5744 | 264572303 | 86862 | 3045 | https://elrc-share.eu/repository/browse/marcell-hungarian-legislative-subcorpus-v2/a87295ec8d6511eb9c1a00155d0267065f7e56dc7db34ce5aaae0b48a329daaa/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 22 | pl | legislation | Poland | MARCELL | 5459 | 299334705 | 89264 | 3353 | https://elrc-share.eu/repository/browse/marcell-polish-legislative-subcorpus-v2/dd14fa1c8d6811eb9c1a00155d026706c4718ddc9c6e4a92a88923816ca8b219/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 23 | pt | caselaw | Brazil | RulingBR | 196919 | 12611760973 | 17251236 | 731 | https://github.com/diego-feijo/rulingbr | No information provided |
| 24 | pt | caselaw | Brazil | CRETA | 196919 | 12611760973 | 17251236 | 731 | https://www.kaggle.com/datasets/eliasjacob/brcad5?resource=download&select=language_modeling_texts.parquet | [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) |
| 25 | pt | caselaw | Brazil | CJPG | 196919 | 12611760973 | 17251236 | 731 | https://esaj.tjsp.jus.br/cjsg/consultaCompleta.do?f=1 | No information provided |
| 26 | ro | legislation | Romania | MARCELL | 10464 | 559092153 | 215694 | 2592 | https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9cebe6889e/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 27 | sk | legislation | Slovakia | MARCELL | 5208 | 280182047 | 76760 | 3650 | https://elrc-share.eu/repository/browse/marcell-slovak-legislative-subcorpus-v2/6bdee1d68c8311eb9c1a00155d0267063398d3f1a3af40e1b728468dcbd6efdd/ | [CC0-1.0](https://elrc-share.eu/static/metashare/licences/CC0-1.0.pdf) |
| 28 | sl | legislation | Slovenia | MARCELL | 6057 | 365513763 | 88651 | 4123 | https://elrc-share.eu/repository/browse/marcell-slovenian-legislative-subcorpus-v2/e2a779868d4611eb9c1a00155d026706983c845a30d741b78e051faf91828b0d/ | [CC-BY-4.0](https://elrc-share.eu/static/metashare/licences/CC-BY-4.0.pdf)
| total | all | all | all | 1297609 | xxx | 81214262514 | 57305071 | 1417 | |
#### Eurlex Resources
See [Eurlex Resources](https://huggingface.co/datasets/joelito/eurlex_resources#data-instances) for more information.
#### Legal-MC4
See [Legal-MC4](https://huggingface.co/datasets/joelito/legal-mc4#data-instances) for more information.
#### Pile-of-Law
See [Pile-of-Law](https://huggingface.co/datasets/pile-of-law/pile-of-law#data-instances) for more information.
| Language | Type | Jurisdiction | Source | Size (MB) | Tokens | Documents | Tokens/Document | Part of Multi_Legal_Pile |
|:-----------|:------------|:---------------|:-------------------------------------|------------:|------------:|------------:|------------------:|:---------------------------|
| en | all | all | all | 503712 | 50547777921 | 9872444 | 5120 | yes |
| en | caselaw | EU | echr | 298 | 28374996 | 8480 | 3346 | yes |
| en | caselaw | Canada | canadian_decisions | 486 | 45438083 | 11343 | 4005 | yes |
| en | caselaw | US | dol_ecab | 942 | 99113541 | 28211 | 3513 | no |
| en | caselaw | US | scotus_oral_arguments | 1092 | 108228951 | 7996 | 13535 | no |
| en | caselaw | US | tax_rulings | 1704 | 166915887 | 54064 | 3087 | no |
| en | caselaw | US | nlrb_decisions | 2652 | 294471818 | 32080 | 9179 | no |
| en | caselaw | US | scotus_filings | 4018 | 593870413 | 63775 | 9311 | yes |
| en | caselaw | US | bva_opinions | 35238 | 4084140080 | 839523 | 4864 | no |
| en | caselaw | US | courtlistener_docket_entry_documents | 139006 | 12713614864 | 1983436 | 6409 | yes |
| en | caselaw | US | courtlistener_opinions | 158110 | 15899704961 | 4518445 | 3518 | yes |
| en | contracts | -- | tos | 4 | 391890 | 50 | 7837 | no |
| en | contracts | US | cfpb_creditcard_contracts | 188 | 25984824 | 2638 | 9850 | yes |
| en | contracts | US | edgar | 28698 | 2936402810 | 987926 | 2972 | yes |
| en | contracts | US | atticus_contracts | 78300 | 7997013703 | 650833 | 12287 | yes |
| en | legislation | US | fre | 2 | 173325 | 68 | 2548 | no |
| en | legislation | US | frcp | 4 | 427614 | 92 | 4647 | no |
| en | legislation | US | eoir | 62 | 6109737 | 2229 | 2741 | no |
| en | legislation | -- | constitutions | 66 | 5984865 | 187 | 32004 | yes |
| en | legislation | US | federal_register | 424 | 39854787 | 5414 | 7361 | yes |
| en | legislation | US | uscode | 716 | 78466325 | 58 | 1352867 | yes |
| en | legislation | EU | euro_parl | 808 | 71344326 | 9672 | 7376 | no |
| en | legislation | US | cfr | 1788 | 160849007 | 243 | 661930 | yes |
| en | legislation | US | us_bills | 3394 | 320723838 | 112483 | 2851 | yes |
| en | legislation | EU | eurlex | 3504 | 401324829 | 142036 | 2825 | no |
| en | legislation | US | state_codes | 18066 | 1858333235 | 217 | 8563747 | yes |
| en | other | -- | bar_exam_outlines | 4 | 346924 | 59 | 5880 | no |
| en | other | US | ftc_advisory_opinions | 4 | 509025 | 145 | 3510 | no |
| en | other | US | olc_memos | 98 | 12764635 | 1384 | 9223 | yes |
| en | other | -- | cc_casebooks | 258 | 24857378 | 73 | 340512 | no |
| en | other | -- | un_debates | 360 | 31152497 | 8481 | 3673 | no |
| en | other | -- | r_legaladvice | 798 | 72605386 | 146671 | 495 | no |
| en | other | US | founding_docs | 1118 | 100390231 | 183664 | 546 | no |
| en | other | US | oig | 5056 | 566782244 | 38954 | 14550 | yes |
| en | other | US | congressional_hearings | 16448 | 1801110892 | 31514 | 57152 | no |
### 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
```
@misc{niklaus2023multilegalpile,
title={MultiLegalPile: A 689GB Multilingual Legal Corpus},
author={Joel Niklaus and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho},
year={2023},
eprint={2306.02069},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
|
joelniklaus/Multi_Legal_Pile_Commercial
|
[
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hr",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-sa-4.0",
"arxiv:2306.02069",
"region:us"
] |
2023-10-18T19:23:08+00:00
|
{"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": "cc-by-sa-4.0", "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain"}
|
2023-10-18T19:40:00+00:00
|
[
"2306.02069"
] |
[
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hr",
"hu",
"it",
"lt",
"lv",
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"nl",
"pl",
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"ro",
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"sl",
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] |
TAGS
#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-sa-4.0 #arxiv-2306.02069 #region-us
|
Dataset Card for MultiLegalPile: A Large-Scale Multilingual Corpus for the Legal Domain
=======================================================================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ 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
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper: MultiLegalPile
* Leaderboard:
* Point of Contact: Joel Niklaus
### Dataset Summary
The Multi\_Legal\_Pile is a large-scale multilingual legal dataset suited for pretraining language models.
It spans over 24 languages and five legal text types. This version only contains data permissively licensed for commercial use.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt,
ro, sk, sl, sv
Dataset Structure
-----------------
It is structured in the following format:
type -> language -> URL
type is one of the following:
* caselaw
* contracts
* legislation
* other
* legal\_mc4
'legal\_mc4' is a subset of the other type but is listed separately so it can be easily excluded since it is less
permissively licensed than the other types.
Use the dataset like this:
'config' is a combination of language and text\_type, e.g. 'en\_contracts' or 'de\_caselaw'.
To load all the languages or all the text\_types, use 'all' instead of the language or text\_type (e.g., '
all\_legislation').
### Data Instances
The file format is URL and there is one split available ("train").
The complete dataset (689GB) consists of four large subsets:
* Native Multi Legal Pile (112GB)
* Eurlex Resources (179GB)
* Legal MC4 (106GB)
* Pile of Law (292GB)
#### Native Multilingual Legal Pile data
#### Eurlex Resources
See Eurlex Resources for more information.
#### Legal-MC4
See Legal-MC4 for more information.
#### Pile-of-Law
See Pile-of-Law for more information.
### 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
Thanks to @JoelNiklaus for adding this dataset.
|
[
"### Dataset Summary\n\n\nThe Multi\\_Legal\\_Pile is a large-scale multilingual legal dataset suited for pretraining language models.\nIt spans over 24 languages and five legal text types. This version only contains data permissively licensed for commercial use.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of fill-mask.",
"### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt,\nro, sk, sl, sv\n\n\nDataset Structure\n-----------------\n\n\nIt is structured in the following format:\ntype -> language -> URL\n\n\ntype is one of the following:\n\n\n* caselaw\n* contracts\n* legislation\n* other\n* legal\\_mc4\n\n\n'legal\\_mc4' is a subset of the other type but is listed separately so it can be easily excluded since it is less\npermissively licensed than the other types.\n\n\nUse the dataset like this:\n\n\n'config' is a combination of language and text\\_type, e.g. 'en\\_contracts' or 'de\\_caselaw'.\nTo load all the languages or all the text\\_types, use 'all' instead of the language or text\\_type (e.g., '\nall\\_legislation').",
"### Data Instances\n\n\nThe file format is URL and there is one split available (\"train\").\n\n\nThe complete dataset (689GB) consists of four large subsets:\n\n\n* Native Multi Legal Pile (112GB)\n* Eurlex Resources (179GB)\n* Legal MC4 (106GB)\n* Pile of Law (292GB)",
"#### Native Multilingual Legal Pile data",
"#### Eurlex Resources\n\n\nSee Eurlex Resources for more information.",
"#### Legal-MC4\n\n\nSee Legal-MC4 for more information.",
"#### Pile-of-Law\n\n\nSee Pile-of-Law for more information.",
"### Data Fields",
"### Data Splits\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset."
] |
[
"TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-sa-4.0 #arxiv-2306.02069 #region-us \n",
"### Dataset Summary\n\n\nThe Multi\\_Legal\\_Pile is a large-scale multilingual legal dataset suited for pretraining language models.\nIt spans over 24 languages and five legal text types. This version only contains data permissively licensed for commercial use.",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of fill-mask.",
"### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hr, hu, it, lt, lv, mt, nl, pl, pt,\nro, sk, sl, sv\n\n\nDataset Structure\n-----------------\n\n\nIt is structured in the following format:\ntype -> language -> URL\n\n\ntype is one of the following:\n\n\n* caselaw\n* contracts\n* legislation\n* other\n* legal\\_mc4\n\n\n'legal\\_mc4' is a subset of the other type but is listed separately so it can be easily excluded since it is less\npermissively licensed than the other types.\n\n\nUse the dataset like this:\n\n\n'config' is a combination of language and text\\_type, e.g. 'en\\_contracts' or 'de\\_caselaw'.\nTo load all the languages or all the text\\_types, use 'all' instead of the language or text\\_type (e.g., '\nall\\_legislation').",
"### Data Instances\n\n\nThe file format is URL and there is one split available (\"train\").\n\n\nThe complete dataset (689GB) consists of four large subsets:\n\n\n* Native Multi Legal Pile (112GB)\n* Eurlex Resources (179GB)\n* Legal MC4 (106GB)\n* Pile of Law (292GB)",
"#### Native Multilingual Legal Pile data",
"#### Eurlex Resources\n\n\nSee Eurlex Resources for more information.",
"#### Legal-MC4\n\n\nSee Legal-MC4 for more information.",
"#### Pile-of-Law\n\n\nSee Pile-of-Law for more information.",
"### Data Fields",
"### Data Splits\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset."
] |
[
219,
64,
24,
236,
75,
11,
15,
15,
21,
5,
11,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
6,
18
] |
[
"passage: TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Croatian #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-sa-4.0 #arxiv-2306.02069 #region-us \n### Dataset Summary\n\n\nThe Multi\\_Legal\\_Pile is a large-scale multilingual legal dataset suited for pretraining language models.\nIt spans over 24 languages and five legal text types. This version only contains data permissively licensed for commercial use.### Supported Tasks and Leaderboards\n\n\nThe dataset supports the tasks of fill-mask."
] |
f717a8618105acb4e41f72a26d1c73f9c4950fd4
|
# Dataset Card for "seizure_eeg_dev"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
JLB-JLB/seizure_eeg_dev
|
[
"region:us"
] |
2023-10-18T19:29:56+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "epoch", "dtype": "int64"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "bckg", "1": "No Event", "2": "seiz"}}}}], "splits": [{"name": "train", "num_bytes": 3322082528.975, "num_examples": 114035}], "download_size": 3418833182, "dataset_size": 3322082528.975}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T19:32:51+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "seizure_eeg_dev"
More Information needed
|
[
"# Dataset Card for \"seizure_eeg_dev\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"seizure_eeg_dev\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"seizure_eeg_dev\"\n\nMore Information needed"
] |
f2a739fbb572704fe5f8ceaad29a0834b62f982d
|
# Dataset Card for "eyeDiseasDdetectionModel"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mujammil131/eyeDiseasDdetectionModel
|
[
"region:us"
] |
2023-10-18T19:33:21+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "AMD", "1": "Cataract", "2": "Diabetes", "3": "Glaucoma", "4": "Hypertension", "5": "Myopia", "6": "Normal", "7": "Other"}}}}], "splits": [{"name": "train", "num_bytes": 379989329.608, "num_examples": 6392}], "download_size": 0, "dataset_size": 379989329.608}}
|
2023-10-19T06:49:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "eyeDiseasDdetectionModel"
More Information needed
|
[
"# Dataset Card for \"eyeDiseasDdetectionModel\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"eyeDiseasDdetectionModel\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"eyeDiseasDdetectionModel\"\n\nMore Information needed"
] |
14f7ada421b73164c5ed3577905e269dce54750d
|
# Dataset Card for "commonsense-dialogues2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
chrisgru/commonsense-dialogues2
|
[
"region:us"
] |
2023-10-18T19:41:13+00:00
|
{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 9152294, "num_examples": 20176}, {"name": "test", "num_bytes": 941561, "num_examples": 2158}, {"name": "validation", "num_bytes": 962952, "num_examples": 2157}], "download_size": 6212665, "dataset_size": 11056807}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}, {"split": "validation", "path": "data/validation-*"}]}]}
|
2023-10-19T12:07:25+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "commonsense-dialogues2"
More Information needed
|
[
"# Dataset Card for \"commonsense-dialogues2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"commonsense-dialogues2\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"commonsense-dialogues2\"\n\nMore Information needed"
] |
43c548d2086e21525fa5ab9db245e6caaced0b2e
|
# Dataset Card for "DDGC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/DDGC
|
[
"region:us"
] |
2023-10-18T19:44:06+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gloss", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9195547, "num_examples": 77350}], "download_size": 5758071, "dataset_size": 9195547}}
|
2024-01-09T16:47:23+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "DDGC"
More Information needed
|
[
"# Dataset Card for \"DDGC\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"DDGC\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"DDGC\"\n\nMore Information needed"
] |
eb75d1908235354b2e10720c90b9ebaf878d228a
|
# Dataset Card for "daily_dialog_gloss_Oct19_adj_adv_verb_noun_capital"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HamdanXI/daily_dialog_gloss_Oct19_adj_adv_verb_noun_capital
|
[
"region:us"
] |
2023-10-18T19:45:46+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gloss", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9195547, "num_examples": 77350}], "download_size": 5738024, "dataset_size": 9195547}}
|
2023-10-18T21:04:41+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "daily_dialog_gloss_Oct19_adj_adv_verb_noun_capital"
More Information needed
|
[
"# Dataset Card for \"daily_dialog_gloss_Oct19_adj_adv_verb_noun_capital\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"daily_dialog_gloss_Oct19_adj_adv_verb_noun_capital\"\n\nMore Information needed"
] |
[
6,
34
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"daily_dialog_gloss_Oct19_adj_adv_verb_noun_capital\"\n\nMore Information needed"
] |
553c0836aa4882d4327537e17d82dd6b64df98e3
|
This repo contains all the datasets contributed by "Multiple-Model Ensemble Learning for Wrong-Way-Cycling Prediction in Long-Form Video".
There are three zips:
- ```orientation_data.zip``` is for training orientation model.
- ```detection_data.zip``` is for training detection model.
- ```final_validation.zip``` contains 4 videos for final validation.
|
CATTAC/wrong-way-cycling
|
[
"license:apache-2.0",
"region:us"
] |
2023-10-18T19:50:51+00:00
|
{"license": "apache-2.0"}
|
2023-10-18T20:49:37+00:00
|
[] |
[] |
TAGS
#license-apache-2.0 #region-us
|
This repo contains all the datasets contributed by "Multiple-Model Ensemble Learning for Wrong-Way-Cycling Prediction in Long-Form Video".
There are three zips:
- is for training orientation model.
- is for training detection model.
- contains 4 videos for final validation.
|
[] |
[
"TAGS\n#license-apache-2.0 #region-us \n"
] |
[
14
] |
[
"passage: TAGS\n#license-apache-2.0 #region-us \n"
] |
7187e2b08764f8cf7aab1dbc256f9f9900886c03
|
## Example Data for "Mastering Pronoun Resolution in Conversational Models"
|
Fredithefish/Pronoun-Rich-Conversations
|
[
"language:en",
"region:us"
] |
2023-10-18T19:56:24+00:00
|
{"language": ["en"]}
|
2023-10-18T20:04:47+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #region-us
|
## Example Data for "Mastering Pronoun Resolution in Conversational Models"
|
[
"## Example Data for \"Mastering Pronoun Resolution in Conversational Models\""
] |
[
"TAGS\n#language-English #region-us \n",
"## Example Data for \"Mastering Pronoun Resolution in Conversational Models\""
] |
[
10,
20
] |
[
"passage: TAGS\n#language-English #region-us \n## Example Data for \"Mastering Pronoun Resolution in Conversational Models\""
] |
cdf64ab3d1dd0fa3df3ce73b18de8cb03e525e8e
|
# 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]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed]
|
jstack32/LatinAccents
|
[
"task_categories:automatic-speech-recognition",
"source_datasets:extended|common_voice",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-10-18T19:59:39+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": {"ab": ["10K<n<100K"], "ar": ["100K<n<1M"], "as": ["1K<n<10K"], "ast": ["n<1K"], "az": ["n<1K"], "ba": ["100K<n<1M"], "bas": ["1K<n<10K"], "be": ["100K<n<1M"], "bg": ["1K<n<10K"], "bn": ["100K<n<1M"], "br": ["10K<n<100K"], "ca": ["1M<n<10M"], "ckb": ["100K<n<1M"], "cnh": ["1K<n<10K"], "cs": ["10K<n<100K"], "cv": ["10K<n<100K"], "cy": ["100K<n<1M"], "da": ["1K<n<10K"], "de": ["100K<n<1M"], "dv": ["10K<n<100K"], "el": ["10K<n<100K"], "en": ["1M<n<10M"], "eo": ["1M<n<10M"], "es": ["1M<n<10M"], "et": ["10K<n<100K"], "eu": ["100K<n<1M"], "fa": ["100K<n<1M"], "fi": ["10K<n<100K"], "fr": ["100K<n<1M"], "fy-NL": ["10K<n<100K"], "ga-IE": ["1K<n<10K"], "gl": ["10K<n<100K"], "gn": ["1K<n<10K"], "ha": ["1K<n<10K"], "hi": ["10K<n<100K"], "hsb": ["1K<n<10K"], "hu": ["10K<n<100K"], "hy-AM": ["1K<n<10K"], "ia": ["10K<n<100K"], "id": ["10K<n<100K"], "ig": ["1K<n<10K"], "it": ["100K<n<1M"], "ja": ["10K<n<100K"], "ka": ["10K<n<100K"], "kab": ["100K<n<1M"], "kk": ["1K<n<10K"], "kmr": ["10K<n<100K"], "ky": ["10K<n<100K"], "lg": ["100K<n<1M"], "lt": ["10K<n<100K"], "lv": ["1K<n<10K"], "mdf": ["n<1K"], "mhr": ["100K<n<1M"], "mk": ["n<1K"], "ml": ["1K<n<10K"], "mn": ["10K<n<100K"], "mr": ["10K<n<100K"], "mrj": ["10K<n<100K"], "mt": ["10K<n<100K"], "myv": ["1K<n<10K"], "nan-tw": ["10K<n<100K"], "ne-NP": ["n<1K"], "nl": ["10K<n<100K"], "nn-NO": ["n<1K"], "or": ["1K<n<10K"], "pa-IN": ["1K<n<10K"], "pl": ["100K<n<1M"], "pt": ["100K<n<1M"], "rm-sursilv": ["1K<n<10K"], "rm-vallader": ["1K<n<10K"], "ro": ["10K<n<100K"], "ru": ["100K<n<1M"], "rw": ["1M<n<10M"], "sah": ["1K<n<10K"], "sat": ["n<1K"], "sc": ["1K<n<10K"], "sk": ["10K<n<100K"], "skr": ["1K<n<10K"], "sl": ["10K<n<100K"], "sr": ["1K<n<10K"], "sv-SE": ["10K<n<100K"], "sw": ["100K<n<1M"], "ta": ["100K<n<1M"], "th": ["100K<n<1M"], "ti": ["n<1K"], "tig": ["n<1K"], "tok": ["1K<n<10K"], "tr": ["10K<n<100K"], "tt": ["10K<n<100K"], "tw": ["n<1K"], "ug": ["10K<n<100K"], "uk": ["10K<n<100K"], "ur": ["100K<n<1M"], "uz": ["100K<n<1M"], "vi": ["10K<n<100K"], "vot": ["n<1K"], "yue": ["10K<n<100K"], "zh-CN": ["100K<n<1M"], "zh-HK": ["100K<n<1M"], "zh-TW": ["100K<n<1M"]}, "source_datasets": ["extended|common_voice"], "task_categories": ["automatic-speech-recognition"], "dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 102, "num_examples": 2}], "download_size": 0, "dataset_size": 102}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-20T21:04:35+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-automatic-speech-recognition #source_datasets-extended|common_voice #language-English #license-apache-2.0 #region-us
|
# 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
#### 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
|
[
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
[
"TAGS\n#task_categories-automatic-speech-recognition #source_datasets-extended|common_voice #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.",
"## Dataset Details",
"### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:",
"### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Dataset Card Authors [optional]",
"## Dataset Card Contact"
] |
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"passage: TAGS\n#task_categories-automatic-speech-recognition #source_datasets-extended|common_voice #language-English #license-apache-2.0 #region-us \n# Dataset Card for Dataset Name\n\n\n\nThis dataset card aims to be a base template for new datasets. It has been generated using this raw template.## Dataset Details### Dataset Description\n\n\n\n\n\n- Curated by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Language(s) (NLP): \n- License:### Dataset Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- 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\n\n\n\nUsers should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Dataset Card Authors [optional]## Dataset Card Contact"
] |
043f123df515603511c448a4361f568a405a1345
|
# Dataset Card for "arxiv_abstracts_probably_about_new_datasets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
librarian-bots/arxiv_abstracts_probably_about_new_datasets
|
[
"region:us"
] |
2023-10-18T19:59:44+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "submitter", "dtype": "string"}, {"name": "authors", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "comments", "dtype": "string"}, {"name": "journal-ref", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "report-no", "dtype": "string"}, {"name": "categories", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "versions", "list": [{"name": "version", "dtype": "string"}, {"name": "created", "dtype": "string"}]}, {"name": "update_date", "dtype": "timestamp[s]"}, {"name": "authors_parsed", "sequence": {"sequence": "string"}}, {"name": "prediction", "dtype": "string"}, {"name": "probability", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 85826043.97696729, "num_examples": 51826}], "download_size": 48360518, "dataset_size": 85826043.97696729}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T19:59:57+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "arxiv_abstracts_probably_about_new_datasets"
More Information needed
|
[
"# Dataset Card for \"arxiv_abstracts_probably_about_new_datasets\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"arxiv_abstracts_probably_about_new_datasets\"\n\nMore Information needed"
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6,
29
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"passage: TAGS\n#region-us \n# Dataset Card for \"arxiv_abstracts_probably_about_new_datasets\"\n\nMore Information needed"
] |
0eca7da999adec38507fc47bce6dff148bb9a43f
|
# Dataset Card for "hellaswag-portuguese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ceia-nlp/hellaswag-portuguese
|
[
"region:us"
] |
2023-10-18T20:01:27+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": "ind", "dtype": "int32"}, {"name": "activity_label", "dtype": "string"}, {"name": "ctx_a", "sequence": "string"}, {"name": "ctx_b", "sequence": "string"}, {"name": "ctx", "sequence": "string"}, {"name": "endings", "sequence": "string"}, {"name": "source_id", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "split_type", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 44275109, "num_examples": 39905}, {"name": "test", "num_bytes": 11058244, "num_examples": 10003}, {"name": "validation", "num_bytes": 11332175, "num_examples": 10042}], "download_size": 36875810, "dataset_size": 66665528}}
|
2023-10-23T21:39:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "hellaswag-portuguese"
More Information needed
|
[
"# Dataset Card for \"hellaswag-portuguese\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"hellaswag-portuguese\"\n\nMore Information needed"
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[
6,
17
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"passage: TAGS\n#region-us \n# Dataset Card for \"hellaswag-portuguese\"\n\nMore Information needed"
] |
6aeaca23f6e422cc89142946aec93240f3ad8770
|
# Dataset Card for Evaluation run of lmsys/longchat-13b-16k
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lmsys/longchat-13b-16k
- **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 [lmsys/longchat-13b-16k](https://huggingface.co/lmsys/longchat-13b-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lmsys__longchat-13b-16k",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T21:36:45.302966](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-13b-16k/blob/main/results_2023-10-18T21-36-45.302966.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": 0.2019714765100671,
"em_stderr": 0.004111439260592583,
"f1": 0.26415687919463077,
"f1_stderr": 0.004150479007025519,
"acc": 0.3712832244178191,
"acc_stderr": 0.009187422062133662
},
"harness|drop|3": {
"em": 0.2019714765100671,
"em_stderr": 0.004111439260592583,
"f1": 0.26415687919463077,
"f1_stderr": 0.004150479007025519
},
"harness|gsm8k|5": {
"acc": 0.04169825625473844,
"acc_stderr": 0.0055062050581757725
},
"harness|winogrande|5": {
"acc": 0.7008681925808997,
"acc_stderr": 0.012868639066091552
}
}
```
### 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_lmsys__longchat-13b-16k
|
[
"region:us"
] |
2023-10-18T20:36:49+00:00
|
{"pretty_name": "Evaluation run of lmsys/longchat-13b-16k", "dataset_summary": "Dataset automatically created during the evaluation run of model [lmsys/longchat-13b-16k](https://huggingface.co/lmsys/longchat-13b-16k) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lmsys__longchat-13b-16k\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T21:36:45.302966](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__longchat-13b-16k/blob/main/results_2023-10-18T21-36-45.302966.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\": 0.2019714765100671,\n \"em_stderr\": 0.004111439260592583,\n \"f1\": 0.26415687919463077,\n \"f1_stderr\": 0.004150479007025519,\n \"acc\": 0.3712832244178191,\n \"acc_stderr\": 0.009187422062133662\n },\n \"harness|drop|3\": {\n \"em\": 0.2019714765100671,\n \"em_stderr\": 0.004111439260592583,\n \"f1\": 0.26415687919463077,\n \"f1_stderr\": 0.004150479007025519\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04169825625473844,\n \"acc_stderr\": 0.0055062050581757725\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7008681925808997,\n \"acc_stderr\": 0.012868639066091552\n }\n}\n```", "repo_url": "https://huggingface.co/lmsys/longchat-13b-16k", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T21_36_45.302966", "path": ["**/details_harness|drop|3_2023-10-18T21-36-45.302966.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T21-36-45.302966.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T21_36_45.302966", "path": ["**/details_harness|gsm8k|5_2023-10-18T21-36-45.302966.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T21-36-45.302966.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T21_36_45.302966", "path": ["**/details_harness|winogrande|5_2023-10-18T21-36-45.302966.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T21-36-45.302966.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T21_36_45.302966", "path": ["results_2023-10-18T21-36-45.302966.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T21-36-45.302966.parquet"]}]}]}
|
2023-10-18T20:37:02+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of lmsys/longchat-13b-16k
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model lmsys/longchat-13b-16k on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T21:36:45.302966(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
|
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"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model lmsys/longchat-13b-16k on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
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"## 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 lmsys/longchat-13b-16k on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T21:36:45.302966(note that their might be results for other tasks in 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 lmsys/longchat-13b-16k## 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 lmsys/longchat-13b-16k on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T21:36:45.302966(note that their might be results for other tasks in 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"
] |
a3749e2be473f7919ca0a2278098722d62cec336
|
# Dataset Card for "paxqa_val_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
manestay/paxqa_val_test
|
[
"region:us"
] |
2023-10-18T20:40:13+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "source_id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context_en", "dtype": "string"}, {"name": "context_src", "dtype": "string"}, {"name": "question_en", "dtype": "string"}, {"name": "question_src", "dtype": "string"}, {"name": "answers_en", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "answers_src", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "mt5_score", "dtype": "float64"}, {"name": "bert_score", "dtype": "float64"}], "splits": [{"name": "test__gale_zh_en", "num_bytes": 311297, "num_examples": 190}, {"name": "validation__gale_zh_en", "num_bytes": 136405, "num_examples": 104}, {"name": "test__gale_ar_en", "num_bytes": 309546, "num_examples": 181}, {"name": "validation__gale_ar_en", "num_bytes": 239967, "num_examples": 132}, {"name": "test__gv_ar_en", "num_bytes": 149988, "num_examples": 87}, {"name": "validation__gv_ar_en", "num_bytes": 284910, "num_examples": 193}, {"name": "test__gv_ru_en", "num_bytes": 337718, "num_examples": 154}, {"name": "validation__gv_ru_en", "num_bytes": 311864, "num_examples": 157}, {"name": "test__nc_ru_en", "num_bytes": 138375, "num_examples": 166}, {"name": "validation__nc_ru_en", "num_bytes": 111749, "num_examples": 143}, {"name": "test__nc_zh_en", "num_bytes": 140032, "num_examples": 156}, {"name": "validation__nc_zh_en", "num_bytes": 113851, "num_examples": 125}], "download_size": 1015007, "dataset_size": 2585702}}
|
2023-10-18T20:42:26+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "paxqa_val_test"
More Information needed
|
[
"# Dataset Card for \"paxqa_val_test\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"paxqa_val_test\"\n\nMore Information needed"
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[
6,
17
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"passage: TAGS\n#region-us \n# Dataset Card for \"paxqa_val_test\"\n\nMore Information needed"
] |
bc6752cf7ef598916da5cee2d549a4ae9627fe4d
|
# Dataset Card for "covidQA_training"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
goodcoffee/covidQA_training
|
[
"region:us"
] |
2023-10-18T20:42:48+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "attention_mask", "sequence": "int64"}, {"name": "answer", "dtype": "string"}, {"name": "start_positions", "dtype": "int64"}, {"name": "end_positions", "dtype": "int64"}, {"name": "token_type_ids", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 17537973, "num_examples": 1413}], "download_size": 1417570, "dataset_size": 17537973}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T12:40:24+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "covidQA_training"
More Information needed
|
[
"# Dataset Card for \"covidQA_training\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"covidQA_training\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"covidQA_training\"\n\nMore Information needed"
] |
dcab7f393b4abf50915a0dcbf6bbfea8dce21cbd
|
# Dataset Card for "covidQA_eval"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
goodcoffee/covidQA_eval
|
[
"region:us"
] |
2023-10-18T20:42:53+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "attention_mask", "sequence": "int64"}, {"name": "answer", "dtype": "string"}, {"name": "start_positions", "dtype": "int64"}, {"name": "end_positions", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 414807, "num_examples": 50}], "download_size": 50631, "dataset_size": 414807}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T10:56:42+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "covidQA_eval"
More Information needed
|
[
"# Dataset Card for \"covidQA_eval\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"covidQA_eval\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"covidQA_eval\"\n\nMore Information needed"
] |
3e568808189dacacb655e0141f00a13046b92a93
|
# Dataset Card for "data-to16Hz"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
grasool/data-to16Hz
|
[
"region:us"
] |
2023-10-18T21:00:57+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10844658264, "num_examples": 11291}, {"name": "test", "num_bytes": 2710421968, "num_examples": 2822}], "download_size": 1783591438, "dataset_size": 13555080232}}
|
2023-10-18T21:02:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "data-to16Hz"
More Information needed
|
[
"# Dataset Card for \"data-to16Hz\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"data-to16Hz\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"data-to16Hz\"\n\nMore Information needed"
] |
d1b6f685bba97b6f14af5f256e2aebf44216261f
|
# Dataset Card for SecurityEval
This dataset is from the paper titled **SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques**.
The project is accepted for The first edition of the International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).
The paper describes the dataset for evaluating machine learning-based code generation output and the application of the dataset to the code generation tools.
## Dataset Details
### Dataset Description
- **Curated by:** Mohammed Latif Siddiq & Joanna C. S. Santos
- **Language(s):** Python
### Dataset Sources
- **Repository:** https://github.com/s2e-lab/SecurityEval
- **Paper:**
"SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques".
International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).
https://s2e-lab.github.io/preprints/msr4ps22-preprint.pdf
## Dataset Structure
- dataset.jsonl: dataset file in jsonl format. Every line contains a JSON object with the following fields:
- `ID`: unique identifier of the sample.
- `Prompt`: Prompt for the code generation model.
- `Insecure_code`: code of the vulnerability example that may be generated from the prompt.
## Citation
**BibTeX:**
```
@inproceedings{siddiq2022seceval,
author={Siddiq, Mohammed Latif and Santos, Joanna C. S. },
booktitle={Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S22)},
title={SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques},
year={2022},
doi={10.1145/3549035.3561184}
}
```
**APA:**
Siddiq, M. L., & Santos, J. C. (2022, November). SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (pp. 29-33).
|
s2e-lab/SecurityEval
|
[
"size_categories:n<1K",
"language:en",
"language:code",
"security",
"code-generation",
"region:us"
] |
2023-10-18T21:26:04+00:00
|
{"language": ["en", "code"], "size_categories": ["n<1K"], "task_categories": ["code-generation"], "tags": ["security", "code-generation"]}
|
2023-11-04T15:43:10+00:00
|
[] |
[
"en",
"code"
] |
TAGS
#size_categories-n<1K #language-English #language-code #security #code-generation #region-us
|
# Dataset Card for SecurityEval
This dataset is from the paper titled SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques.
The project is accepted for The first edition of the International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).
The paper describes the dataset for evaluating machine learning-based code generation output and the application of the dataset to the code generation tools.
## Dataset Details
### Dataset Description
- Curated by: Mohammed Latif Siddiq & Joanna C. S. Santos
- Language(s): Python
### Dataset Sources
- Repository: URL
- Paper:
"SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques".
International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).
URL
## Dataset Structure
- URL: dataset file in jsonl format. Every line contains a JSON object with the following fields:
- 'ID': unique identifier of the sample.
- 'Prompt': Prompt for the code generation model.
- 'Insecure_code': code of the vulnerability example that may be generated from the prompt.
BibTeX:
APA:
Siddiq, M. L., & Santos, J. C. (2022, November). SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (pp. 29-33).
|
[
"# Dataset Card for SecurityEval\n\n\nThis dataset is from the paper titled SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques. \nThe project is accepted for The first edition of the International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22). \nThe paper describes the dataset for evaluating machine learning-based code generation output and the application of the dataset to the code generation tools.",
"## Dataset Details",
"### Dataset Description\n\n\n- Curated by: Mohammed Latif Siddiq & Joanna C. S. Santos\n- Language(s): Python",
"### Dataset Sources\n\n\n- Repository: URL\n- Paper:\n\"SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques\".\nInternational Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).\nURL",
"## Dataset Structure\n\n- URL: dataset file in jsonl format. Every line contains a JSON object with the following fields:\n - 'ID': unique identifier of the sample.\n - 'Prompt': Prompt for the code generation model.\n - 'Insecure_code': code of the vulnerability example that may be generated from the prompt.\n\n\nBibTeX:\n\n\n\nAPA:\n\nSiddiq, M. L., & Santos, J. C. (2022, November). SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (pp. 29-33)."
] |
[
"TAGS\n#size_categories-n<1K #language-English #language-code #security #code-generation #region-us \n",
"# Dataset Card for SecurityEval\n\n\nThis dataset is from the paper titled SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques. \nThe project is accepted for The first edition of the International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22). \nThe paper describes the dataset for evaluating machine learning-based code generation output and the application of the dataset to the code generation tools.",
"## Dataset Details",
"### Dataset Description\n\n\n- Curated by: Mohammed Latif Siddiq & Joanna C. S. Santos\n- Language(s): Python",
"### Dataset Sources\n\n\n- Repository: URL\n- Paper:\n\"SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques\".\nInternational Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).\nURL",
"## Dataset Structure\n\n- URL: dataset file in jsonl format. Every line contains a JSON object with the following fields:\n - 'ID': unique identifier of the sample.\n - 'Prompt': Prompt for the code generation model.\n - 'Insecure_code': code of the vulnerability example that may be generated from the prompt.\n\n\nBibTeX:\n\n\n\nAPA:\n\nSiddiq, M. L., & Santos, J. C. (2022, November). SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (pp. 29-33)."
] |
[
31,
110,
4,
29,
71,
164
] |
[
"passage: TAGS\n#size_categories-n<1K #language-English #language-code #security #code-generation #region-us \n# Dataset Card for SecurityEval\n\n\nThis dataset is from the paper titled SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques. \nThe project is accepted for The first edition of the International Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22). \nThe paper describes the dataset for evaluating machine learning-based code generation output and the application of the dataset to the code generation tools.## Dataset Details### Dataset Description\n\n\n- Curated by: Mohammed Latif Siddiq & Joanna C. S. Santos\n- Language(s): Python### Dataset Sources\n\n\n- Repository: URL\n- Paper:\n\"SecurityEval Dataset: Mining Vulnerability Examples to Evaluate Machine Learning-Based Code Generation Techniques\".\nInternational Workshop on Mining Software Repositories Applications for Privacy and Security (MSR4P&S '22).\nURL## Dataset Structure\n\n- URL: dataset file in jsonl format. Every line contains a JSON object with the following fields:\n - 'ID': unique identifier of the sample.\n - 'Prompt': Prompt for the code generation model.\n - 'Insecure_code': code of the vulnerability example that may be generated from the prompt.\n\n\nBibTeX:\n\n\n\nAPA:\n\nSiddiq, M. L., & Santos, J. C. (2022, November). SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In Proceedings of the 1st International Workshop on Mining Software Repositories Applications for Privacy and Security (pp. 29-33)."
] |
c273a371dca6691458a6477f8c913a4e5c0ad03a
|
# Dataset Card for Evaluation run of yeontaek/llama-2-13b-Beluga-QLoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-13b-Beluga-QLoRA
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [yeontaek/llama-2-13b-Beluga-QLoRA](https://huggingface.co/yeontaek/llama-2-13b-Beluga-QLoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_yeontaek__llama-2-13b-Beluga-QLoRA",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T22:26:55.805701](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13b-Beluga-QLoRA/blob/main/results_2023-10-18T22-26-55.805701.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": 0.3896812080536913,
"em_stderr": 0.004994278468867637,
"f1": 0.44408871644295367,
"f1_stderr": 0.004822247735604221,
"acc": 0.3923953414757179,
"acc_stderr": 0.007449958542081619
},
"harness|drop|3": {
"em": 0.3896812080536913,
"em_stderr": 0.004994278468867637,
"f1": 0.44408871644295367,
"f1_stderr": 0.004822247735604221
},
"harness|gsm8k|5": {
"acc": 0.01288855193328279,
"acc_stderr": 0.003106901266499646
},
"harness|winogrande|5": {
"acc": 0.7719021310181531,
"acc_stderr": 0.011793015817663592
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_yeontaek__llama-2-13b-Beluga-QLoRA
|
[
"region:us"
] |
2023-10-18T21:27:00+00:00
|
{"pretty_name": "Evaluation run of yeontaek/llama-2-13b-Beluga-QLoRA", "dataset_summary": "Dataset automatically created during the evaluation run of model [yeontaek/llama-2-13b-Beluga-QLoRA](https://huggingface.co/yeontaek/llama-2-13b-Beluga-QLoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_yeontaek__llama-2-13b-Beluga-QLoRA\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T22:26:55.805701](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13b-Beluga-QLoRA/blob/main/results_2023-10-18T22-26-55.805701.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\": 0.3896812080536913,\n \"em_stderr\": 0.004994278468867637,\n \"f1\": 0.44408871644295367,\n \"f1_stderr\": 0.004822247735604221,\n \"acc\": 0.3923953414757179,\n \"acc_stderr\": 0.007449958542081619\n },\n \"harness|drop|3\": {\n \"em\": 0.3896812080536913,\n \"em_stderr\": 0.004994278468867637,\n \"f1\": 0.44408871644295367,\n \"f1_stderr\": 0.004822247735604221\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.01288855193328279,\n \"acc_stderr\": 0.003106901266499646\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7719021310181531,\n \"acc_stderr\": 0.011793015817663592\n }\n}\n```", "repo_url": "https://huggingface.co/yeontaek/llama-2-13b-Beluga-QLoRA", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T22_26_55.805701", "path": ["**/details_harness|drop|3_2023-10-18T22-26-55.805701.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T22-26-55.805701.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T22_26_55.805701", "path": ["**/details_harness|gsm8k|5_2023-10-18T22-26-55.805701.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T22-26-55.805701.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T22_26_55.805701", "path": ["**/details_harness|winogrande|5_2023-10-18T22-26-55.805701.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T22-26-55.805701.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T22_26_55.805701", "path": ["results_2023-10-18T22-26-55.805701.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T22-26-55.805701.parquet"]}]}]}
|
2023-10-18T21:27:08+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of yeontaek/llama-2-13b-Beluga-QLoRA
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model yeontaek/llama-2-13b-Beluga-QLoRA on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T22:26:55.805701(note that their might be results for other tasks in 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 yeontaek/llama-2-13b-Beluga-QLoRA",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model yeontaek/llama-2-13b-Beluga-QLoRA on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T22:26:55.805701(note that their might be results for other tasks in 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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"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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"TAGS\n#region-us \n",
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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 yeontaek/llama-2-13b-Beluga-QLoRA on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T22:26:55.805701(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of yeontaek/llama-2-13b-Beluga-QLoRA## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model yeontaek/llama-2-13b-Beluga-QLoRA on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T22:26:55.805701(note that their might be results for other tasks in 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"
] |
cc9acc729682c3824728a449d2f4b46e57130d69
|
# Dataset Card for "go_emotions_shrinked"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
aiancheruk/go_emotions_mini
|
[
"region:us"
] |
2023-10-18T21:29:50+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": "text", "dtype": "string"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "admiration", "1": "amusement", "2": "anger", "3": "annoyance", "4": "approval", "5": "caring", "6": "confusion", "7": "curiosity", "8": "desire", "9": "disappointment", "10": "disapproval", "11": "disgust", "12": "embarrassment", "13": "excitement", "14": "fear", "15": "gratitude", "16": "grief", "17": "joy", "18": "love", "19": "nervousness", "20": "optimism", "21": "pride", "22": "realization", "23": "relief", "24": "remorse", "25": "sadness", "26": "surprise", "27": "neutral"}}}}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48653.97373876987, "num_examples": 500}, {"name": "validation", "num_bytes": 9714.688536675267, "num_examples": 100}, {"name": "test", "num_bytes": 9663.589460106872, "num_examples": 100}], "download_size": 54811, "dataset_size": 68032.25173555201}}
|
2023-10-18T21:29:55+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "go_emotions_shrinked"
More Information needed
|
[
"# Dataset Card for \"go_emotions_shrinked\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"go_emotions_shrinked\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"go_emotions_shrinked\"\n\nMore Information needed"
] |
d4af0aadb581fca7cb45f09506a6c85833acfd0d
|
# Dataset Card for "firefly-train-chinese-zhtw"
## 資料集摘要
本資料集主要是應用於專案：[Firefly（流螢）: 中文對話式大語言模型](https://github.com/yangjianxin1/Firefly) ，經過訓練後得到的模型 [firefly-1b4](https://huggingface.co/YeungNLP/firefly-bloom-1b4)。
[Firefly（流螢）: 中文對話式大語言模型]專案(https://github.com/yangjianxin1/Firefly)收集了 23 個常見的中文資料集，并且對於每種不同的 NLP 任務，由人工書寫若干種指令模板來保證資料的高品質與豐富度。
資料量為115萬 。數據分佈如下圖所示:
訓練資料集的 token 長度分佈如下圖所示，絕大部分資料的長度都小於 600:
原始資料來源:
- [YeungNLP/firefly-train-1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
- [Firefly(流萤): 中文对话式大语言模型](https://github.com/yangjianxin1/Firefly)
## 資料下載清理
1. 下載 chinese-poetry: 最全中文诗歌古典文集数据库 的 Repo
2. 使用 OpenCC 來進行簡繁轉換
3. 使用 Huggingface Datasets 來上傳至 Huggingface Hub
## 資料集結構
```json
{
"kind": "ClassicalChinese",
"input": "將下面句子翻譯成現代文：\n石中央又生一樹，高百餘尺，條幹偃陰為五色，翠葉如盤，花徑尺餘，色深碧，蕊深紅，異香成煙，著物霏霏。",
"target": "大石的中央長著一棵樹，一百多尺高，枝幹是彩色的，樹葉有盤子那樣大，花的直徑有一尺寬，花瓣深藍色，花中飄出奇異的香氣籠罩著周圍，如煙似霧。"
}
```
## 資料欄位
- `kind`: (string) 任務類別
- `input`: (string) 任務輸入
- `target`: (string) 任務輸入目標
## 如何使用
```python
from datasets import load_dataset
dataset = load_dataset("erhwenkuo/firefly-train-chinese-zhtw", split="train")
```
## 許可資訊
資料來源未定義許可資訊
## 引用
```
@misc{Firefly,
author = {Jianxin Yang},
title = {Firefly(流萤): 中文对话式大语言模型},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/yangjianxin1/Firefly}},
}
```
|
erhwenkuo/firefly-train-chinese-zhtw
|
[
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"region:us"
] |
2023-10-18T22:00:50+00:00
|
{"language": ["zh"], "size_categories": ["1M<n<10M"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "kind", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1116720203, "num_examples": 1649399}], "download_size": 800075000, "dataset_size": 1116720203}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T12:07:50+00:00
|
[] |
[
"zh"
] |
TAGS
#task_categories-text-generation #size_categories-1M<n<10M #language-Chinese #region-us
|
# Dataset Card for "firefly-train-chinese-zhtw"
## 資料集摘要
本資料集主要是應用於專案：Firefly（流螢）: 中文對話式大語言模型 ，經過訓練後得到的模型 firefly-1b4。
[Firefly（流螢）: 中文對話式大語言模型]專案(URL)收集了 23 個常見的中文資料集，并且對於每種不同的 NLP 任務，由人工書寫若干種指令模板來保證資料的高品質與豐富度。
資料量為115萬 。數據分佈如下圖所示:
!task_distribution
訓練資料集的 token 長度分佈如下圖所示，絕大部分資料的長度都小於 600:
!len_distribution.png
原始資料來源:
- YeungNLP/firefly-train-1.1M
- Firefly(流萤): 中文对话式大语言模型
## 資料下載清理
1. 下載 chinese-poetry: 最全中文诗歌古典文集数据库 的 Repo
2. 使用 OpenCC 來進行簡繁轉換
3. 使用 Huggingface Datasets 來上傳至 Huggingface Hub
## 資料集結構
## 資料欄位
- 'kind': (string) 任務類別
- 'input': (string) 任務輸入
- 'target': (string) 任務輸入目標
## 如何使用
## 許可資訊
資料來源未定義許可資訊
## 引用
|
[
"# Dataset Card for \"firefly-train-chinese-zhtw\"",
"## 資料集摘要\n\n本資料集主要是應用於專案：Firefly（流螢）: 中文對話式大語言模型 ，經過訓練後得到的模型 firefly-1b4。\n\n[Firefly（流螢）: 中文對話式大語言模型]專案(URL)收集了 23 個常見的中文資料集，并且對於每種不同的 NLP 任務，由人工書寫若干種指令模板來保證資料的高品質與豐富度。\n\n資料量為115萬 。數據分佈如下圖所示:\n\n!task_distribution\n\n訓練資料集的 token 長度分佈如下圖所示，絕大部分資料的長度都小於 600:\n\n!len_distribution.png\n\n原始資料來源:\n\n- YeungNLP/firefly-train-1.1M\n- Firefly(流萤): 中文对话式大语言模型",
"## 資料下載清理\n\n1. 下載 chinese-poetry: 最全中文诗歌古典文集数据库 的 Repo\n2. 使用 OpenCC 來進行簡繁轉換\n3. 使用 Huggingface Datasets 來上傳至 Huggingface Hub",
"## 資料集結構",
"## 資料欄位\n\n- 'kind': (string) 任務類別\n- 'input': (string) 任務輸入\n- 'target': (string) 任務輸入目標",
"## 如何使用",
"## 許可資訊\n\n資料來源未定義許可資訊",
"## 引用"
] |
[
"TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Chinese #region-us \n",
"# Dataset Card for \"firefly-train-chinese-zhtw\"",
"## 資料集摘要\n\n本資料集主要是應用於專案：Firefly（流螢）: 中文對話式大語言模型 ，經過訓練後得到的模型 firefly-1b4。\n\n[Firefly（流螢）: 中文對話式大語言模型]專案(URL)收集了 23 個常見的中文資料集，并且對於每種不同的 NLP 任務，由人工書寫若干種指令模板來保證資料的高品質與豐富度。\n\n資料量為115萬 。數據分佈如下圖所示:\n\n!task_distribution\n\n訓練資料集的 token 長度分佈如下圖所示，絕大部分資料的長度都小於 600:\n\n!len_distribution.png\n\n原始資料來源:\n\n- YeungNLP/firefly-train-1.1M\n- Firefly(流萤): 中文对话式大语言模型",
"## 資料下載清理\n\n1. 下載 chinese-poetry: 最全中文诗歌古典文集数据库 的 Repo\n2. 使用 OpenCC 來進行簡繁轉換\n3. 使用 Huggingface Datasets 來上傳至 Huggingface Hub",
"## 資料集結構",
"## 資料欄位\n\n- 'kind': (string) 任務類別\n- 'input': (string) 任務輸入\n- 'target': (string) 任務輸入目標",
"## 如何使用",
"## 許可資訊\n\n資料來源未定義許可資訊",
"## 引用"
] |
[
34,
18,
188,
57,
5,
41,
4,
11,
3
] |
[
"passage: TAGS\n#task_categories-text-generation #size_categories-1M<n<10M #language-Chinese #region-us \n# Dataset Card for \"firefly-train-chinese-zhtw\"## 資料集摘要\n\n本資料集主要是應用於專案：Firefly（流螢）: 中文對話式大語言模型 ，經過訓練後得到的模型 firefly-1b4。\n\n[Firefly（流螢）: 中文對話式大語言模型]專案(URL)收集了 23 個常見的中文資料集，并且對於每種不同的 NLP 任務，由人工書寫若干種指令模板來保證資料的高品質與豐富度。\n\n資料量為115萬 。數據分佈如下圖所示:\n\n!task_distribution\n\n訓練資料集的 token 長度分佈如下圖所示，絕大部分資料的長度都小於 600:\n\n!len_distribution.png\n\n原始資料來源:\n\n- YeungNLP/firefly-train-1.1M\n- Firefly(流萤): 中文对话式大语言模型## 資料下載清理\n\n1. 下載 chinese-poetry: 最全中文诗歌古典文集数据库 的 Repo\n2. 使用 OpenCC 來進行簡繁轉換\n3. 使用 Huggingface Datasets 來上傳至 Huggingface Hub## 資料集結構## 資料欄位\n\n- 'kind': (string) 任務類別\n- 'input': (string) 任務輸入\n- 'target': (string) 任務輸入目標## 如何使用## 許可資訊\n\n資料來源未定義許可資訊## 引用"
] |
cc50802299678c256a07c3ea325429657f4ebd7c
|
# Dataset card for pale
## Table of contents
- [Dataset description](#dataset-description)
- [Dataset summary](#dataset-summary)
- [Dataset structure](#dataset-structure)
- [Dataset instance](#dataset-instance)
- [Dataset fields](#dataset-fields)
## Dataset description
- **Homepage:** [pale homepage](https://huggingface.co/datasets/zeio/pale)
- **Repository:** [pale repository](https://huggingface.co/datasets/zeio/pale)
- **Point of contact:** [Zeio Nara](mailto:[email protected])
- **Dataset version:** `30.10.2023`
### Dataset summary
This dataset contains league of legends champions' quotes parsed from [fandom](https://leagueoflegends.fandom.com).
See dataset viewer at the [derivative repo](/datasets/zeio/auto-pale).
See dataset usage example [at google colab](https://cutt.ly/3wEKDUI9).
The dataset is available in the following configurations:
1. `vanilla` - all data pulled from the website without significant modifications apart from the web page structure parsing;
1. `quotes` - truncated version of the corpus, which does't contain sound effects;
1. `annotated` - an extended version of the full configuration with a couple of additional columns with labels;
1. `pulled` - same as vanilla, but sound files have been pulled from the website, and `source` column is replaced with `sound`.
## Dataset structure
### Data instance
An example of an entry from the dataset is given below:
```json
{
"header": "Attack",
"subheader": "Attacking",
"text": "Kindred: \"The masks of the Kindred seek you!\"",
"source": "https://static.wikia.nocookie.net/leagueoflegends/images/1/12/Kindred_Original_Passive_Mark_Enemy_6.ogg/revision/latest?cb=20221204121356",
"champion": "kindred"
}
```
### Data fields
Each dataset entry therefore consists of the following fields:
- `header` - main category of the text;
- `subheader` - secondary category of the text (none in some cases);
- `text` - text said by the champion or description of sound made by the champion;
- `source` - link to the audio file (only `vanilla` configuration);
- `champion` - name of the champion in lowercase;
- `quote` - binary field displaying whether corresponding text contains quote or not (only `annotated` configuration);
- `sound` - audio data for the entry (only `pulled` configuration).
|
zeio/pale
|
[
"task_categories:text-generation",
"task_categories:text-classification",
"task_categories:automatic-speech-recognition",
"language_creators:crowdsourced",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"gaming",
"region:us"
] |
2023-10-18T22:16:36+00:00
|
{"language_creators": ["crowdsourced"], "language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text-classification", "automatic-speech-recognition"], "pretty_name": "pale", "tags": ["gaming"], "annotation_creators": ["crowdsourced"]}
|
2023-10-31T19:35:16+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-text-generation #task_categories-text-classification #task_categories-automatic-speech-recognition #language_creators-crowdsourced #size_categories-10K<n<100K #language-English #license-apache-2.0 #gaming #region-us
|
# Dataset card for pale
## Table of contents
- Dataset description
- Dataset summary
- Dataset structure
- Dataset instance
- Dataset fields
## Dataset description
- Homepage: pale homepage
- Repository: pale repository
- Point of contact: Zeio Nara
- Dataset version: '30.10.2023'
### Dataset summary
This dataset contains league of legends champions' quotes parsed from fandom.
See dataset viewer at the derivative repo.
See dataset usage example at google colab.
The dataset is available in the following configurations:
1. 'vanilla' - all data pulled from the website without significant modifications apart from the web page structure parsing;
1. 'quotes' - truncated version of the corpus, which does't contain sound effects;
1. 'annotated' - an extended version of the full configuration with a couple of additional columns with labels;
1. 'pulled' - same as vanilla, but sound files have been pulled from the website, and 'source' column is replaced with 'sound'.
## Dataset structure
### Data instance
An example of an entry from the dataset is given below:
### Data fields
Each dataset entry therefore consists of the following fields:
- 'header' - main category of the text;
- 'subheader' - secondary category of the text (none in some cases);
- 'text' - text said by the champion or description of sound made by the champion;
- 'source' - link to the audio file (only 'vanilla' configuration);
- 'champion' - name of the champion in lowercase;
- 'quote' - binary field displaying whether corresponding text contains quote or not (only 'annotated' configuration);
- 'sound' - audio data for the entry (only 'pulled' configuration).
|
[
"# Dataset card for pale",
"## Table of contents\n\n- Dataset description\n - Dataset summary\n- Dataset structure\n - Dataset instance\n - Dataset fields",
"## Dataset description\n\n- Homepage: pale homepage\n- Repository: pale repository\n- Point of contact: Zeio Nara\n- Dataset version: '30.10.2023'",
"### Dataset summary\n\nThis dataset contains league of legends champions' quotes parsed from fandom.\nSee dataset viewer at the derivative repo.\nSee dataset usage example at google colab.\nThe dataset is available in the following configurations:\n\n1. 'vanilla' - all data pulled from the website without significant modifications apart from the web page structure parsing;\n1. 'quotes' - truncated version of the corpus, which does't contain sound effects;\n1. 'annotated' - an extended version of the full configuration with a couple of additional columns with labels;\n1. 'pulled' - same as vanilla, but sound files have been pulled from the website, and 'source' column is replaced with 'sound'.",
"## Dataset structure",
"### Data instance\n\nAn example of an entry from the dataset is given below:",
"### Data fields\n\nEach dataset entry therefore consists of the following fields:\n\n- 'header' - main category of the text;\n- 'subheader' - secondary category of the text (none in some cases);\n- 'text' - text said by the champion or description of sound made by the champion;\n- 'source' - link to the audio file (only 'vanilla' configuration);\n- 'champion' - name of the champion in lowercase;\n- 'quote' - binary field displaying whether corresponding text contains quote or not (only 'annotated' configuration);\n- 'sound' - audio data for the entry (only 'pulled' configuration)."
] |
[
"TAGS\n#task_categories-text-generation #task_categories-text-classification #task_categories-automatic-speech-recognition #language_creators-crowdsourced #size_categories-10K<n<100K #language-English #license-apache-2.0 #gaming #region-us \n",
"# Dataset card for pale",
"## Table of contents\n\n- Dataset description\n - Dataset summary\n- Dataset structure\n - Dataset instance\n - Dataset fields",
"## Dataset description\n\n- Homepage: pale homepage\n- Repository: pale repository\n- Point of contact: Zeio Nara\n- Dataset version: '30.10.2023'",
"### Dataset summary\n\nThis dataset contains league of legends champions' quotes parsed from fandom.\nSee dataset viewer at the derivative repo.\nSee dataset usage example at google colab.\nThe dataset is available in the following configurations:\n\n1. 'vanilla' - all data pulled from the website without significant modifications apart from the web page structure parsing;\n1. 'quotes' - truncated version of the corpus, which does't contain sound effects;\n1. 'annotated' - an extended version of the full configuration with a couple of additional columns with labels;\n1. 'pulled' - same as vanilla, but sound files have been pulled from the website, and 'source' column is replaced with 'sound'.",
"## Dataset structure",
"### Data instance\n\nAn example of an entry from the dataset is given below:",
"### Data fields\n\nEach dataset entry therefore consists of the following fields:\n\n- 'header' - main category of the text;\n- 'subheader' - secondary category of the text (none in some cases);\n- 'text' - text said by the champion or description of sound made by the champion;\n- 'source' - link to the audio file (only 'vanilla' configuration);\n- 'champion' - name of the champion in lowercase;\n- 'quote' - binary field displaying whether corresponding text contains quote or not (only 'annotated' configuration);\n- 'sound' - audio data for the entry (only 'pulled' configuration)."
] |
[
82,
6,
26,
39,
167,
4,
17,
150
] |
[
"passage: TAGS\n#task_categories-text-generation #task_categories-text-classification #task_categories-automatic-speech-recognition #language_creators-crowdsourced #size_categories-10K<n<100K #language-English #license-apache-2.0 #gaming #region-us \n# Dataset card for pale## Table of contents\n\n- Dataset description\n - Dataset summary\n- Dataset structure\n - Dataset instance\n - Dataset fields## Dataset description\n\n- Homepage: pale homepage\n- Repository: pale repository\n- Point of contact: Zeio Nara\n- Dataset version: '30.10.2023'### Dataset summary\n\nThis dataset contains league of legends champions' quotes parsed from fandom.\nSee dataset viewer at the derivative repo.\nSee dataset usage example at google colab.\nThe dataset is available in the following configurations:\n\n1. 'vanilla' - all data pulled from the website without significant modifications apart from the web page structure parsing;\n1. 'quotes' - truncated version of the corpus, which does't contain sound effects;\n1. 'annotated' - an extended version of the full configuration with a couple of additional columns with labels;\n1. 'pulled' - same as vanilla, but sound files have been pulled from the website, and 'source' column is replaced with 'sound'.## Dataset structure### Data instance\n\nAn example of an entry from the dataset is given below:### Data fields\n\nEach dataset entry therefore consists of the following fields:\n\n- 'header' - main category of the text;\n- 'subheader' - secondary category of the text (none in some cases);\n- 'text' - text said by the champion or description of sound made by the champion;\n- 'source' - link to the audio file (only 'vanilla' configuration);\n- 'champion' - name of the champion in lowercase;\n- 'quote' - binary field displaying whether corresponding text contains quote or not (only 'annotated' configuration);\n- 'sound' - audio data for the entry (only 'pulled' configuration)."
] |
edbfefaa684848ee6cda61b74695642a9cafe57e
|
# Dataset Card for Evaluation run of upstage/Llama-2-70b-instruct-1024
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/upstage/Llama-2-70b-instruct-1024
- **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 [upstage/Llama-2-70b-instruct-1024](https://huggingface.co/upstage/Llama-2-70b-instruct-1024) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_upstage__Llama-2-70b-instruct-1024",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-18T23:28:06.884616](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__Llama-2-70b-instruct-1024/blob/main/results_2023-10-18T23-28-06.884616.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": 0.49989513422818793,
"em_stderr": 0.005120467878578845,
"f1": 0.5841736577181234,
"f1_stderr": 0.004671177225967014,
"acc": 0.5754715400500128,
"acc_stderr": 0.011730426388075654
},
"harness|drop|3": {
"em": 0.49989513422818793,
"em_stderr": 0.005120467878578845,
"f1": 0.5841736577181234,
"f1_stderr": 0.004671177225967014
},
"harness|gsm8k|5": {
"acc": 0.32221379833206976,
"acc_stderr": 0.01287243548118878
},
"harness|winogrande|5": {
"acc": 0.8287292817679558,
"acc_stderr": 0.010588417294962526
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
|
open-llm-leaderboard/details_upstage__Llama-2-70b-instruct-1024
|
[
"region:us"
] |
2023-10-18T22:28:10+00:00
|
{"pretty_name": "Evaluation run of upstage/Llama-2-70b-instruct-1024", "dataset_summary": "Dataset automatically created during the evaluation run of model [upstage/Llama-2-70b-instruct-1024](https://huggingface.co/upstage/Llama-2-70b-instruct-1024) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_upstage__Llama-2-70b-instruct-1024\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-18T23:28:06.884616](https://huggingface.co/datasets/open-llm-leaderboard/details_upstage__Llama-2-70b-instruct-1024/blob/main/results_2023-10-18T23-28-06.884616.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\": 0.49989513422818793,\n \"em_stderr\": 0.005120467878578845,\n \"f1\": 0.5841736577181234,\n \"f1_stderr\": 0.004671177225967014,\n \"acc\": 0.5754715400500128,\n \"acc_stderr\": 0.011730426388075654\n },\n \"harness|drop|3\": {\n \"em\": 0.49989513422818793,\n \"em_stderr\": 0.005120467878578845,\n \"f1\": 0.5841736577181234,\n \"f1_stderr\": 0.004671177225967014\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.32221379833206976,\n \"acc_stderr\": 0.01287243548118878\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.8287292817679558,\n \"acc_stderr\": 0.010588417294962526\n }\n}\n```", "repo_url": "https://huggingface.co/upstage/Llama-2-70b-instruct-1024", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_18T23_28_06.884616", "path": ["**/details_harness|drop|3_2023-10-18T23-28-06.884616.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-18T23-28-06.884616.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_18T23_28_06.884616", "path": ["**/details_harness|gsm8k|5_2023-10-18T23-28-06.884616.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-18T23-28-06.884616.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_18T23_28_06.884616", "path": ["**/details_harness|winogrande|5_2023-10-18T23-28-06.884616.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-18T23-28-06.884616.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_18T23_28_06.884616", "path": ["results_2023-10-18T23-28-06.884616.parquet"]}, {"split": "latest", "path": ["results_2023-10-18T23-28-06.884616.parquet"]}]}]}
|
2023-10-18T22:28:19+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of upstage/Llama-2-70b-instruct-1024
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model upstage/Llama-2-70b-instruct-1024 on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-18T23:28:06.884616(note that their might be results for other tasks in 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 upstage/Llama-2-70b-instruct-1024",
"## 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 upstage/Llama-2-70b-instruct-1024 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T23:28:06.884616(note that their might be results for other tasks in 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 upstage/Llama-2-70b-instruct-1024",
"## 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 upstage/Llama-2-70b-instruct-1024 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-18T23:28:06.884616(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
23,
31,
171,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of upstage/Llama-2-70b-instruct-1024## 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 upstage/Llama-2-70b-instruct-1024 on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-18T23:28:06.884616(note that their might be results for other tasks in 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"
] |
3099887452ece4cfaaf42c43ff0775190f24abde
|
# Dataset Card for "SlimOrcaEN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dim/SlimOrcaEN
|
[
"region:us"
] |
2023-10-18T22:54:18+00:00
|
{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "weight", "dtype": "float64"}]}, {"name": "key", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 928070255, "num_examples": 517982}], "download_size": 468726589, "dataset_size": 928070255}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-18T22:56:44+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SlimOrcaEN"
More Information needed
|
[
"# Dataset Card for \"SlimOrcaEN\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SlimOrcaEN\"\n\nMore Information needed"
] |
[
6,
15
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SlimOrcaEN\"\n\nMore Information needed"
] |
5a342c43e83d5da4abb2dafc70ed7907a08a8624
|
# Dataset Card for "SDv2-CLIP-aligned-6000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
Doub7e/SDv2-CLIP-aligned-6000
|
[
"region:us"
] |
2023-10-18T23:07:33+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "T5_last_hidden_states", "sequence": {"sequence": {"sequence": "float32"}}}], "splits": [{"name": "train", "num_bytes": 6017886905.25, "num_examples": 6014}], "download_size": 2715834079, "dataset_size": 6017886905.25}}
|
2023-11-17T12:11:07+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SDv2-CLIP-aligned-6000"
More Information needed
|
[
"# Dataset Card for \"SDv2-CLIP-aligned-6000\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SDv2-CLIP-aligned-6000\"\n\nMore Information needed"
] |
[
6,
20
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SDv2-CLIP-aligned-6000\"\n\nMore Information needed"
] |
8240e01bb1218f782851bddb799f270b85e72453
|
# Dataset Card for "SMILES-250M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
HoangHa/SMILES-250M
|
[
"region:us"
] |
2023-10-18T23:30:45+00:00
|
{"dataset_info": {"features": [{"name": "SMILES", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1626837623.1854794, "num_examples": 84345972}], "download_size": 1799608655, "dataset_size": 1626837623.1854794}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-22T05:06:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "SMILES-250M"
More Information needed
|
[
"# Dataset Card for \"SMILES-250M\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"SMILES-250M\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"SMILES-250M\"\n\nMore Information needed"
] |
1a1e4a8b30a0fd01a6351dbd69fd7468cf33aad6
|
# Dataset Card for "commonSpidersBalanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zkdeng/commonSpidersBalanced
|
[
"region:us"
] |
2023-10-18T23:45:24+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Aculepeira_ceropegia", "1": "Agalenatea_redii", "2": "Agelena_labyrinthica", "3": "Anasaitis_canosa", "4": "Anyphaena_accentuata", "5": "Aphonopelma_hentzi", "6": "Araneus_diadematus", "7": "Araneus_marmoreus", "8": "Araneus_quadratus", "9": "Araneus_trifolium", "10": "Araniella_displicata", "11": "Argiope_argentata", "12": "Argiope_aurantia", "13": "Argiope_bruennichi", "14": "Argiope_keyserlingi", "15": "Argiope_lobata", "16": "Argiope_trifasciata", "17": "Attulus_fasciger", "18": "Austracantha_minax", "19": "Badumna_longinqua", "20": "Carrhotus_xanthogramma", "21": "Colonus_hesperus", "22": "Colonus_sylvanus", "23": "Cyclosa_conica", "24": "Cyrtophora_citricola", "25": "Dolomedes_albineus", "26": "Dolomedes_minor", "27": "Dolomedes_scriptus", "28": "Dolomedes_tenebrosus", "29": "Dolomedes_triton", "30": "Dysdera_crocata", "31": "Ebrechtella_tricuspidata", "32": "Enoplognatha_ovata", "33": "Eratigena_duellica", "34": "Eriophora_ravilla", "35": "Eris_militaris", "36": "Evarcha_arcuata", "37": "Gasteracantha_cancriformis", "38": "Habronattus_pyrrithrix", "39": "Hasarius_adansoni", "40": "Helpis_minitabunda", "41": "Hentzia_mitrata", "42": "Hentzia_palmarum", "43": "Herpyllus_ecclesiasticus", "44": "Heteropoda_venatoria", "45": "Hogna_radiata", "46": "Holocnemus_pluchei", "47": "Kukulcania_hibernalis", "48": "Larinioides_cornutus", "49": "Larinioides_sclopetarius", "50": "Latrodectus_geometricus", "51": "Latrodectus_hesperus", "52": "Latrodectus_mactans", "53": "Leucauge_argyra", "54": "Leucauge_argyrobapta", "55": "Leucauge_dromedaria", "56": "Leucauge_venusta", "57": "Lyssomanes_viridis", "58": "Maevia_inclemens", "59": "Mangora_acalypha", "60": "Maratus_griseus", "61": "Marpissa_muscosa", "62": "Mecynogea_lemniscata", "63": "Menemerus_bivittatus", "64": "Menemerus_semilimbatus", "65": "Micrathena_gracilis", "66": "Micrathena_sagittata", "67": "Micrommata_virescens", "68": "Misumena_vatia", "69": "Misumenoides_formosipes", "70": "Misumessus_oblongus", "71": "Naphrys_pulex", "72": "Neoscona_arabesca", "73": "Neoscona_crucifera", "74": "Neoscona_oaxacensis", "75": "Nephila_pilipes", "76": "Neriene_radiata", "77": "Nesticodes_rufipes", "78": "Nuctenea_umbratica", "79": "Oxyopes_salticus", "80": "Oxyopes_scalaris", "81": "Paraphidippus_aurantius", "82": "Parasteatoda_tepidariorum", "83": "Peucetia_viridans", "84": "Phidippus_audax", "85": "Phidippus_clarus", "86": "Phidippus_johnsoni", "87": "Phidippus_putnami", "88": "Philaeus_chrysops", "89": "Philodromus_dispar", "90": "Pholcus_phalangioides", "91": "Pisaura_mirabilis", "92": "Pisaurina_mira", "93": "Platycryptus_californicus", "94": "Platycryptus_undatus", "95": "Plebs_eburnus", "96": "Plexippus_paykulli", "97": "Rabidosa_rabida", "98": "Salticus_scenicus", "99": "Sassacus_vitis", "100": "Scytodes_thoracica", "101": "Socca_pustulosa", "102": "Steatoda_grossa", "103": "Steatoda_nobilis", "104": "Steatoda_triangulosa", "105": "Synema_globosum", "106": "Thomisus_onustus", "107": "Trichonephila_clavata", "108": "Trichonephila_clavipes", "109": "Trichonephila_edulis", "110": "Trichonephila_plumipes", "111": "Verrucosa_arenata", "112": "Zoropsis_spinimana", "113": "Zygiella_x-notata"}}}}], "splits": [{"name": "train", "num_bytes": 3394498525.325, "num_examples": 166907}], "download_size": 3267608949, "dataset_size": 3394498525.325}}
|
2023-10-19T00:18:58+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "commonSpidersBalanced"
More Information needed
|
[
"# Dataset Card for \"commonSpidersBalanced\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"commonSpidersBalanced\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"commonSpidersBalanced\"\n\nMore Information needed"
] |
43bad8a3399614b5b749d94d70db2c84c1c7d278
|
# Dataset Card for "combinedSpidersBalanced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zkdeng/combinedSpidersBalanced
|
[
"region:us"
] |
2023-10-18T23:46:30+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Acantholycosa_lignaria", "1": "Aculepeira_ceropegia", "2": "Agalenatea_redii", "3": "Agelena_labyrinthica", "4": "Aglaoctenus_castaneus", "5": "Aglaoctenus_lagotis", "6": "Allocosa_funerea", "7": "Allotrochosina_schauinslandi", "8": "Alopecosa_albofasciata", "9": "Alopecosa_barbipes", "10": "Alopecosa_cuneata", "11": "Alopecosa_inquilina", "12": "Alopecosa_kochi", "13": "Alopecosa_pulverulenta", "14": "Anahita_punctulata", "15": "Anasaitis_canosa", "16": "Ancylometes_bogotensis", "17": "Ancylometes_concolor", "18": "Ancylometes_rufus", "19": "Anoteropsis_hilaris", "20": "Anoteropsis_litoralis", "21": "Anyphaena_accentuata", "22": "Aphonopelma_hentzi", "23": "Araneus_diadematus", "24": "Araneus_marmoreus", "25": "Araneus_quadratus", "26": "Araneus_trifolium", "27": "Araniella_displicata", "28": "Arctosa_cinerea", "29": "Arctosa_leopardus", "30": "Arctosa_littoralis", "31": "Arctosa_perita", "32": "Arctosa_personata", "33": "Argiope_argentata", "34": "Argiope_aurantia", "35": "Argiope_bruennichi", "36": "Argiope_keyserlingi", "37": "Argiope_lobata", "38": "Argiope_trifasciata", "39": "Asthenoctenus_borellii", "40": "Attulus_fasciger", "41": "Aulonia_albimana", "42": "Austracantha_minax", "43": "Badumna_longinqua", "44": "Carrhotus_xanthogramma", "45": "Centroctenus_brevipes", "46": "Cheiracanthium_erraticum", "47": "Cheiracanthium_gracile", "48": "Cheiracanthium_inclusum", "49": "Cheiracanthium_mildei", "50": "Cheiracanthium_punctorium", "51": "Colonus_hesperus", "52": "Colonus_sylvanus", "53": "Ctenus_amphora", "54": "Ctenus_hibernalis", "55": "Ctenus_medius", "56": "Ctenus_ornatus", "57": "Cupiennius_coccineus", "58": "Cupiennius_getazi", "59": "Cupiennius_salei", "60": "Cyclosa_conica", "61": "Cyrtophora_citricola", "62": "Diapontia_uruguayensis", "63": "Dolomedes_albineus", "64": "Dolomedes_minor", "65": "Dolomedes_scriptus", "66": "Dolomedes_tenebrosus", "67": "Dolomedes_triton", "68": "Dysdera_crocata", "69": "Ebrechtella_tricuspidata", "70": "Enoplognatha_ovata", "71": "Eratigena_agrestis", "72": "Eratigena_duellica", "73": "Eriophora_ravilla", "74": "Eris_militaris", "75": "Evarcha_arcuata", "76": "Gasteracantha_cancriformis", "77": "Geolycosa_vultuosa", "78": "Gladicosa_gulosa", "79": "Gladicosa_pulchra", "80": "Habronattus_pyrrithrix", "81": "Hasarius_adansoni", "82": "Helpis_minitabunda", "83": "Hentzia_mitrata", "84": "Hentzia_palmarum", "85": "Herpyllus_ecclesiasticus", "86": "Heteropoda_venatoria", "87": "Hippasa_holmerae", "88": "Hogna_antelucana", "89": "Hogna_baltimoriana", "90": "Hogna_bivittata", "91": "Hogna_carolinensis", "92": "Hogna_crispipes", "93": "Hogna_frondicola", "94": "Hogna_gumia", "95": "Hogna_radiata", "96": "Holocnemus_pluchei", "97": "Kukulcania_hibernalis", "98": "Lampona_cylindrata", "99": "Larinioides_cornutus", "100": "Larinioides_sclopetarius", "101": "Latrodectus_bishopi", "102": "Latrodectus_curacaviensis", "103": "Latrodectus_geometricus", "104": "Latrodectus_hasselti", "105": "Latrodectus_hesperus", "106": "Latrodectus_katipo", "107": "Latrodectus_mactans", "108": "Latrodectus_mirabilis", "109": "Latrodectus_renivulvatus", "110": "Latrodectus_tredecimguttatus", "111": "Latrodectus_variolus", "112": "Leucauge_argyra", "113": "Leucauge_argyrobapta", "114": "Leucauge_dromedaria", "115": "Leucauge_venusta", "116": "Loxosceles_amazonica", "117": "Loxosceles_deserta", "118": "Loxosceles_laeta", "119": "Loxosceles_reclusa", "120": "Loxosceles_rufescens", "121": "Loxosceles_tenochtitlan", "122": "Loxosceles_yucatana", "123": "Lycosa_erythrognatha", "124": "Lycosa_hispanica", "125": "Lycosa_pampeana", "126": "Lycosa_praegrandis", "127": "Lycosa_singoriensis", "128": "Lycosa_tarantula", "129": "Lyssomanes_viridis", "130": "Maevia_inclemens", "131": "Mangora_acalypha", "132": "Maratus_griseus", "133": "Marpissa_muscosa", "134": "Mecynogea_lemniscata", "135": "Menemerus_bivittatus", "136": "Menemerus_semilimbatus", "137": "Micrathena_gracilis", "138": "Micrathena_sagittata", "139": "Micrommata_virescens", "140": "Missulena_bradleyi", "141": "Missulena_occatoria", "142": "Misumena_vatia", "143": "Misumenoides_formosipes", "144": "Misumessus_oblongus", "145": "Naphrys_pulex", "146": "Neoscona_arabesca", "147": "Neoscona_crucifera", "148": "Neoscona_oaxacensis", "149": "Nephila_pilipes", "150": "Neriene_radiata", "151": "Nesticodes_rufipes", "152": "Nuctenea_umbratica", "153": "Oxyopes_salticus", "154": "Oxyopes_scalaris", "155": "Paraphidippus_aurantius", "156": "Parasteatoda_tepidariorum", "157": "Paratrochosina_amica", "158": "Pardosa_amentata", "159": "Pardosa_lapidicina", "160": "Pardosa_mercurialis", "161": "Pardosa_moesta", "162": "Pardosa_wagleri", "163": "Peucetia_viridans", "164": "Phidippus_audax", "165": "Phidippus_clarus", "166": "Phidippus_johnsoni", "167": "Phidippus_putnami", "168": "Philaeus_chrysops", "169": "Philodromus_dispar", "170": "Pholcus_phalangioides", "171": "Phoneutria_boliviensis", "172": "Phoneutria_depilata", "173": "Phoneutria_fera", "174": "Phoneutria_nigriventer", "175": "Phoneutria_pertyi", "176": "Phoneutria_reidyi", "177": "Pirata_piraticus", "178": "Pisaura_mirabilis", "179": "Pisaurina_mira", "180": "Platycryptus_californicus", "181": "Platycryptus_undatus", "182": "Plebs_eburnus", "183": "Plexippus_paykulli", "184": "Portacosa_cinerea", "185": "Rabidosa_hentzi", "186": "Rabidosa_punctulata", "187": "Rabidosa_rabida", "188": "Salticus_scenicus", "189": "Sassacus_vitis", "190": "Schizocosa_avida", "191": "Schizocosa_malitiosa", "192": "Schizocosa_mccooki", "193": "Scytodes_thoracica", "194": "Sicarius_thomisoides", "195": "Socca_pustulosa", "196": "Sosippus_californicus", "197": "Steatoda_grossa", "198": "Steatoda_nobilis", "199": "Steatoda_triangulosa", "200": "Synema_globosum", "201": "Thomisus_onustus", "202": "Tigrosa_annexa", "203": "Tigrosa_aspersa", "204": "Tigrosa_georgicola", "205": "Tigrosa_helluo", "206": "Trichonephila_clavata", "207": "Trichonephila_clavipes", "208": "Trichonephila_edulis", "209": "Trichonephila_plumipes", "210": "Trochosa_ruricola", "211": "Trochosa_sepulchralis", "212": "Trochosa_terricola", "213": "Tropicosa_moesta", "214": "Venator_immansuetus", "215": "Venator_spenceri", "216": "Venatrix_furcillata", "217": "Verrucosa_arenata", "218": "Wadicosa_fidelis", "219": "Xerolycosa_miniata", "220": "Xerolycosa_nemoralis", "221": "Zoropsis_spinimana", "222": "Zygiella_x-notata"}}}}], "splits": [{"name": "train", "num_bytes": 6290926802.784, "num_examples": 269172}], "download_size": 5634209308, "dataset_size": 6290926802.784}}
|
2023-10-19T00:35:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "combinedSpidersBalanced"
More Information needed
|
[
"# Dataset Card for \"combinedSpidersBalanced\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"combinedSpidersBalanced\"\n\nMore Information needed"
] |
[
6,
19
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"combinedSpidersBalanced\"\n\nMore Information needed"
] |
1b2e0cb098ef3d58584f80bde015207c5a3d00c7
|
# Dataset Card for "caps_data_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
advancedcv/Food500Cap
|
[
"region:us"
] |
2023-10-19T00:25:20+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "cat", "dtype": "string"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3004559279.747, "num_examples": 19877}, {"name": "test", "num_bytes": 601407879.384, "num_examples": 4938}], "download_size": 3000710601, "dataset_size": 3605967159.131}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
|
2023-10-19T01:01:16+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "caps_data_2"
More Information needed
|
[
"# Dataset Card for \"caps_data_2\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"caps_data_2\"\n\nMore Information needed"
] |
[
6,
16
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"caps_data_2\"\n\nMore Information needed"
] |
c91feedd39a35be4954d209d5ae4bedb4cd7db9a
|
# Dataset Card for "movie_posters"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
skvarre/movie_posters
|
[
"region:us"
] |
2023-10-19T00:31:05+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "title", "dtype": "string"}, {"name": "genres", "list": [{"name": "id", "dtype": "int64"}, {"name": "name", "dtype": "string"}]}, {"name": "overview", "dtype": "string"}, {"name": "popularity", "dtype": "float64"}, {"name": "release_date", "dtype": "string"}, {"name": "budget", "dtype": "int64"}, {"name": "revenue", "dtype": "int64"}, {"name": "tagline", "dtype": "string"}, {"name": "original_language", "dtype": "string"}, {"name": "runtime", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 4559499046.67, "num_examples": 9955}], "download_size": 4558666511, "dataset_size": 4559499046.67}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T00:35:45+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "movie_posters"
More Information needed
|
[
"# Dataset Card for \"movie_posters\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"movie_posters\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"movie_posters\"\n\nMore Information needed"
] |
91ef5c91274612fd3fe3ac18da5c7c820a39378e
|
Data for [**Flip-Flop Language Modeling**](https://arxiv.org/abs/2306.00946). The task is to correctly execute the sequential operations of a 1-bit register. The Transformer architecture, despite being apparently built for this operation, makes sporadic extrapolation errors (*attention glitches*). An open challenge is to fix these without recourse to long-tailed data or a recurrent architecture. Splits reflect the FFLM setup from the paper:
- `train`: 1.6M sequences from FFL(0.8) *(256 instructions, 80% ignore, 10% read, 10% write)*.
- `val`: 16K sequences from FFL(0.8).
- `val_dense`: 4K sequences from FFL(0.1).
- `val_sparse`: 160K sequences from FFL(0.98).
Usage
---
```python
import torch
import datasets
dataset = datasets.load_dataset('synthseq/flipflop')
dataset['train'][0] # {'text': 'w1i1w0i0 ...
def tokenize_batch(batch):
mapping = {'w': 0, 'r': 1, 'i': 2, '0': 3, '1': 4}
tokenized_batch = [[mapping[char] for char in s] for s in batch['text']]
return {'tokens': torch.tensor(tokenized_batch, dtype=torch.int64)}
dataset.set_transform(tokenize_batch)
dataset['train'][0] # {'tokens': tensor([0, 4, 2, 4, 0, 3, 2, 3, 2 ...
```
Citation
---
```
@article{liu2023exposing,
title={Exposing Attention Glitches with Flip-Flop Language Modeling},
author={Liu, Bingbin and Ash, Jordan T and Goel, Surbhi and Krishnamurthy, Akshay and Zhang, Cyril},
journal={arXiv preprint arXiv:2306.00946},
year={2023}
}
```
|
synthseq/flipflop
|
[
"license:mit",
"arxiv:2306.00946",
"region:us"
] |
2023-10-19T01:10:07+00:00
|
{"license": "mit", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "val", "path": "data/val-*"}, {"split": "val_dense", "path": "data/val_dense-*"}, {"split": "val_sparse", "path": "data/val_sparse-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 825600000, "num_examples": 1600000}, {"name": "val", "num_bytes": 8256000, "num_examples": 16000}, {"name": "val_dense", "num_bytes": 2064000, "num_examples": 4000}, {"name": "val_sparse", "num_bytes": 82560000, "num_examples": 160000}], "download_size": 354675733, "dataset_size": 918480000}}
|
2023-10-19T14:40:50+00:00
|
[
"2306.00946"
] |
[] |
TAGS
#license-mit #arxiv-2306.00946 #region-us
|
Data for Flip-Flop Language Modeling. The task is to correctly execute the sequential operations of a 1-bit register. The Transformer architecture, despite being apparently built for this operation, makes sporadic extrapolation errors (*attention glitches*). An open challenge is to fix these without recourse to long-tailed data or a recurrent architecture. Splits reflect the FFLM setup from the paper:
- 'train': 1.6M sequences from FFL(0.8) *(256 instructions, 80% ignore, 10% read, 10% write)*.
- 'val': 16K sequences from FFL(0.8).
- 'val_dense': 4K sequences from FFL(0.1).
- 'val_sparse': 160K sequences from FFL(0.98).
Usage
---
Citation
---
|
[] |
[
"TAGS\n#license-mit #arxiv-2306.00946 #region-us \n"
] |
[
19
] |
[
"passage: TAGS\n#license-mit #arxiv-2306.00946 #region-us \n"
] |
7f99a8c3bf0a05e1b5a934d7da0208891fe798f1
|
Bongard-OpenWorld
===
Bongard-OpenWorld is a new benchmark for evaluating real-world few-shot reasoning for machine vision. Please refer to our [project page](https://joyjayng.github.io/Bongard-OpenWorld.github.io/).
Some links may be invalid due to the instability of the URLs. To ensure that the community can reproduce our results from scratch, we have provided a backup of all the images. You can download from [Google Drive](https://drive.google.com/file/d/1aXr3ihVq0mtzbl6ZNJMogYEyEY-WALNr/view?usp=sharing).
Please note that this page only hosts the data for Bongard-OpenWorld. All images of Bongard-OpenWorld are crawled from [Google Images](https://images.google.com). We do not claim ownership of any image in Bongard-OpenWorld. Therefore, we strongly recommend that you delete all images immediately after benchmarking all approaches and evaluations.
|
joyjay/Bongard-OpenWorld
|
[
"task_categories:feature-extraction",
"task_categories:question-answering",
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-10-19T01:11:40+00:00
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["feature-extraction", "question-answering", "summarization"], "pretty_name": "Bongard-OpenWorld"}
|
2023-10-20T05:06:25+00:00
|
[] |
[
"en"
] |
TAGS
#task_categories-feature-extraction #task_categories-question-answering #task_categories-summarization #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #region-us
|
Bongard-OpenWorld
===
Bongard-OpenWorld is a new benchmark for evaluating real-world few-shot reasoning for machine vision. Please refer to our project page.
Some links may be invalid due to the instability of the URLs. To ensure that the community can reproduce our results from scratch, we have provided a backup of all the images. You can download from Google Drive.
Please note that this page only hosts the data for Bongard-OpenWorld. All images of Bongard-OpenWorld are crawled from Google Images. We do not claim ownership of any image in Bongard-OpenWorld. Therefore, we strongly recommend that you delete all images immediately after benchmarking all approaches and evaluations.
|
[] |
[
"TAGS\n#task_categories-feature-extraction #task_categories-question-answering #task_categories-summarization #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #region-us \n"
] |
[
69
] |
[
"passage: TAGS\n#task_categories-feature-extraction #task_categories-question-answering #task_categories-summarization #size_categories-1K<n<10K #language-English #license-cc-by-nc-sa-4.0 #region-us \n"
] |
26f8d497e306e6d4159dcc3b507d78011bcb9df0
|
# Dataset Card for "prm800k-parsed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
SUSTech/prm800k-parsed
|
[
"region:us"
] |
2023-10-19T01:22:15+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "history", "sequence": "string"}, {"name": "problem", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "solution", "dtype": "string"}, {"name": "completions", "sequence": "string"}, {"name": "label", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 166770330, "num_examples": 82391}, {"name": "test", "num_bytes": 3974574, "num_examples": 2079}], "download_size": 0, "dataset_size": 170744904}}
|
2023-10-21T03:45:29+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "prm800k-parsed"
More Information needed
|
[
"# Dataset Card for \"prm800k-parsed\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"prm800k-parsed\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"prm800k-parsed\"\n\nMore Information needed"
] |
21f186b4f2b7c8770437b2f78169960c0c4583f0
|
# Dataset Card for "enamine_diversity_standardized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
phanvancongthanh/enamine_diversity_standardized
|
[
"region:us"
] |
2023-10-19T01:55:08+00:00
|
{"dataset_info": {"features": [{"name": "standardized_smiles", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2422193889.0, "num_examples": 47999999}], "download_size": 955961392, "dataset_size": 2422193889.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T05:22:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "enamine_diversity_standardized"
More Information needed
|
[
"# Dataset Card for \"enamine_diversity_standardized\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"enamine_diversity_standardized\"\n\nMore Information needed"
] |
[
6,
18
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"enamine_diversity_standardized\"\n\nMore Information needed"
] |
25b87fbedfb86840abaf8cd09af7a029208a971a
|
# Dataset Card for HackAPrompt 💻🔍
This dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference. 📊👾
Submissions were sourced from two environments: a playground for experimentation and an official submissions platform.
The playground itself can be accessed [here](https://huggingface.co/spaces/hackaprompt/playground) 🎮
More details about the competition itself [here](http://paper.hackaprompt.com) 🏆
## Dataset Details 📋
### Dataset Description 📄
We conducted a prompt hacking competition where users were competing to "hack" different large language models (LLMs). Different levels were proposed, with varying degrees of difficulty, and for each level, 3 LLMs were evaluated: GPT-3 (`text-davinci-003`), FlanT5-XXL (`philschmid/flan-t5-xxl-sharded-fp16`), and ChatGPT (`gpt-3.5-turbo`).
We anonymously collected user submissions throughout the competition and also had users submit their best attempts via an online platform for a chance to win the competition. Users submitted their prompts, and our servers automatically evaluated their attempts. To delineate between ties, token counts were used where lower counts gave better scores.
This dataset releases all submissions sent to both our playground and submission servers. 📤📥
### Columns Description 🧾
- **level**: A numerical value indicating the difficulty or complexity of the prompt.
- **user_input**: The input provided by the user or participant in response to the given challenge.
- **prompt**: The full prompt that was used to query the model, this includes the user's input.
- **completion**: The output or completion generated by the model based on the user's input.
- **model**: The type or version of the model that generated the completion. For example, "gpt-3.5-turbo" or "FlanT5-XXL".
- **expected_completion**: The expected or ideal output that should have been generated by the model for the given user input.
- **token_count**: The number of tokens present in the user's input. This serves as a measure of the input's length.
- **correct**: A boolean value indicating whether the model's completion was correct or not, based on the expected output.
- **error**: A boolean value indicating if there was an error during the model's processing of the user input. Note: we did not include submissions that triggered errors in this dataset.
- **score**: A numerical value representing the score assigned to the model's completion based on its accuracy, correctness, and other evaluation metrics. (Only available for prompts on the submissions platform)
- **dataset**: A categorical variable indicating the source of the submission. The two categories are "playground_data" (for submissions from the playground environment) and "submission_data" (for official submissions).
- **timestamp**: The date and time when the submission was made. (Only available for playground dataset)
<!-- - **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] -->
## Uses 🧑🔬
This dataset is meant to be used in a research context to better understand the different types of attacks "in the wild" on LLMs. 📚🔬
<!-- Address questions around how the dataset is intended to be used. -->
#### Personal and Sensitive Information 🔒
We did not release directly any personal or sensitive information explicitly. On the playground, users could submit anonymously, and we did not collect information about the users directly.
For the submissions data, teams did submit in their names, but that information has not been made available in this version of the dataset to preserve participants' privacy.
<!-- 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. -->
## Bias, Risks, and Limitations ⚠️
The data was submitted via a public portal hosted on huggingface.
We did not curate the data before publishing it.
The data may contain offensive material.
Please use at your own risk.
### Recommendations 🚀
Users should be made aware of the risks, biases, and limitations of the dataset and use at their own risk.
Please use at your own risk.
## Citation 📝
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
https://arxiv.org/abs/2311.16119
```
@inproceedings{Schulhoff:Pinto:Khan:Bouchard:Si:Boyd-Graber:Anati:Tagliabue:Kost:Carnahan-2023,
Title = {Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs Through a Global Prompt Hacking Competition},
Author = {Sander V Schulhoff and Jeremy Pinto and Anaum Khan and Louis-François Bouchard and Chenglei Si and Jordan Lee Boyd-Graber and Svetlina Anati and Valen Tagliabue and Anson Liu Kost and Christopher R Carnahan},
Booktitle = {Empirical Methods in Natural Language Processing},
Year = {2023},
Location = {Singapore}
}
```
|
hackaprompt/hackaprompt-dataset
|
[
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"code",
"arxiv:2311.16119",
"region:us"
] |
2023-10-19T02:01:52+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "pretty_name": "HackAPrompt Dataset", "tags": ["code"]}
|
2024-01-24T16:32:38+00:00
|
[
"2311.16119"
] |
[
"en"
] |
TAGS
#size_categories-100K<n<1M #language-English #license-mit #code #arxiv-2311.16119 #region-us
|
# Dataset Card for HackAPrompt
This dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference.
Submissions were sourced from two environments: a playground for experimentation and an official submissions platform.
The playground itself can be accessed here
More details about the competition itself here
## Dataset Details
### Dataset Description
We conducted a prompt hacking competition where users were competing to "hack" different large language models (LLMs). Different levels were proposed, with varying degrees of difficulty, and for each level, 3 LLMs were evaluated: GPT-3 ('text-davinci-003'), FlanT5-XXL ('philschmid/flan-t5-xxl-sharded-fp16'), and ChatGPT ('gpt-3.5-turbo').
We anonymously collected user submissions throughout the competition and also had users submit their best attempts via an online platform for a chance to win the competition. Users submitted their prompts, and our servers automatically evaluated their attempts. To delineate between ties, token counts were used where lower counts gave better scores.
This dataset releases all submissions sent to both our playground and submission servers.
### Columns Description
- level: A numerical value indicating the difficulty or complexity of the prompt.
- user_input: The input provided by the user or participant in response to the given challenge.
- prompt: The full prompt that was used to query the model, this includes the user's input.
- completion: The output or completion generated by the model based on the user's input.
- model: The type or version of the model that generated the completion. For example, "gpt-3.5-turbo" or "FlanT5-XXL".
- expected_completion: The expected or ideal output that should have been generated by the model for the given user input.
- token_count: The number of tokens present in the user's input. This serves as a measure of the input's length.
- correct: A boolean value indicating whether the model's completion was correct or not, based on the expected output.
- error: A boolean value indicating if there was an error during the model's processing of the user input. Note: we did not include submissions that triggered errors in this dataset.
- score: A numerical value representing the score assigned to the model's completion based on its accuracy, correctness, and other evaluation metrics. (Only available for prompts on the submissions platform)
- dataset: A categorical variable indicating the source of the submission. The two categories are "playground_data" (for submissions from the playground environment) and "submission_data" (for official submissions).
- timestamp: The date and time when the submission was made. (Only available for playground dataset)
## Uses
This dataset is meant to be used in a research context to better understand the different types of attacks "in the wild" on LLMs.
#### Personal and Sensitive Information
We did not release directly any personal or sensitive information explicitly. On the playground, users could submit anonymously, and we did not collect information about the users directly.
For the submissions data, teams did submit in their names, but that information has not been made available in this version of the dataset to preserve participants' privacy.
## Bias, Risks, and Limitations ️
The data was submitted via a public portal hosted on huggingface.
We did not curate the data before publishing it.
The data may contain offensive material.
Please use at your own risk.
### Recommendations
Users should be made aware of the risks, biases, and limitations of the dataset and use at their own risk.
Please use at your own risk.
BibTeX:
URL
|
[
"# Dataset Card for HackAPrompt \n\nThis dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference. \n\nSubmissions were sourced from two environments: a playground for experimentation and an official submissions platform.\n\nThe playground itself can be accessed here \n\nMore details about the competition itself here",
"## Dataset Details",
"### Dataset Description \n\nWe conducted a prompt hacking competition where users were competing to \"hack\" different large language models (LLMs). Different levels were proposed, with varying degrees of difficulty, and for each level, 3 LLMs were evaluated: GPT-3 ('text-davinci-003'), FlanT5-XXL ('philschmid/flan-t5-xxl-sharded-fp16'), and ChatGPT ('gpt-3.5-turbo').\n\nWe anonymously collected user submissions throughout the competition and also had users submit their best attempts via an online platform for a chance to win the competition. Users submitted their prompts, and our servers automatically evaluated their attempts. To delineate between ties, token counts were used where lower counts gave better scores.\n\nThis dataset releases all submissions sent to both our playground and submission servers.",
"### Columns Description \n\n- level: A numerical value indicating the difficulty or complexity of the prompt.\n\n- user_input: The input provided by the user or participant in response to the given challenge.\n\n- prompt: The full prompt that was used to query the model, this includes the user's input.\n\n- completion: The output or completion generated by the model based on the user's input.\n\n- model: The type or version of the model that generated the completion. For example, \"gpt-3.5-turbo\" or \"FlanT5-XXL\".\n\n- expected_completion: The expected or ideal output that should have been generated by the model for the given user input.\n\n- token_count: The number of tokens present in the user's input. This serves as a measure of the input's length.\n\n- correct: A boolean value indicating whether the model's completion was correct or not, based on the expected output.\n\n- error: A boolean value indicating if there was an error during the model's processing of the user input. Note: we did not include submissions that triggered errors in this dataset.\n\n- score: A numerical value representing the score assigned to the model's completion based on its accuracy, correctness, and other evaluation metrics. (Only available for prompts on the submissions platform)\n\n- dataset: A categorical variable indicating the source of the submission. The two categories are \"playground_data\" (for submissions from the playground environment) and \"submission_data\" (for official submissions).\n\n- timestamp: The date and time when the submission was made. (Only available for playground dataset)",
"## Uses \n\nThis dataset is meant to be used in a research context to better understand the different types of attacks \"in the wild\" on LLMs.",
"#### Personal and Sensitive Information \n\nWe did not release directly any personal or sensitive information explicitly. On the playground, users could submit anonymously, and we did not collect information about the users directly.\n\nFor the submissions data, teams did submit in their names, but that information has not been made available in this version of the dataset to preserve participants' privacy.",
"## Bias, Risks, and Limitations ️\n\nThe data was submitted via a public portal hosted on huggingface. \nWe did not curate the data before publishing it. \nThe data may contain offensive material.\n\nPlease use at your own risk.",
"### Recommendations \n\nUsers should be made aware of the risks, biases, and limitations of the dataset and use at their own risk.\n\nPlease use at your own risk.\n\n\n\n\n\nBibTeX:\n\nURL"
] |
[
"TAGS\n#size_categories-100K<n<1M #language-English #license-mit #code #arxiv-2311.16119 #region-us \n",
"# Dataset Card for HackAPrompt \n\nThis dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference. \n\nSubmissions were sourced from two environments: a playground for experimentation and an official submissions platform.\n\nThe playground itself can be accessed here \n\nMore details about the competition itself here",
"## Dataset Details",
"### Dataset Description \n\nWe conducted a prompt hacking competition where users were competing to \"hack\" different large language models (LLMs). Different levels were proposed, with varying degrees of difficulty, and for each level, 3 LLMs were evaluated: GPT-3 ('text-davinci-003'), FlanT5-XXL ('philschmid/flan-t5-xxl-sharded-fp16'), and ChatGPT ('gpt-3.5-turbo').\n\nWe anonymously collected user submissions throughout the competition and also had users submit their best attempts via an online platform for a chance to win the competition. Users submitted their prompts, and our servers automatically evaluated their attempts. To delineate between ties, token counts were used where lower counts gave better scores.\n\nThis dataset releases all submissions sent to both our playground and submission servers.",
"### Columns Description \n\n- level: A numerical value indicating the difficulty or complexity of the prompt.\n\n- user_input: The input provided by the user or participant in response to the given challenge.\n\n- prompt: The full prompt that was used to query the model, this includes the user's input.\n\n- completion: The output or completion generated by the model based on the user's input.\n\n- model: The type or version of the model that generated the completion. For example, \"gpt-3.5-turbo\" or \"FlanT5-XXL\".\n\n- expected_completion: The expected or ideal output that should have been generated by the model for the given user input.\n\n- token_count: The number of tokens present in the user's input. This serves as a measure of the input's length.\n\n- correct: A boolean value indicating whether the model's completion was correct or not, based on the expected output.\n\n- error: A boolean value indicating if there was an error during the model's processing of the user input. Note: we did not include submissions that triggered errors in this dataset.\n\n- score: A numerical value representing the score assigned to the model's completion based on its accuracy, correctness, and other evaluation metrics. (Only available for prompts on the submissions platform)\n\n- dataset: A categorical variable indicating the source of the submission. The two categories are \"playground_data\" (for submissions from the playground environment) and \"submission_data\" (for official submissions).\n\n- timestamp: The date and time when the submission was made. (Only available for playground dataset)",
"## Uses \n\nThis dataset is meant to be used in a research context to better understand the different types of attacks \"in the wild\" on LLMs.",
"#### Personal and Sensitive Information \n\nWe did not release directly any personal or sensitive information explicitly. On the playground, users could submit anonymously, and we did not collect information about the users directly.\n\nFor the submissions data, teams did submit in their names, but that information has not been made available in this version of the dataset to preserve participants' privacy.",
"## Bias, Risks, and Limitations ️\n\nThe data was submitted via a public portal hosted on huggingface. \nWe did not curate the data before publishing it. \nThe data may contain offensive material.\n\nPlease use at your own risk.",
"### Recommendations \n\nUsers should be made aware of the risks, biases, and limitations of the dataset and use at their own risk.\n\nPlease use at your own risk.\n\n\n\n\n\nBibTeX:\n\nURL"
] |
[
38,
83,
4,
207,
386,
34,
78,
54,
48
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[
"passage: TAGS\n#size_categories-100K<n<1M #language-English #license-mit #code #arxiv-2311.16119 #region-us \n# Dataset Card for HackAPrompt \n\nThis dataset contains submissions from a prompt hacking competition. An in-depth analysis of the dataset has been accepted at the EMNLP 2023 conference. \n\nSubmissions were sourced from two environments: a playground for experimentation and an official submissions platform.\n\nThe playground itself can be accessed here \n\nMore details about the competition itself here## Dataset Details### Dataset Description \n\nWe conducted a prompt hacking competition where users were competing to \"hack\" different large language models (LLMs). Different levels were proposed, with varying degrees of difficulty, and for each level, 3 LLMs were evaluated: GPT-3 ('text-davinci-003'), FlanT5-XXL ('philschmid/flan-t5-xxl-sharded-fp16'), and ChatGPT ('gpt-3.5-turbo').\n\nWe anonymously collected user submissions throughout the competition and also had users submit their best attempts via an online platform for a chance to win the competition. Users submitted their prompts, and our servers automatically evaluated their attempts. To delineate between ties, token counts were used where lower counts gave better scores.\n\nThis dataset releases all submissions sent to both our playground and submission servers."
] |
b5bcfd82d16a543b9bdea4383c4034ff5d618cf5
|
# Dataset Card for "lima"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quyanh/lima
|
[
"region:us"
] |
2023-10-19T02:13:48+00:00
|
{"dataset_info": {"features": [{"name": "system_prompt", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3033066, "num_examples": 1030}], "download_size": 1711487, "dataset_size": 3033066}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T02:47:15+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "lima"
More Information needed
|
[
"# Dataset Card for \"lima\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"lima\"\n\nMore Information needed"
] |
[
6,
11
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"lima\"\n\nMore Information needed"
] |
59b4f4a1c769fe7d03f310c71d5c4907294914d4
|
# Dataset Card for Evaluation run of openchat/opencoderplus
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openchat/opencoderplus
- **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 [openchat/opencoderplus](https://huggingface.co/openchat/opencoderplus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openchat__opencoderplus",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T03:19:58.630219](https://huggingface.co/datasets/open-llm-leaderboard/details_openchat__opencoderplus/blob/main/results_2023-10-19T03-19-58.630219.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": 0.033976510067114093,
"em_stderr": 0.0018553373122680704,
"f1": 0.09136325503355722,
"f1_stderr": 0.0022463129422016395,
"acc": 0.3538260251930829,
"acc_stderr": 0.009542580764267136
},
"harness|drop|3": {
"em": 0.033976510067114093,
"em_stderr": 0.0018553373122680704,
"f1": 0.09136325503355722,
"f1_stderr": 0.0022463129422016395
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.0057849916626918465
},
"harness|winogrande|5": {
"acc": 0.6614048934490924,
"acc_stderr": 0.013300169865842424
}
}
```
### 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_openchat__opencoderplus
|
[
"region:us"
] |
2023-10-19T02:20:02+00:00
|
{"pretty_name": "Evaluation run of openchat/opencoderplus", "dataset_summary": "Dataset automatically created during the evaluation run of model [openchat/opencoderplus](https://huggingface.co/openchat/opencoderplus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openchat__opencoderplus\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T03:19:58.630219](https://huggingface.co/datasets/open-llm-leaderboard/details_openchat__opencoderplus/blob/main/results_2023-10-19T03-19-58.630219.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\": 0.033976510067114093,\n \"em_stderr\": 0.0018553373122680704,\n \"f1\": 0.09136325503355722,\n \"f1_stderr\": 0.0022463129422016395,\n \"acc\": 0.3538260251930829,\n \"acc_stderr\": 0.009542580764267136\n },\n \"harness|drop|3\": {\n \"em\": 0.033976510067114093,\n \"em_stderr\": 0.0018553373122680704,\n \"f1\": 0.09136325503355722,\n \"f1_stderr\": 0.0022463129422016395\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04624715693707354,\n \"acc_stderr\": 0.0057849916626918465\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.6614048934490924,\n \"acc_stderr\": 0.013300169865842424\n }\n}\n```", "repo_url": "https://huggingface.co/openchat/opencoderplus", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_19T03_19_58.630219", "path": ["**/details_harness|drop|3_2023-10-19T03-19-58.630219.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T03-19-58.630219.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_19T03_19_58.630219", "path": ["**/details_harness|gsm8k|5_2023-10-19T03-19-58.630219.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-19T03-19-58.630219.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_19T03_19_58.630219", "path": ["**/details_harness|winogrande|5_2023-10-19T03-19-58.630219.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-19T03-19-58.630219.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_19T03_19_58.630219", "path": ["results_2023-10-19T03-19-58.630219.parquet"]}, {"split": "latest", "path": ["results_2023-10-19T03-19-58.630219.parquet"]}]}]}
|
2023-10-19T02:20:11+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of openchat/opencoderplus
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model openchat/opencoderplus on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-19T03:19:58.630219(note that their might be results for other tasks in 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 openchat/opencoderplus",
"## 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 openchat/opencoderplus on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-19T03:19:58.630219(note that their might be results for other tasks in 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 openchat/opencoderplus",
"## 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 openchat/opencoderplus on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-19T03:19:58.630219(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the \"latest\" split for each eval):",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
] |
[
6,
16,
31,
164,
67,
10,
4,
6,
6,
5,
5,
5,
7,
4,
10,
10,
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9,
8,
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5,
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5
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of openchat/opencoderplus## 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 openchat/opencoderplus on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-19T03:19:58.630219(note that their might be results for other tasks in 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"
] |
9688634d55f322972976545d2a0dc5c52eaa8965
|
# Dataset Card for "passage_report"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
fiveflow/passage_report
|
[
"region:us"
] |
2023-10-19T02:25:30+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9522212, "num_examples": 1190}], "download_size": 4789024, "dataset_size": 9522212}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-26T12:24:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "passage_report"
More Information needed
|
[
"# Dataset Card for \"passage_report\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"passage_report\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"passage_report\"\n\nMore Information needed"
] |
6b2800378bfa8e5f53a0f283849e18011f0ab6b0
|
# Dataset Card for "dolly"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
quyanh/dolly
|
[
"region:us"
] |
2023-10-19T02:34:09+00:00
|
{"dataset_info": {"features": [{"name": "system_prompt", "dtype": "string"}, {"name": "inputs", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14079200, "num_examples": 15011}], "download_size": 7841758, "dataset_size": 14079200}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-24T14:59:27+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "dolly"
More Information needed
|
[
"# Dataset Card for \"dolly\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"dolly\"\n\nMore Information needed"
] |
[
6,
12
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"dolly\"\n\nMore Information needed"
] |
306e44ba1dd5e4686d52c192ffd236fc5312975b
|
# Dataset Card for Evaluation run of KoboldAI/LLaMA2-13B-Holomax
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax
- **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 [KoboldAI/LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Holomax",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T03:44:46.836868](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Holomax/blob/main/results_2023-10-19T03-44-46.836868.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": 0.0019924496644295304,
"em_stderr": 0.0004566676462666988,
"f1": 0.06074769295302006,
"f1_stderr": 0.0013672043421452582,
"acc": 0.4305631433729482,
"acc_stderr": 0.010496955983172063
},
"harness|drop|3": {
"em": 0.0019924496644295304,
"em_stderr": 0.0004566676462666988,
"f1": 0.06074769295302006,
"f1_stderr": 0.0013672043421452582
},
"harness|gsm8k|5": {
"acc": 0.11448066717210008,
"acc_stderr": 0.008770157532110506
},
"harness|winogrande|5": {
"acc": 0.7466456195737964,
"acc_stderr": 0.012223754434233623
}
}
```
### 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_KoboldAI__LLaMA2-13B-Holomax
|
[
"region:us"
] |
2023-10-19T02:44:51+00:00
|
{"pretty_name": "Evaluation run of KoboldAI/LLaMA2-13B-Holomax", "dataset_summary": "Dataset automatically created during the evaluation run of model [KoboldAI/LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Holomax\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T03:44:46.836868](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__LLaMA2-13B-Holomax/blob/main/results_2023-10-19T03-44-46.836868.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\": 0.0019924496644295304,\n \"em_stderr\": 0.0004566676462666988,\n \"f1\": 0.06074769295302006,\n \"f1_stderr\": 0.0013672043421452582,\n \"acc\": 0.4305631433729482,\n \"acc_stderr\": 0.010496955983172063\n },\n \"harness|drop|3\": {\n \"em\": 0.0019924496644295304,\n \"em_stderr\": 0.0004566676462666988,\n \"f1\": 0.06074769295302006,\n \"f1_stderr\": 0.0013672043421452582\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.11448066717210008,\n \"acc_stderr\": 0.008770157532110506\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.7466456195737964,\n \"acc_stderr\": 0.012223754434233623\n }\n}\n```", "repo_url": "https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_19T03_44_46.836868", "path": ["**/details_harness|drop|3_2023-10-19T03-44-46.836868.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T03-44-46.836868.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_19T03_44_46.836868", "path": ["**/details_harness|gsm8k|5_2023-10-19T03-44-46.836868.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-19T03-44-46.836868.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_19T03_44_46.836868", "path": ["**/details_harness|winogrande|5_2023-10-19T03-44-46.836868.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-19T03-44-46.836868.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_19T03_44_46.836868", "path": ["results_2023-10-19T03-44-46.836868.parquet"]}, {"split": "latest", "path": ["results_2023-10-19T03-44-46.836868.parquet"]}]}]}
|
2023-10-19T02:44:59+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of KoboldAI/LLaMA2-13B-Holomax
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model KoboldAI/LLaMA2-13B-Holomax on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-19T03:44:46.836868(note that their might be results for other tasks in 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 KoboldAI/LLaMA2-13B-Holomax",
"## 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 KoboldAI/LLaMA2-13B-Holomax on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-19T03:44:46.836868(note that their might be results for other tasks in 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 KoboldAI/LLaMA2-13B-Holomax",
"## 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 KoboldAI/LLaMA2-13B-Holomax on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-19T03:44:46.836868(note that their might be results for other tasks in 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 KoboldAI/LLaMA2-13B-Holomax## 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 KoboldAI/LLaMA2-13B-Holomax on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-19T03:44:46.836868(note that their might be results for other tasks in 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"
] |
8494c2464f11e449d267273665b2f4878264fbf8
|
# Dataset Card for "squad_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
zxvix/squad_text
|
[
"region:us"
] |
2023-10-19T02:52:06+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "original", "path": "data/original-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "original", "num_bytes": 1611043, "num_examples": 2067}], "download_size": 1039425, "dataset_size": 1611043}}
|
2023-10-19T02:59:00+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "squad_text"
More Information needed
|
[
"# Dataset Card for \"squad_text\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_text\"\n\nMore Information needed"
] |
[
6,
14
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"squad_text\"\n\nMore Information needed"
] |
cded1be2417f45f56fc0de15f70df7bb89822f53
|
# Dataset Card for Evaluation run of KoboldAI/fairseq-dense-1.3B
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KoboldAI/fairseq-dense-1.3B
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** [email protected]
### Dataset Summary
Dataset automatically created during the evaluation run of model [KoboldAI/fairseq-dense-1.3B](https://huggingface.co/KoboldAI/fairseq-dense-1.3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_KoboldAI__fairseq-dense-1.3B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T04:22:19.785222](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__fairseq-dense-1.3B/blob/main/results_2023-10-19T04-22-19.785222.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": 0.024119127516778523,
"em_stderr": 0.0015711543458424907,
"f1": 0.10603817114093886,
"f1_stderr": 0.002447898366394225,
"acc": 0.2951854775059195,
"acc_stderr": 0.006910524554827735
},
"harness|drop|3": {
"em": 0.024119127516778523,
"em_stderr": 0.0015711543458424907,
"f1": 0.10603817114093886,
"f1_stderr": 0.002447898366394225
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.590370955011839,
"acc_stderr": 0.01382104910965547
}
}
```
### 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_KoboldAI__fairseq-dense-1.3B
|
[
"region:us"
] |
2023-10-19T03:22:23+00:00
|
{"pretty_name": "Evaluation run of KoboldAI/fairseq-dense-1.3B", "dataset_summary": "Dataset automatically created during the evaluation run of model [KoboldAI/fairseq-dense-1.3B](https://huggingface.co/KoboldAI/fairseq-dense-1.3B) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\nTo load the details from a run, you can for instance do the following:\n```python\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_KoboldAI__fairseq-dense-1.3B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-19T04:22:19.785222](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__fairseq-dense-1.3B/blob/main/results_2023-10-19T04-22-19.785222.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\": 0.024119127516778523,\n \"em_stderr\": 0.0015711543458424907,\n \"f1\": 0.10603817114093886,\n \"f1_stderr\": 0.002447898366394225,\n \"acc\": 0.2951854775059195,\n \"acc_stderr\": 0.006910524554827735\n },\n \"harness|drop|3\": {\n \"em\": 0.024119127516778523,\n \"em_stderr\": 0.0015711543458424907,\n \"f1\": 0.10603817114093886,\n \"f1_stderr\": 0.002447898366394225\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.590370955011839,\n \"acc_stderr\": 0.01382104910965547\n }\n}\n```", "repo_url": "https://huggingface.co/KoboldAI/fairseq-dense-1.3B", "leaderboard_url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard", "point_of_contact": "[email protected]", "configs": [{"config_name": "harness_drop_3", "data_files": [{"split": "2023_10_19T04_22_19.785222", "path": ["**/details_harness|drop|3_2023-10-19T04-22-19.785222.parquet"]}, {"split": "latest", "path": ["**/details_harness|drop|3_2023-10-19T04-22-19.785222.parquet"]}]}, {"config_name": "harness_gsm8k_5", "data_files": [{"split": "2023_10_19T04_22_19.785222", "path": ["**/details_harness|gsm8k|5_2023-10-19T04-22-19.785222.parquet"]}, {"split": "latest", "path": ["**/details_harness|gsm8k|5_2023-10-19T04-22-19.785222.parquet"]}]}, {"config_name": "harness_winogrande_5", "data_files": [{"split": "2023_10_19T04_22_19.785222", "path": ["**/details_harness|winogrande|5_2023-10-19T04-22-19.785222.parquet"]}, {"split": "latest", "path": ["**/details_harness|winogrande|5_2023-10-19T04-22-19.785222.parquet"]}]}, {"config_name": "results", "data_files": [{"split": "2023_10_19T04_22_19.785222", "path": ["results_2023-10-19T04-22-19.785222.parquet"]}, {"split": "latest", "path": ["results_2023-10-19T04-22-19.785222.parquet"]}]}]}
|
2023-10-19T03:22:32+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for Evaluation run of KoboldAI/fairseq-dense-1.3B
## Dataset Description
- Homepage:
- Repository: URL
- Paper:
- Leaderboard: URL
- Point of Contact: clementine@URL
### Dataset Summary
Dataset automatically created during the evaluation run of model KoboldAI/fairseq-dense-1.3B on the Open LLM Leaderboard.
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).
To load the details from a run, you can for instance do the following:
## Latest results
These are the latest results from run 2023-10-19T04:22:19.785222(note that their might be results for other tasks in 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 KoboldAI/fairseq-dense-1.3B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KoboldAI/fairseq-dense-1.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-19T04:22:19.785222(note that their might be results for other tasks in 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 KoboldAI/fairseq-dense-1.3B",
"## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL",
"### Dataset Summary\n\nDataset automatically created during the evaluation run of model KoboldAI/fairseq-dense-1.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:",
"## Latest results\n\nThese are the latest results from run 2023-10-19T04:22:19.785222(note that their might be results for other tasks in 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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169,
66,
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6,
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[
"passage: TAGS\n#region-us \n# Dataset Card for Evaluation run of KoboldAI/fairseq-dense-1.3B## Dataset Description\n\n- Homepage: \n- Repository: URL\n- Paper: \n- Leaderboard: URL\n- Point of Contact: clementine@URL### Dataset Summary\n\nDataset automatically created during the evaluation run of model KoboldAI/fairseq-dense-1.3B on the Open LLM Leaderboard.\n\nThe dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The \"train\" split is always pointing to the latest results.\n\nAn additional configuration \"results\" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the Open LLM Leaderboard).\n\nTo load the details from a run, you can for instance do the following:## Latest results\n\nThese are the latest results from run 2023-10-19T04:22:19.785222(note that their might be results for other tasks in 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"
] |
ad688d31fd3a69aa113468e0b44faaf8613f8426
|
# Dataset Card for "cg-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
MonaNashaatAli/cg-llama2-1k
|
[
"region:us"
] |
2023-10-19T03:33:03+00:00
|
{"dataset_info": {"features": [{"name": "patch", "dtype": "string"}, {"name": "y", "dtype": "int64"}, {"name": "oldf", "dtype": "string"}, {"name": "idx", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "msg", "dtype": "string"}, {"name": "proj", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 269262062, "num_examples": 10169}], "download_size": 92189788, "dataset_size": 269262062}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T03:33:10+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "cg-llama2-1k"
More Information needed
|
[
"# Dataset Card for \"cg-llama2-1k\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"cg-llama2-1k\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"cg-llama2-1k\"\n\nMore Information needed"
] |
e6326cfb59e36aa67d930cc8407d6b96fa298f4c
|
# Dataset Card for "GPT-4V-DescribeChangesCutscene"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
asgaardlab/GPT-4V-DescribeChangesCutscene
|
[
"region:us"
] |
2023-10-19T03:58:20+00:00
|
{"dataset_info": {"features": [{"name": "image_2", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "gpt-4v-response", "sequence": "string"}, {"name": "image_2_width", "dtype": "int64"}, {"name": "image_2_height", "dtype": "int64"}, {"name": "image_1_width", "dtype": "int64"}, {"name": "image_1_height", "dtype": "int64"}, {"name": "header_text", "dtype": "string"}, {"name": "projection_id", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 52762891.0, "num_examples": 218}], "download_size": 39843787, "dataset_size": 52762891.0}}
|
2023-10-20T04:28:18+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "GPT-4V-DescribeChangesCutscene"
More Information needed
|
[
"# Dataset Card for \"GPT-4V-DescribeChangesCutscene\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"GPT-4V-DescribeChangesCutscene\"\n\nMore Information needed"
] |
[
6,
23
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"GPT-4V-DescribeChangesCutscene\"\n\nMore Information needed"
] |
ef6bdf0d5c589bf345eea68860dc94d3a891c6a2
|
# Dataset Card for "insulin_pen_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gn03249822/insulin_pen_dataset
|
[
"region:us"
] |
2023-10-19T04:01:46+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": "\u8afe\u80f0\u4fdd", "1": "\u8afe\u80f0\u5f97"}}}}], "splits": [{"name": "train", "num_bytes": 287496503.26086956, "num_examples": 117}, {"name": "test", "num_bytes": 54706739.739130445, "num_examples": 21}], "download_size": 342220863, "dataset_size": 342203243.0}}
|
2023-10-19T04:07:56+00:00
|
[] |
[] |
TAGS
#region-us
|
# Dataset Card for "insulin_pen_dataset"
More Information needed
|
[
"# Dataset Card for \"insulin_pen_dataset\"\n\nMore Information needed"
] |
[
"TAGS\n#region-us \n",
"# Dataset Card for \"insulin_pen_dataset\"\n\nMore Information needed"
] |
[
6,
17
] |
[
"passage: TAGS\n#region-us \n# Dataset Card for \"insulin_pen_dataset\"\n\nMore Information needed"
] |
0ce93bcd2b6b151fcc96649bd5aad871699670cc
|
# Dataset Card for "SampleDataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
jstack32/SampleDataset
|
[
"language:en",
"region:us"
] |
2023-10-19T04:12:40+00:00
|
{"language": ["en"], "dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": "int64"}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 102, "num_examples": 2}], "download_size": 1893, "dataset_size": 102}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
|
2023-10-19T04:14:25+00:00
|
[] |
[
"en"
] |
TAGS
#language-English #region-us
|
# Dataset Card for "SampleDataset"
More Information needed
|
[
"# Dataset Card for \"SampleDataset\"\n\nMore Information needed"
] |
[
"TAGS\n#language-English #region-us \n",
"# Dataset Card for \"SampleDataset\"\n\nMore Information needed"
] |
[
10,
14
] |
[
"passage: TAGS\n#language-English #region-us \n# Dataset Card for \"SampleDataset\"\n\nMore Information needed"
] |
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