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hails/mmlu_no_train | hails | "2024-01-22T20:46:30" | 36,428,292 | 18 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"region:us"
] | [
"question-answering"
] | "2023-10-31T17:25:54" | ---
language:
- en
license: mit
task_categories:
- question-answering
pretty_name: MMLU loader with no auxiliary train set
dataset_info:
config_name: all
features:
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configs:
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data_files:
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path: all/test-*
- split: validation
path: all/validation-*
- split: dev
path: all/dev-*
---
This dataset contains a copy of the `cais/mmlu` HF dataset but without the `auxiliary_train` split that takes a long time to generate again each time when loading multiple subsets of the dataset.
Please visit https://huggingface.co/datasets/cais/mmlu for more information on the MMLU dataset. |
lighteval/mmlu | lighteval | "2023-06-09T16:36:19" | 12,063,930 | 30 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
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"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | [
"question-answering"
] | "2023-05-16T09:39:28" | ---
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license:
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multilinguality:
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size_categories:
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source_datasets:
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task_categories:
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task_ids:
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paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
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---
# Dataset Card for MMLU
## 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
- **Repository**: https://github.com/hendrycks/test
- **Paper**: https://arxiv.org/abs/2009.03300
### Dataset Summary
[Measuring Massive Multitask Language Understanding](https://arxiv.org/pdf/2009.03300) by [Dan Hendrycks](https://people.eecs.berkeley.edu/~hendrycks/), [Collin Burns](http://collinpburns.com), [Steven Basart](https://stevenbas.art), Andy Zou, Mantas Mazeika, [Dawn Song](https://people.eecs.berkeley.edu/~dawnsong/), and [Jacob Steinhardt](https://www.stat.berkeley.edu/~jsteinhardt/) (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability.
A complete list of tasks: ['abstract_algebra', 'anatomy', 'astronomy', 'business_ethics', 'clinical_knowledge', 'college_biology', 'college_chemistry', 'college_computer_science', 'college_mathematics', 'college_medicine', 'college_physics', 'computer_security', 'conceptual_physics', 'econometrics', 'electrical_engineering', 'elementary_mathematics', 'formal_logic', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_computer_science', 'high_school_european_history', 'high_school_geography', 'high_school_government_and_politics', 'high_school_macroeconomics', 'high_school_mathematics', 'high_school_microeconomics', 'high_school_physics', 'high_school_psychology', 'high_school_statistics', 'high_school_us_history', 'high_school_world_history', 'human_aging', 'human_sexuality', 'international_law', 'jurisprudence', 'logical_fallacies', 'machine_learning', 'management', 'marketing', 'medical_genetics', 'miscellaneous', 'moral_disputes', 'moral_scenarios', 'nutrition', 'philosophy', 'prehistory', 'professional_accounting', 'professional_law', 'professional_medicine', 'professional_psychology', 'public_relations', 'security_studies', 'sociology', 'us_foreign_policy', 'virology', 'world_religions']
### Supported Tasks and Leaderboards
| Model | Authors | Humanities | Social Science | STEM | Other | Average |
|------------------------------------|----------|:-------:|:-------:|:-------:|:-------:|:-------:|
| [UnifiedQA](https://arxiv.org/abs/2005.00700) | Khashabi et al., 2020 | 45.6 | 56.6 | 40.2 | 54.6 | 48.9
| [GPT-3](https://arxiv.org/abs/2005.14165) (few-shot) | Brown et al., 2020 | 40.8 | 50.4 | 36.7 | 48.8 | 43.9
| [GPT-2](https://arxiv.org/abs/2005.14165) | Radford et al., 2019 | 32.8 | 33.3 | 30.2 | 33.1 | 32.4
| Random Baseline | N/A | 25.0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
## Dataset Structure
### Data Instances
An example from anatomy subtask looks as follows:
```
{
"question": "What is the embryological origin of the hyoid bone?",
"choices": ["The first pharyngeal arch", "The first and second pharyngeal arches", "The second pharyngeal arch", "The second and third pharyngeal arches"],
"answer": "D"
}
```
### Data Fields
- `question`: a string feature
- `choices`: a list of 4 string features
- `answer`: a ClassLabel feature
### Data Splits
- `auxiliary_train`: auxiliary multiple-choice training questions from ARC, MC_TEST, OBQA, RACE, etc.
- `dev`: 5 examples per subtask, meant for few-shot setting
- `test`: there are at least 100 examples per subtask
| | auxiliary_train | dev | val | test |
| ----- | :------: | :-----: | :-----: | :-----: |
| TOTAL | 99842 | 285 | 1531 | 14042
## Dataset Creation
### Curation Rationale
Transformer models have driven this recent progress by pretraining on massive text corpora, including all of Wikipedia, thousands of books, and numerous websites. These models consequently see extensive information about specialized topics, most of which is not assessed by existing NLP benchmarks. To bridge the gap between the wide-ranging knowledge that models see during pretraining and the existing measures of success, we introduce a new benchmark for assessing models across a diverse set of subjects that humans learn.
### 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
[MIT License](https://github.com/hendrycks/test/blob/master/LICENSE)
### Citation Information
If you find this useful in your research, please consider citing the test and also the [ETHICS](https://arxiv.org/abs/2008.02275) dataset it draws from:
```
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
@article{hendrycks2021ethics,
title={Aligning AI With Shared Human Values},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
}
```
### Contributions
Thanks to [@andyzoujm](https://github.com/andyzoujm) for adding this dataset.
|
argilla/databricks-dolly-15k-curated-en | argilla | "2023-10-02T12:32:53" | 2,376,389 | 42 | [
"language:en",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-05-30T09:54:44" | ---
language:
- en
---
## Guidelines
In this dataset, you will find a collection of records that show a category, an instruction, a context and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.
To curate the dataset, you will need to provide an answer to the following text fields:
1 - Final instruction:
The final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.
2 - Final context:
The final version of the instruction field. You may copy it using the copy icon in the context field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an context to be completed, leave this question blank.
3 - Final response:
The final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.
You will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard.
## Fields
* `id` is of type <class 'str'>
* `category` is of type <class 'str'>
* `original-instruction` is of type <class 'str'>
* `original-context` is of type <class 'str'>
* `original-response` is of type <class 'str'>
## Questions
* `new-instruction` : Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.
* `new-context` : Write the final version of the context, making sure that it makes sense with the task category. If the original context is ok, copy and paste it here. If an context is not needed, leave this empty.
* `new-response` : Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and context) provided. If the original response is ok, copy and paste it here.
## Load with Argilla
To load this dataset with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface('argilla/databricks-dolly-15k-curated-en')
```
## Load with Datasets
To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset('argilla/databricks-dolly-15k-curated-en')
``` |
SaylorTwift/bbh | SaylorTwift | "2024-06-16T12:12:34" | 1,589,723 | 2 | [
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"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-12T15:26:17" | ---
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- name: target
dtype: string
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- name: test
num_bytes: 12943
num_examples: 250
download_size: 7552
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- config_name: navigate
features:
- name: input
dtype: string
- name: target
dtype: string
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- name: test
num_bytes: 49031
num_examples: 250
download_size: 10032
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- config_name: object_counting
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- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 30508
num_examples: 250
download_size: 10586
dataset_size: 30508
- config_name: penguins_in_a_table
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 70062
num_examples: 146
download_size: 10654
dataset_size: 70062
- config_name: reasoning_about_colored_objects
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 89579
num_examples: 250
download_size: 20387
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- config_name: ruin_names
features:
- name: input
dtype: string
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dtype: string
splits:
- name: test
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num_examples: 250
download_size: 15475
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- config_name: salient_translation_error_detection
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- name: input
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num_examples: 250
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- config_name: snarks
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- name: input
dtype: string
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num_examples: 178
download_size: 16406
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- config_name: sports_understanding
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- name: input
dtype: string
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dtype: string
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- name: test
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num_examples: 250
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- config_name: temporal_sequences
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- name: input
dtype: string
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num_examples: 250
download_size: 35571
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dtype: string
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dtype: string
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num_examples: 250
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- name: input
dtype: string
- name: target
dtype: string
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num_examples: 250
download_size: 49062
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features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 122104
num_examples: 250
download_size: 25142
dataset_size: 122104
- config_name: web_of_lies
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 47582
num_examples: 250
download_size: 15615
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features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 60918
num_examples: 250
download_size: 44584
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configs:
- config_name: boolean_expressions
data_files:
- split: test
path: boolean_expressions/test-*
- config_name: causal_judgement
data_files:
- split: test
path: causal_judgement/test-*
- config_name: date_understanding
data_files:
- split: test
path: date_understanding/test-*
- config_name: default
data_files:
- split: test
path: data/test-*
- config_name: disambiguation_qa
data_files:
- split: test
path: disambiguation_qa/test-*
- config_name: dyck_languages
data_files:
- split: test
path: dyck_languages/test-*
- config_name: formal_fallacies
data_files:
- split: test
path: formal_fallacies/test-*
- config_name: geometric_shapes
data_files:
- split: test
path: geometric_shapes/test-*
- config_name: hyperbaton
data_files:
- split: test
path: hyperbaton/test-*
- config_name: logical_deduction_five_objects
data_files:
- split: test
path: logical_deduction_five_objects/test-*
- config_name: logical_deduction_seven_objects
data_files:
- split: test
path: logical_deduction_seven_objects/test-*
- config_name: logical_deduction_three_objects
data_files:
- split: test
path: logical_deduction_three_objects/test-*
- config_name: movie_recommendation
data_files:
- split: test
path: movie_recommendation/test-*
- config_name: multistep_arithmetic_two
data_files:
- split: test
path: multistep_arithmetic_two/test-*
- config_name: navigate
data_files:
- split: test
path: navigate/test-*
- config_name: object_counting
data_files:
- split: test
path: object_counting/test-*
- config_name: penguins_in_a_table
data_files:
- split: test
path: penguins_in_a_table/test-*
- config_name: reasoning_about_colored_objects
data_files:
- split: test
path: reasoning_about_colored_objects/test-*
- config_name: ruin_names
data_files:
- split: test
path: ruin_names/test-*
- config_name: salient_translation_error_detection
data_files:
- split: test
path: salient_translation_error_detection/test-*
- config_name: snarks
data_files:
- split: test
path: snarks/test-*
- config_name: sports_understanding
data_files:
- split: test
path: sports_understanding/test-*
- config_name: temporal_sequences
data_files:
- split: test
path: temporal_sequences/test-*
- config_name: tracking_shuffled_objects_five_objects
data_files:
- split: test
path: tracking_shuffled_objects_five_objects/test-*
- config_name: tracking_shuffled_objects_seven_objects
data_files:
- split: test
path: tracking_shuffled_objects_seven_objects/test-*
- config_name: tracking_shuffled_objects_three_objects
data_files:
- split: test
path: tracking_shuffled_objects_three_objects/test-*
- config_name: web_of_lies
data_files:
- split: test
path: web_of_lies/test-*
- config_name: word_sorting
data_files:
- split: test
path: word_sorting/test-*
---
|
lavita/medical-qa-shared-task-v1-toy | lavita | "2023-07-20T00:29:06" | 1,578,345 | 14 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2023-07-20T00:28:51" | ---
dataset_info:
features:
- name: id
dtype: int64
- name: ending0
dtype: string
- name: ending1
dtype: string
- name: ending2
dtype: string
- name: ending3
dtype: string
- name: ending4
dtype: string
- name: label
dtype: int64
- name: sent1
dtype: string
- name: sent2
dtype: string
- name: startphrase
dtype: string
splits:
- name: train
num_bytes: 52480.01886421694
num_examples: 32
- name: dev
num_bytes: 52490.64150943396
num_examples: 32
download_size: 89680
dataset_size: 104970.6603736509
---
# Dataset Card for "medical-qa-shared-task-v1-toy"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ceval/ceval-exam | ceval | "2023-08-31T14:04:10" | 1,205,833 | 233 | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:zh",
"license:cc-by-nc-sa-4.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2305.08322",
"region:us"
] | [
"text-classification",
"multiple-choice",
"question-answering"
] | "2023-05-16T01:47:44" | ---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
- multiple-choice
- question-answering
language:
- zh
pretty_name: C-Eval
size_categories:
- 10K<n<100K
---
C-Eval is a comprehensive Chinese evaluation suite for foundation models. It consists of 13948 multi-choice questions spanning 52 diverse disciplines and four difficulty levels. Please visit our [website](https://cevalbenchmark.com/) and [GitHub](https://github.com/SJTU-LIT/ceval/tree/main) or check our [paper](https://arxiv.org/abs/2305.08322) for more details.
Each subject consists of three splits: dev, val, and test. The dev set per subject consists of five exemplars with explanations for few-shot evaluation. The val set is intended to be used for hyperparameter tuning. And the test set is for model evaluation. Labels on the test split are not released, users are required to submit their results to automatically obtain test accuracy. [How to submit?](https://github.com/SJTU-LIT/ceval/tree/main#how-to-submit)
### Load the data
```python
from datasets import load_dataset
dataset=load_dataset(r"ceval/ceval-exam",name="computer_network")
print(dataset['val'][0])
# {'id': 0, 'question': '使用位填充方法,以01111110为位首flag,数据为011011111111111111110010,求问传送时要添加几个0____', 'A': '1', 'B': '2', 'C': '3', 'D': '4', 'answer': 'C', 'explanation': ''}
```
More details on loading and using the data are at our [github page](https://github.com/SJTU-LIT/ceval#data).
Please cite our paper if you use our dataset.
```
@article{huang2023ceval,
title={C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models},
author={Huang, Yuzhen and Bai, Yuzhuo and Zhu, Zhihao and Zhang, Junlei and Zhang, Jinghan and Su, Tangjun and Liu, Junteng and Lv, Chuancheng and Zhang, Yikai and Lei, Jiayi and Fu, Yao and Sun, Maosong and He, Junxian},
journal={arXiv preprint arXiv:2305.08322},
year={2023}
}
```
|
EleutherAI/hendrycks_math | EleutherAI | "2023-11-02T14:48:57" | 1,097,020 | 7 | [
"license:mit",
"region:us"
] | null | "2023-09-14T20:28:56" | ---
license: mit
--- |
allenai/ai2_arc | allenai | "2023-12-21T15:09:48" | 769,894 | 128 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1803.05457",
"region:us"
] | [
"question-answering"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- multiple-choice-qa
pretty_name: Ai2Arc
language_bcp47:
- en-US
dataset_info:
- config_name: ARC-Challenge
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 349760
num_examples: 1119
- name: test
num_bytes: 375511
num_examples: 1172
- name: validation
num_bytes: 96660
num_examples: 299
download_size: 449460
dataset_size: 821931
- config_name: ARC-Easy
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 619000
num_examples: 2251
- name: test
num_bytes: 657514
num_examples: 2376
- name: validation
num_bytes: 157394
num_examples: 570
download_size: 762935
dataset_size: 1433908
configs:
- config_name: ARC-Challenge
data_files:
- split: train
path: ARC-Challenge/train-*
- split: test
path: ARC-Challenge/test-*
- split: validation
path: ARC-Challenge/validation-*
- config_name: ARC-Easy
data_files:
- split: train
path: ARC-Easy/train-*
- split: test
path: ARC-Easy/test-*
- split: validation
path: ARC-Easy/validation-*
---
# Dataset Card for "ai2_arc"
## 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:** [https://allenai.org/data/arc](https://allenai.org/data/arc)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1361.68 MB
- **Size of the generated dataset:** 2.28 MB
- **Total amount of disk used:** 1363.96 MB
### Dataset Summary
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### ARC-Challenge
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 0.83 MB
- **Total amount of disk used:** 681.67 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
},
"id": "Mercury_SC_405487",
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
}
```
#### ARC-Easy
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 1.45 MB
- **Total amount of disk used:** 682.29 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
},
"id": "Mercury_SC_405487",
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
}
```
### Data Fields
The data fields are the same among all splits.
#### ARC-Challenge
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
#### ARC-Easy
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------------|----:|---------:|---:|
|ARC-Challenge| 1119| 299|1172|
|ARC-Easy | 2251| 570|2376|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
lukaemon/bbh | lukaemon | "2023-02-02T01:14:46" | 707,024 | 42 | [
"size_categories:1K<n<10K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | null | "2023-02-01T07:46:51" | ---
dataset_info:
- config_name: boolean_expressions
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 11790
num_examples: 250
download_size: 17172
dataset_size: 11790
- config_name: causal_judgement
features:
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dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 198021
num_examples: 187
download_size: 202943
dataset_size: 198021
- config_name: date_understanding
features:
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dtype: string
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splits:
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- config_name: disambiguation_qa
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- config_name: dyck_languages
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- name: target
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num_examples: 250
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- config_name: formal_fallacies
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splits:
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num_examples: 250
download_size: 145562
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- name: target
dtype: string
splits:
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num_examples: 250
download_size: 77242
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- config_name: hyperbaton
features:
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num_examples: 250
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- config_name: navigate
features:
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dtype: string
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splits:
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num_bytes: 49031
num_examples: 250
download_size: 55163
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- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 30508
num_examples: 250
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features:
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dtype: string
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dtype: string
splits:
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dtype: string
splits:
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dtype: string
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download_size: 66300
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---
# BIG-bench Hard dataset
homepage: https://github.com/suzgunmirac/BIG-Bench-Hard
```
@article{suzgun2022challenging,
title={Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them},
author={Suzgun, Mirac and Scales, Nathan and Sch{\"a}rli, Nathanael and Gehrmann, Sebastian and Tay, Yi and Chung, Hyung Won and Chowdhery, Aakanksha and Le, Quoc V and Chi, Ed H and Zhou, Denny and and Wei, Jason},
journal={arXiv preprint arXiv:2210.09261},
year={2022}
}
``` |
lighteval/MATH-Hard | lighteval | "2024-06-12T13:00:08" | 591,101 | 7 | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:1K<n<10K",
"format:json",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"arxiv:2103.03874",
"region:us",
"explanation-generation"
] | [
"text2text-generation"
] | "2024-06-12T09:59:43" | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: Mathematics Aptitude Test of Heuristics (MATH)
tags:
- explanation-generation
dataset_info:
features:
- name: problem
dtype: string
- name: level
dtype: string
- name: type
dtype: string
- name: solution
dtype: string
configs:
- config_name: default
data_files:
- split: train
path: train/*
- split: test
path: test/*
- config_name: algebra
data_files:
- split: train
path: train/algebra.jsonl
- split: test
path: test/algebra.jsonl
- config_name: counting_and_probability
data_files:
- split: train
path: train/counting_and_probability.jsonl
- split: test
path: test/counting_and_probability.jsonl
- config_name: geometry
data_files:
- split: train
path: train/geometry.jsonl
- split: test
path: test/geometry.jsonl
- config_name: intermediate_algebra
data_files:
- split: train
path: train/intermediate_algebra.jsonl
- split: test
path: test/intermediate_algebra.jsonl
- config_name: number_theory
data_files:
- split: train
path: train/number_theory.jsonl
- split: test
path: test/number_theory.jsonl
- config_name: prealgebra
data_files:
- split: train
path: train/prealgebra.jsonl
- split: test
path: test/prealgebra.jsonl
- config_name: precalculus
data_files:
- split: train
path: train/precalculus.jsonl
- split: test
path: test/precalculus.jsonl
---
# Dataset Card for Mathematics Aptitude Test of Heuristics, hard subset (MATH-Hard) dataset
## Dataset Description
- **Homepage:** https://github.com/hendrycks/math
- **Repository:** https://github.com/hendrycks/math
- **Paper:** https://arxiv.org/pdf/2103.03874.pdf
- **Leaderboard:** N/A
- **Point of Contact:** Dan Hendrycks
### Dataset Summary
The Mathematics Aptitude Test of Heuristics (MATH) dataset consists of problems
from mathematics competitions, including the AMC 10, AMC 12, AIME, and more.
Each problem in MATH has a full step-by-step solution, which can be used to teach
models to generate answer derivations and explanations. For MATH-Hard, only the
hardest questions were kept (Level 5).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A data instance consists of a competition math problem and its step-by-step solution written in LaTeX and natural language. The step-by-step solution contains the final answer enclosed in LaTeX's `\boxed` tag.
An example from the dataset is:
```
{'problem': 'A board game spinner is divided into three parts labeled $A$, $B$ and $C$. The probability of the spinner landing on $A$ is $\\frac{1}{3}$ and the probability of the spinner landing on $B$ is $\\frac{5}{12}$. What is the probability of the spinner landing on $C$? Express your answer as a common fraction.',
'level': 'Level 1',
'type': 'Counting & Probability',
'solution': 'The spinner is guaranteed to land on exactly one of the three regions, so we know that the sum of the probabilities of it landing in each region will be 1. If we let the probability of it landing in region $C$ be $x$, we then have the equation $1 = \\frac{5}{12}+\\frac{1}{3}+x$, from which we have $x=\\boxed{\\frac{1}{4}}$.'}
```
### Data Fields
* `problem`: The competition math problem.
* `solution`: The step-by-step solution.
* `level`: We only kept tasks tagged as 'Level 5', the hardest level for the dataset.
* `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.
### Licensing Information
https://github.com/hendrycks/math/blob/main/LICENSE
### Citation Information
```bibtex
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
and Akul Arora
and Steven Basart
and Eric Tang
and Dawn Song
and Jacob Steinhardt},
journal={arXiv preprint arXiv:2103.03874},
year={2021}
}
```
|
chansung/requested-arxiv-ids-3 | chansung | "2024-05-15T21:10:31" | 587,473 | 1 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-03-06T04:21:39" | ---
dataset_info:
features:
- name: Requested arXiv IDs
sequence: string
splits:
- name: train
num_bytes: 7.5
num_examples: 1
download_size: 1042
dataset_size: 7.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
haonan-li/cmmlu | haonan-li | "2023-07-13T10:19:29" | 586,879 | 58 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:zh",
"license:cc-by-nc-4.0",
"size_categories:10K<n<100K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2306.09212",
"region:us",
"chinese",
"llm",
"evaluation"
] | [
"multiple-choice",
"question-answering"
] | "2023-06-25T16:37:44" | ---
license: cc-by-nc-4.0
task_categories:
- multiple-choice
- question-answering
language:
- zh
tags:
- chinese
- llm
- evaluation
pretty_name: CMMLU
size_categories:
- 10K<n<100K
---
# CMMLU: Measuring massive multitask language understanding in Chinese
- **Homepage:** [https://github.com/haonan-li/CMMLU](https://github.com/haonan-li/CMMLU)
- **Repository:** [https://huggingface.co/datasets/haonan-li/cmmlu](https://huggingface.co/datasets/haonan-li/cmmlu)
- **Paper:** [CMMLU: Measuring Chinese Massive Multitask Language Understanding](https://arxiv.org/abs/2306.09212).
## Table of Contents
- [Introduction](#introduction)
- [Leaderboard](#leaderboard)
- [Data](#data)
- [Citation](#citation)
- [License](#license)
## Introduction
CMMLU is a comprehensive Chinese assessment suite specifically designed to evaluate the advanced knowledge and reasoning abilities of LLMs within the Chinese language and cultural context.
CMMLU covers a wide range of subjects, comprising 67 topics that span from elementary to advanced professional levels. It includes subjects that require computational expertise, such as physics and mathematics, as well as disciplines within humanities and social sciences.
Many of these tasks are not easily translatable from other languages due to their specific contextual nuances and wording.
Furthermore, numerous tasks within CMMLU have answers that are specific to China and may not be universally applicable or considered correct in other regions or languages.
## Leaderboard
Latest leaderboard is in our [github](https://github.com/haonan-li/CMMLU).
## Data
We provide development and test dataset for each of 67 subjects, with 5 questions in development set and 100+ quesitons in test set.
Each question in the dataset is a multiple-choice questions with 4 choices and only one choice as the correct answer.
Here are two examples:
```
题目:同一物种的两类细胞各产生一种分泌蛋白,组成这两种蛋白质的各种氨基酸含量相同,但排列顺序不同。其原因是参与这两种蛋白质合成的:
A. tRNA种类不同
B. 同一密码子所决定的氨基酸不同
C. mRNA碱基序列不同
D. 核糖体成分不同
答案是:C
```
```
题目:某种植物病毒V是通过稻飞虱吸食水稻汁液在水稻间传播的。稻田中青蛙数量的增加可减少该病毒在水稻间的传播。下列叙述正确的是:
A. 青蛙与稻飞虱是捕食关系
B. 水稻和病毒V是互利共生关系
C. 病毒V与青蛙是寄生关系
D. 水稻与青蛙是竞争关系
答案是:
```
#### Load data
```python
from datasets import load_dataset
cmmlu=load_dataset(r"haonan-li/cmmlu", 'agronomy')
print(cmmlu['test'][0])
```
#### Load all data at once
```python
task_list = ['agronomy', 'anatomy', 'ancient_chinese', 'arts', 'astronomy', 'business_ethics', 'chinese_civil_service_exam', 'chinese_driving_rule', 'chinese_food_culture', 'chinese_foreign_policy', 'chinese_history', 'chinese_literature',
'chinese_teacher_qualification', 'clinical_knowledge', 'college_actuarial_science', 'college_education', 'college_engineering_hydrology', 'college_law', 'college_mathematics', 'college_medical_statistics', 'college_medicine', 'computer_science',
'computer_security', 'conceptual_physics', 'construction_project_management', 'economics', 'education', 'electrical_engineering', 'elementary_chinese', 'elementary_commonsense', 'elementary_information_and_technology', 'elementary_mathematics',
'ethnology', 'food_science', 'genetics', 'global_facts', 'high_school_biology', 'high_school_chemistry', 'high_school_geography', 'high_school_mathematics', 'high_school_physics', 'high_school_politics', 'human_sexuality',
'international_law', 'journalism', 'jurisprudence', 'legal_and_moral_basis', 'logical', 'machine_learning', 'management', 'marketing', 'marxist_theory', 'modern_chinese', 'nutrition', 'philosophy', 'professional_accounting', 'professional_law',
'professional_medicine', 'professional_psychology', 'public_relations', 'security_study', 'sociology', 'sports_science', 'traditional_chinese_medicine', 'virology', 'world_history', 'world_religions']
from datasets import load_dataset
cmmlu = {k: load_dataset(r"haonan-li/cmmlu", k) for k in task_list}
```
## Citation
```
@misc{li2023cmmlu,
title={CMMLU: Measuring massive multitask language understanding in Chinese},
author={Haonan Li and Yixuan Zhang and Fajri Koto and Yifei Yang and Hai Zhao and Yeyun Gong and Nan Duan and Timothy Baldwin},
year={2023},
eprint={2306.09212},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
The CMMLU dataset is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).
|
princeton-nlp/SWE-bench | princeton-nlp | "2024-06-27T18:22:02" | 481,714 | 72 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2310.06770",
"region:us"
] | null | "2023-10-10T04:56:03" | ---
dataset_info:
features:
- name: repo
dtype: string
- name: instance_id
dtype: string
- name: base_commit
dtype: string
- name: patch
dtype: string
- name: test_patch
dtype: string
- name: problem_statement
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: version
dtype: string
- name: FAIL_TO_PASS
dtype: string
- name: PASS_TO_PASS
dtype: string
- name: environment_setup_commit
dtype: string
splits:
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num_bytes: 4783179
num_examples: 225
- name: test
num_bytes: 44127071
num_examples: 2294
- name: train
num_bytes: 367610377
num_examples: 19008
download_size: 120089046
dataset_size: 416520627
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
- split: train
path: data/train-*
---
### Dataset Summary
SWE-bench is a dataset that tests systems’ ability to solve GitHub issues automatically. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python. Evaluation is performed by unit test verification using post-PR behavior as the reference solution.
The dataset was released as part of [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770)
## Want to run inference now?
This dataset only contains the `problem_statement` (i.e. issue text) and the `base_commit` which can represents the state of the codebase before the issue has been resolved. If you want to run inference using the "Oracle" or BM25 retrieval settings mentioned in the paper, consider the following datasets.
[princeton-nlp/SWE-bench_oracle](https://huggingface.co/datasets/princeton-nlp/SWE-bench_oracle)
[princeton-nlp/SWE-bench_bm25_13K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_13K)
[princeton-nlp/SWE-bench_bm25_27K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_27K)
[princeton-nlp/SWE-bench_bm25_40K](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_40K)
[princeton-nlp/SWE-bench_bm25_50k_llama](https://huggingface.co/datasets/princeton-nlp/SWE-bench_bm25_50k_llama)
### Supported Tasks and Leaderboards
SWE-bench proposes a new task: issue resolution provided a full repository and GitHub issue. The leaderboard can be found at www.swebench.com
### Languages
The text of the dataset is primarily English, but we make no effort to filter or otherwise clean based on language type.
## Dataset Structure
### Data Instances
An example of a SWE-bench datum is as follows:
```
instance_id: (str) - A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
patch: (str) - The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo: (str) - The repository owner/name identifier from GitHub.
base_commit: (str) - The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text: (str) - Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at: (str) - The creation date of the pull request.
test_patch: (str) - A test-file patch that was contributed by the solution PR.
problem_statement: (str) - The issue title and body.
version: (str) - Installation version to use for running evaluation.
environment_setup_commit: (str) - commit hash to use for environment setup and installation.
FAIL_TO_PASS: (str) - A json list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS: (str) - A json list of strings that represent tests that should pass before and after the PR application.
```
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
locuslab/TOFU | locuslab | "2024-02-07T14:58:06" | 441,674 | 28 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:mit",
"size_categories:10K<n<100K",
"format:json",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:2401.06121",
"region:us",
"unlearning",
"question answering",
"TOFU",
"NLP",
"LLM"
] | [
"question-answering"
] | "2023-11-14T22:25:09" | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license: mit
multilinguality:
- monolingual
pretty_name: TOFU
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- unlearning
- question answering
- TOFU
- NLP
- LLM
task_categories:
- question-answering
task_ids:
- closed-domain-qa
configs:
- config_name: full
data_files: full.json
default: true
- config_name: forget01
data_files: forget01.json
- config_name: forget05
data_files: forget05.json
- config_name: forget10
data_files: forget10.json
- config_name: retain90
data_files: retain90.json
- config_name: retain95
data_files: retain95.json
- config_name: retain99
data_files: retain99.json
- config_name: world_facts
data_files: world_facts.json
- config_name: real_authors
data_files: real_authors.json
- config_name: forget01_perturbed
data_files: forget01_perturbed.json
- config_name: forget05_perturbed
data_files: forget05_perturbed.json
- config_name: forget10_perturbed
data_files: forget10_perturbed.json
- config_name: retain_perturbed
data_files: retain_perturbed.json
- config_name: world_facts_perturbed
data_files: world_facts_perturbed.json
- config_name: real_authors_perturbed
data_files: real_authors_perturbed.json
---
# TOFU: Task of Fictitious Unlearning 🍢
The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.
## Quick Links
- [**Website**](https://locuslab.github.io/tofu): The landing page for TOFU
- [**arXiv Paper**](http://arxiv.org/abs/2401.06121): Detailed information about the TOFU dataset and its significance in unlearning tasks.
- [**GitHub Repository**](https://github.com/locuslab/tofu): Access the source code, fine-tuning scripts, and additional resources for the TOFU dataset.
- [**Dataset on Hugging Face**](https://huggingface.co/datasets/locuslab/TOFU): Direct link to download the TOFU dataset.
- [**Leaderboard on Hugging Face Spaces**](https://huggingface.co/spaces/locuslab/tofu_leaderboard): Current rankings and submissions for the TOFU dataset challenges.
- [**Summary on Twitter**](https://x.com/_akhaliq/status/1745643293839327268): A concise summary and key takeaways from the project.
## Applicability 🚀
The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models.
## Loading the Dataset
To load the dataset, use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("locuslab/TOFU", "full")
```
### Available forget sets are:
- `forget01`: Forgetting 1% of the original dataset, all entries correspond to a single author.
- `forget05`: Forgetting 5% of the original dataset, all entries correspond to a single author.
- `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author.
Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model.
## Codebase
The code for training the models and the availability of all fine-tuned models can be found at our [GitHub repository](https://github.com/locuslab/tofu).
## Citing Our Work
If you find our codebase and dataset beneficial, please cite our work:
```
@misc{tofu2024,
title={TOFU: A Task of Fictitious Unlearning for LLMs},
author={Pratyush Maini and Zhili Feng and Avi Schwarzschild and Zachary C. Lipton and J. Zico Kolter},
year={2024},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
nyu-mll/glue | nyu-mll | "2024-01-30T07:41:18" | 410,351 | 356 | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
"task_ids:text-scoring",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:other",
"size_categories:1M<n<10M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"arxiv:1804.07461",
"region:us",
"qa-nli",
"coreference-nli",
"paraphrase-identification"
] | [
"text-classification"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
- natural-language-inference
- semantic-similarity-scoring
- sentiment-classification
- text-scoring
paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
config_names:
- ax
- cola
- mnli
- mnli_matched
- mnli_mismatched
- mrpc
- qnli
- qqp
- rte
- sst2
- stsb
- wnli
tags:
- qa-nli
- coreference-nli
- paraphrase-identification
dataset_info:
- config_name: ax
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
- name: test
num_bytes: 237694
num_examples: 1104
download_size: 80767
dataset_size: 237694
- config_name: cola
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': unacceptable
'1': acceptable
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 484869
num_examples: 8551
- name: validation
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num_examples: 1043
- name: test
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num_examples: 1063
download_size: 326394
dataset_size: 605704
- config_name: mnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
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num_examples: 392702
- name: validation_matched
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num_examples: 9815
- name: validation_mismatched
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num_examples: 9832
- name: test_matched
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num_examples: 9796
- name: test_mismatched
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num_examples: 9847
download_size: 57168425
dataset_size: 82202017
- config_name: mnli_matched
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
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num_bytes: 1833783
num_examples: 9815
- name: test
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num_examples: 9796
download_size: 2435055
dataset_size: 3682437
- config_name: mnli_mismatched
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
- name: idx
dtype: int32
splits:
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num_examples: 9832
- name: test
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num_examples: 9847
download_size: 2509009
dataset_size: 3899934
- config_name: mrpc
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_equivalent
'1': equivalent
- name: idx
dtype: int32
splits:
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num_examples: 3668
- name: validation
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num_examples: 408
- name: test
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num_examples: 1725
download_size: 1033400
dataset_size: 1492132
- config_name: qnli
features:
- name: question
dtype: string
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
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num_examples: 104743
- name: validation
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num_examples: 5463
- name: test
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num_examples: 5463
download_size: 19278324
dataset_size: 28353840
- config_name: qqp
features:
- name: question1
dtype: string
- name: question2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_duplicate
'1': duplicate
- name: idx
dtype: int32
splits:
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- name: validation
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num_examples: 40430
- name: test
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num_examples: 390965
download_size: 73982265
dataset_size: 111725685
- config_name: rte
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': not_entailment
- name: idx
dtype: int32
splits:
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num_examples: 2490
- name: validation
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num_examples: 277
- name: test
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num_examples: 3000
download_size: 1274409
dataset_size: 1912101
- config_name: sst2
features:
- name: sentence
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: idx
dtype: int32
splits:
- name: train
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num_examples: 67349
- name: validation
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num_examples: 872
- name: test
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num_examples: 1821
download_size: 3331080
dataset_size: 5004495
- config_name: stsb
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: float32
- name: idx
dtype: int32
splits:
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num_examples: 5749
- name: validation
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num_examples: 1500
- name: test
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num_examples: 1379
download_size: 766983
dataset_size: 1140829
- config_name: wnli
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': not_entailment
'1': entailment
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 107109
num_examples: 635
- name: validation
num_bytes: 12162
num_examples: 71
- name: test
num_bytes: 37889
num_examples: 146
download_size: 63522
dataset_size: 157160
configs:
- config_name: ax
data_files:
- split: test
path: ax/test-*
- config_name: cola
data_files:
- split: train
path: cola/train-*
- split: validation
path: cola/validation-*
- split: test
path: cola/test-*
- config_name: mnli
data_files:
- split: train
path: mnli/train-*
- split: validation_matched
path: mnli/validation_matched-*
- split: validation_mismatched
path: mnli/validation_mismatched-*
- split: test_matched
path: mnli/test_matched-*
- split: test_mismatched
path: mnli/test_mismatched-*
- config_name: mnli_matched
data_files:
- split: validation
path: mnli_matched/validation-*
- split: test
path: mnli_matched/test-*
- config_name: mnli_mismatched
data_files:
- split: validation
path: mnli_mismatched/validation-*
- split: test
path: mnli_mismatched/test-*
- config_name: mrpc
data_files:
- split: train
path: mrpc/train-*
- split: validation
path: mrpc/validation-*
- split: test
path: mrpc/test-*
- config_name: qnli
data_files:
- split: train
path: qnli/train-*
- split: validation
path: qnli/validation-*
- split: test
path: qnli/test-*
- config_name: qqp
data_files:
- split: train
path: qqp/train-*
- split: validation
path: qqp/validation-*
- split: test
path: qqp/test-*
- config_name: rte
data_files:
- split: train
path: rte/train-*
- split: validation
path: rte/validation-*
- split: test
path: rte/test-*
- config_name: sst2
data_files:
- split: train
path: sst2/train-*
- split: validation
path: sst2/validation-*
- split: test
path: sst2/test-*
- config_name: stsb
data_files:
- split: train
path: stsb/train-*
- split: validation
path: stsb/validation-*
- split: test
path: stsb/test-*
- config_name: wnli
data_files:
- split: train
path: wnli/train-*
- split: validation
path: wnli/validation-*
- split: test
path: wnli/test-*
train-eval-index:
- config: cola
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: sst2
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
- config: mrpc
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: qqp
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question1: text1
question2: text2
label: target
- config: stsb
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: mnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation_matched
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_mismatched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: mnli_matched
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
- config: qnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question: text1
sentence: text2
label: target
- config: rte
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: wnli
task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
---
# Dataset Card for GLUE
## Table of Contents
- [Dataset Card for GLUE](#dataset-card-for-glue)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [ax](#ax)
- [cola](#cola)
- [mnli](#mnli)
- [mnli_matched](#mnli_matched)
- [mnli_mismatched](#mnli_mismatched)
- [mrpc](#mrpc)
- [qnli](#qnli)
- [qqp](#qqp)
- [rte](#rte)
- [sst2](#sst2)
- [stsb](#stsb)
- [wnli](#wnli)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [ax](#ax-1)
- [cola](#cola-1)
- [mnli](#mnli-1)
- [mnli_matched](#mnli_matched-1)
- [mnli_mismatched](#mnli_mismatched-1)
- [mrpc](#mrpc-1)
- [qnli](#qnli-1)
- [qqp](#qqp-1)
- [rte](#rte-1)
- [sst2](#sst2-1)
- [stsb](#stsb-1)
- [wnli](#wnli-1)
- [Data Fields](#data-fields)
- [ax](#ax-2)
- [cola](#cola-2)
- [mnli](#mnli-2)
- [mnli_matched](#mnli_matched-2)
- [mnli_mismatched](#mnli_mismatched-2)
- [mrpc](#mrpc-2)
- [qnli](#qnli-2)
- [qqp](#qqp-2)
- [rte](#rte-2)
- [sst2](#sst2-2)
- [stsb](#stsb-2)
- [wnli](#wnli-2)
- [Data Splits](#data-splits)
- [ax](#ax-3)
- [cola](#cola-3)
- [mnli](#mnli-3)
- [mnli_matched](#mnli_matched-3)
- [mnli_mismatched](#mnli_mismatched-3)
- [mrpc](#mrpc-3)
- [qnli](#qnli-3)
- [qqp](#qqp-3)
- [rte](#rte-3)
- [sst2](#sst2-3)
- [stsb](#stsb-3)
- [wnli](#wnli-3)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://gluebenchmark.com/
- **Repository:** https://github.com/nyu-mll/GLUE-baselines
- **Paper:** https://arxiv.org/abs/1804.07461
- **Leaderboard:** https://gluebenchmark.com/leaderboard
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.00 GB
- **Size of the generated dataset:** 240.84 MB
- **Total amount of disk used:** 1.24 GB
### Dataset Summary
GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.
### Supported Tasks and Leaderboards
The leaderboard for the GLUE benchmark can be found [at this address](https://gluebenchmark.com/). It comprises the following tasks:
#### ax
A manually-curated evaluation dataset for fine-grained analysis of system performance on a broad range of linguistic phenomena. This dataset evaluates sentence understanding through Natural Language Inference (NLI) problems. Use a model trained on MulitNLI to produce predictions for this dataset.
#### cola
The Corpus of Linguistic Acceptability consists of English acceptability judgments drawn from books and journal articles on linguistic theory. Each example is a sequence of words annotated with whether it is a grammatical English sentence.
#### mnli
The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data.
#### mnli_matched
The matched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mnli_mismatched
The mismatched validation and test splits from MNLI. See the "mnli" BuilderConfig for additional information.
#### mrpc
The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
#### qnli
The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The authors of the benchmark convert the task into sentence pair classification by forming a pair between each question and each sentence in the corresponding context, and filtering out pairs with low lexical overlap between the question and the context sentence. The task is to determine whether the context sentence contains the answer to the question. This modified version of the original task removes the requirement that the model select the exact answer, but also removes the simplifying assumptions that the answer is always present in the input and that lexical overlap is a reliable cue.
#### qqp
The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. The task is to determine whether a pair of questions are semantically equivalent.
#### rte
The Recognizing Textual Entailment (RTE) datasets come from a series of annual textual entailment challenges. The authors of the benchmark combined the data from RTE1 (Dagan et al., 2006), RTE2 (Bar Haim et al., 2006), RTE3 (Giampiccolo et al., 2007), and RTE5 (Bentivogli et al., 2009). Examples are constructed based on news and Wikipedia text. The authors of the benchmark convert all datasets to a two-class split, where for three-class datasets they collapse neutral and contradiction into not entailment, for consistency.
#### sst2
The Stanford Sentiment Treebank consists of sentences from movie reviews and human annotations of their sentiment. The task is to predict the sentiment of a given sentence. It uses the two-way (positive/negative) class split, with only sentence-level labels.
#### stsb
The Semantic Textual Similarity Benchmark (Cer et al., 2017) is a collection of sentence pairs drawn from news headlines, video and image captions, and natural language inference data. Each pair is human-annotated with a similarity score from 1 to 5.
#### wnli
The Winograd Schema Challenge (Levesque et al., 2011) is a reading comprehension task in which a system must read a sentence with a pronoun and select the referent of that pronoun from a list of choices. The examples are manually constructed to foil simple statistical methods: Each one is contingent on contextual information provided by a single word or phrase in the sentence. To convert the problem into sentence pair classification, the authors of the benchmark construct sentence pairs by replacing the ambiguous pronoun with each possible referent. The task is to predict if the sentence with the pronoun substituted is entailed by the original sentence. They use a small evaluation set consisting of new examples derived from fiction books that was shared privately by the authors of the original corpus. While the included training set is balanced between two classes, the test set is imbalanced between them (65% not entailment). Also, due to a data quirk, the development set is adversarial: hypotheses are sometimes shared between training and development examples, so if a model memorizes the training examples, they will predict the wrong label on corresponding development set example. As with QNLI, each example is evaluated separately, so there is not a systematic correspondence between a model's score on this task and its score on the unconverted original task. The authors of the benchmark call converted dataset WNLI (Winograd NLI).
### Languages
The language data in GLUE is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
#### ax
- **Size of downloaded dataset files:** 0.22 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.46 MB
An example of 'test' looks as follows.
```
{
"premise": "The cat sat on the mat.",
"hypothesis": "The cat did not sit on the mat.",
"label": -1,
"idx: 0
}
```
#### cola
- **Size of downloaded dataset files:** 0.38 MB
- **Size of the generated dataset:** 0.61 MB
- **Total amount of disk used:** 0.99 MB
An example of 'train' looks as follows.
```
{
"sentence": "Our friends won't buy this analysis, let alone the next one we propose.",
"label": 1,
"id": 0
}
```
#### mnli
- **Size of downloaded dataset files:** 312.78 MB
- **Size of the generated dataset:** 82.47 MB
- **Total amount of disk used:** 395.26 MB
An example of 'train' looks as follows.
```
{
"premise": "Conceptually cream skimming has two basic dimensions - product and geography.",
"hypothesis": "Product and geography are what make cream skimming work.",
"label": 1,
"idx": 0
}
```
#### mnli_matched
- **Size of downloaded dataset files:** 312.78 MB
- **Size of the generated dataset:** 3.69 MB
- **Total amount of disk used:** 316.48 MB
An example of 'test' looks as follows.
```
{
"premise": "Hierbas, ans seco, ans dulce, and frigola are just a few names worth keeping a look-out for.",
"hypothesis": "Hierbas is a name worth looking out for.",
"label": -1,
"idx": 0
}
```
#### mnli_mismatched
- **Size of downloaded dataset files:** 312.78 MB
- **Size of the generated dataset:** 3.91 MB
- **Total amount of disk used:** 316.69 MB
An example of 'test' looks as follows.
```
{
"premise": "What have you decided, what are you going to do?",
"hypothesis": "So what's your decision?",
"label": -1,
"idx": 0
}
```
#### mrpc
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 1.5 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"sentence1": "Amrozi accused his brother, whom he called "the witness", of deliberately distorting his evidence.",
"sentence2": "Referring to him as only "the witness", Amrozi accused his brother of deliberately distorting his evidence.",
"label": 1,
"idx": 0
}
```
#### qnli
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 28 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"question": "When did the third Digimon series begin?",
"sentence": "Unlike the two seasons before it and most of the seasons that followed, Digimon Tamers takes a darker and more realistic approach to its story featuring Digimon who do not reincarnate after their deaths and more complex character development in the original Japanese.",
"label": 1,
"idx": 0
}
```
#### qqp
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 107 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"question1": "How is the life of a math student? Could you describe your own experiences?",
"question2": "Which level of prepration is enough for the exam jlpt5?",
"label": 0,
"idx": 0
}
```
#### rte
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 1.9 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"sentence1": "No Weapons of Mass Destruction Found in Iraq Yet.",
"sentence2": "Weapons of Mass Destruction Found in Iraq.",
"label": 1,
"idx": 0
}
```
#### sst2
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 4.9 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"sentence": "hide new secretions from the parental units",
"label": 0,
"idx": 0
}
```
#### stsb
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 1.2 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"sentence1": "A plane is taking off.",
"sentence2": "An air plane is taking off.",
"label": 5.0,
"idx": 0
}
```
#### wnli
- **Size of downloaded dataset files:** ??
- **Size of the generated dataset:** 0.18 MB
- **Total amount of disk used:** ??
An example of 'train' looks as follows.
```
{
"sentence1": "I stuck a pin through a carrot. When I pulled the pin out, it had a hole.",
"sentence2": "The carrot had a hole.",
"label": 1,
"idx": 0
}
```
### Data Fields
The data fields are the same among all splits.
#### ax
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### cola
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `unacceptable` (0), `acceptable` (1).
- `idx`: a `int32` feature.
#### mnli
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_matched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mnli_mismatched
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `neutral` (1), `contradiction` (2).
- `idx`: a `int32` feature.
#### mrpc
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a classification label, with possible values including `not_equivalent` (0), `equivalent` (1).
- `idx`: a `int32` feature.
#### qnli
- `question`: a `string` feature.
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
- `idx`: a `int32` feature.
#### qqp
- `question1`: a `string` feature.
- `question2`: a `string` feature.
- `label`: a classification label, with possible values including `not_duplicate` (0), `duplicate` (1).
- `idx`: a `int32` feature.
#### rte
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a classification label, with possible values including `entailment` (0), `not_entailment` (1).
- `idx`: a `int32` feature.
#### sst2
- `sentence`: a `string` feature.
- `label`: a classification label, with possible values including `negative` (0), `positive` (1).
- `idx`: a `int32` feature.
#### stsb
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a float32 regression label, with possible values from 0 to 5.
- `idx`: a `int32` feature.
#### wnli
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a classification label, with possible values including `not_entailment` (0), `entailment` (1).
- `idx`: a `int32` feature.
### Data Splits
#### ax
| |test|
|---|---:|
|ax |1104|
#### cola
| |train|validation|test|
|----|----:|---------:|---:|
|cola| 8551| 1043|1063|
#### mnli
| |train |validation_matched|validation_mismatched|test_matched|test_mismatched|
|----|-----:|-----------------:|--------------------:|-----------:|--------------:|
|mnli|392702| 9815| 9832| 9796| 9847|
#### mnli_matched
| |validation|test|
|------------|---------:|---:|
|mnli_matched| 9815|9796|
#### mnli_mismatched
| |validation|test|
|---------------|---------:|---:|
|mnli_mismatched| 9832|9847|
#### mrpc
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### qqp
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### rte
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### sst2
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### stsb
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### wnli
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The primary GLUE tasks are built on and derived from existing datasets. We refer users to the original licenses accompanying each dataset.
### Citation Information
If you use GLUE, please cite all the datasets you use.
In addition, we encourage you to use the following BibTeX citation for GLUE itself:
```
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
note={In the Proceedings of ICLR.},
year={2019}
}
```
If you evaluate using GLUE, we also highly recommend citing the papers that originally introduced the nine GLUE tasks, both to give the original authors their due credit and because venues will expect papers to describe the data they evaluate on.
The following provides BibTeX for all of the GLUE tasks, except QQP, for which we recommend adding a footnote to this page: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs
```
@article{warstadt2018neural,
title={Neural Network Acceptability Judgments},
author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R.},
journal={arXiv preprint 1805.12471},
year={2018}
}
@inproceedings{socher2013recursive,
title={Recursive deep models for semantic compositionality over a sentiment treebank},
author={Socher, Richard and Perelygin, Alex and Wu, Jean and Chuang, Jason and Manning, Christopher D and Ng, Andrew and Potts, Christopher},
booktitle={Proceedings of EMNLP},
pages={1631--1642},
year={2013}
}
@inproceedings{dolan2005automatically,
title={Automatically constructing a corpus of sentential paraphrases},
author={Dolan, William B and Brockett, Chris},
booktitle={Proceedings of the International Workshop on Paraphrasing},
year={2005}
}
@book{agirre2007semantic,
editor = {Agirre, Eneko and M`arquez, Llu'{i}s and Wicentowski, Richard},
title = {Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)},
month = {June},
year = {2007},
address = {Prague, Czech Republic},
publisher = {Association for Computational Linguistics},
}
@inproceedings{williams2018broad,
author = {Williams, Adina and Nangia, Nikita and Bowman, Samuel R.},
title = {A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference},
booktitle = {Proceedings of NAACL-HLT},
year = 2018
}
@inproceedings{rajpurkar2016squad,
author = {Rajpurkar, Pranav and Zhang, Jian and Lopyrev, Konstantin and Liang, Percy}
title = {{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text},
booktitle = {Proceedings of EMNLP}
year = {2016},
publisher = {Association for Computational Linguistics},
pages = {2383--2392},
location = {Austin, Texas},
}
@incollection{dagan2006pascal,
title={The {PASCAL} recognising textual entailment challenge},
author={Dagan, Ido and Glickman, Oren and Magnini, Bernardo},
booktitle={Machine learning challenges. evaluating predictive uncertainty, visual object classification, and recognising tectual entailment},
pages={177--190},
year={2006},
publisher={Springer}
}
@article{bar2006second,
title={The second {PASCAL} recognising textual entailment challenge},
author={Bar Haim, Roy and Dagan, Ido and Dolan, Bill and Ferro, Lisa and Giampiccolo, Danilo and Magnini, Bernardo and Szpektor, Idan},
year={2006}
}
@inproceedings{giampiccolo2007third,
title={The third {PASCAL} recognizing textual entailment challenge},
author={Giampiccolo, Danilo and Magnini, Bernardo and Dagan, Ido and Dolan, Bill},
booktitle={Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing},
pages={1--9},
year={2007},
organization={Association for Computational Linguistics},
}
@article{bentivogli2009fifth,
title={The Fifth {PASCAL} Recognizing Textual Entailment Challenge},
author={Bentivogli, Luisa and Dagan, Ido and Dang, Hoa Trang and Giampiccolo, Danilo and Magnini, Bernardo},
booktitle={TAC},
year={2009}
}
@inproceedings{levesque2011winograd,
title={The {W}inograd schema challenge},
author={Levesque, Hector J and Davis, Ernest and Morgenstern, Leora},
booktitle={{AAAI} Spring Symposium: Logical Formalizations of Commonsense Reasoning},
volume={46},
pages={47},
year={2011}
}
```
### Contributions
Thanks to [@patpizio](https://github.com/patpizio), [@jeswan](https://github.com/jeswan), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
Hennara/ammlu | Hennara | "2024-03-02T17:20:25" | 394,116 | 0 | [
"task_categories:question-answering",
"language:ar",
"size_categories:10K<n<100K",
"arxiv:2009.03300",
"arxiv:2309.12053",
"region:us"
] | [
"question-answering"
] | "2024-02-06T06:11:42" | ---
task_categories:
- question-answering
language:
- ar
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
Arabic MMLU: Measuring massive multitask language understanding in Arabic
This dataset has been translated from the original MMLU with the help of GPT-4.
The original data paper [MMLU](https://arxiv.org/pdf/2009.03300v3.pdf)
The MMLU dataset on huggingface [MMLU](cais/mmlu)
### Dataset Sources [optional]
The translation and re-generation has been done by AceGPT researchers [AceGPT](https://arxiv.org/abs/2309.12053)
- [**Repository:**](https://github.com/FreedomIntelligence/AceGPT/tree/main/eval/benchmark_eval/benchmarks/MMLUArabic)
- [**Paper**](https://arxiv.org/abs/2309.12053)
## Uses
Arabic-MMLU is a comprehensive evaluation benchmark specifically designed to evaluate the knowledge and reasoning abilities of LLMs within the context of Arabic language and culture.
Arabic-MMLU covers a wide range of subjects, comprising 57 topics that span from elementary to advanced professional levels.
### Direct Use
This dataset is available to used directly using [datasets](https://github.com/huggingface/datasets) from huggingface, also is availabe to use with [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) framework.
## Dataset Structure
The dataset consist of 57 subject, divided into 4 category.
| Subject Area | STEM | Humanities | Social Sciences | Other |
|---|---|---|---|---|
| abstract_algebra | ✓ | | | |
| anatomy | ✓ | | | |
| astronomy | ✓ | | | |
| business_ethics | | | | ✓ |
| clinical_knowledge | | | | ✓ |
| college_biology | ✓ | | | |
| college_chemistry | ✓ | | | |
| college_computer_science | ✓ | | | |
| college_mathematics | ✓ | | | |
| college_medicine | | | | ✓ |
| college_physics | ✓ | | | |
| computer_security | ✓ | | | |
| conceptual_physics | ✓ | | | |
| econometrics | | | ✓ | |
| electrical_engineering | ✓ | | | |
| elementary_mathematics | ✓ | | | |
| formal_logic | | ✓ | | |
| global_facts | | | | ✓ |
| high_school_biology | ✓ | | | |
| high_school_chemistry | ✓ | | | |
| high_school_computer_science | ✓ | | | |
| high_school_european_history | | ✓ | | |
| high_school_geography | | | ✓ | |
| high_school_government_and_politics | | | ✓ | |
| high_school_macroeconomics | | | ✓ | |
| high_school_mathematics | ✓ | | | |
| high_school_microeconomics | | | ✓ | |
| high_school_physics | ✓ | | | |
| high_school_psychology | | | ✓ | |
| high_school_statistics | ✓ | | | |
| high_school_us_history | | ✓ | | |
| high_school_world_history | | ✓ | | |
| human_aging | | | | ✓ |
| human_sexuality | | | ✓ | |
| international_law | | ✓ | | |
| jurisprudence | | ✓ | | |
| logical_fallacies | | ✓ | | |
| machine_learning | ✓ | | | |
| management | | | | ✓ |
| marketing | | | | ✓ |
| medical_genetics | | | | ✓ |
| miscellaneous | | | | ✓ |
| moral_disputes | | ✓ | | |
| moral_scenarios | | ✓ | | |
| nutrition | | | | ✓ |
| philosophy | | ✓ | | |
| prehistory | | ✓ | | |
| professional_accounting | | | | ✓ |
| professional_law | | ✓ | | |
| professional_medicine | | | | ✓ |
| professional_psychology | | | ✓ | |
| public_relations | | | ✓ | |
| security_studies | | | ✓ | |
| sociology | | | ✓ | |
| us_foreign_policy | | | ✓ | |
| virology | | | | ✓ |
| world_religions | | ✓ | | |
| - | - | - | - | - |
each item of the dataset is a dictionary with **Question, A, B, C, D, Answer** where A,B,C,D are options to the choose from.
here is three example from the abstract algebra subject.
| Question | A | B | C | D | Answer |
|---|---|---|---|---|---|
| مجموعة فرعية H من مجموعة (G،*) هي مجموعة إذا | 'a، b في H => a * b في H' | 'a في H => a^-1 في H' | 'a، b في H => a * b^-1 في H' | 'H يحتوي على العنصر المحدد' | C |
| 'ما هو ترتيب العنصر (4، 2) من Z_12 x Z_8' | 2 | 4 | 8 | 12 | C |
|ما هو الدرجة لتمديد الحقل المعطى Q(sqrt(2) + sqrt(3)) على Q| 0 | 4 | 2 | 6| B |
The size of each subject within the dataset
| Subject | Test Length | Eval Length |
|---|---|---|
| professional_law | 1534 | 5 |
| moral_scenarios | 895 | 5 |
| miscellaneous | 783 | 5 |
| professional_psychology | 612 | 5 |
| high_school_psychology | 545 | 5 |
| high_school_macroeconomics | 390 | 5 |
| elementary_mathematics | 378 | 5 |
| moral_disputes | 346 | 5 |
| prehistory | 324 | 5 |
| philosophy | 311 | 5 |
| high_school_biology | 310 | 5 |
| nutrition | 306 | 5 |
| professional_accounting | 282 | 5 |
| professional_medicine | 272 | 5 |
| high_school_mathematics | 270 | 5 |
| clinical_knowledge | 265 | 5 |
| security_studies | 245 | 5 |
| high_school_microeconomics | 238 | 5 |
| high_school_world_history | 237 | 5 |
| conceptual_physics | 235 | 5 |
| marketing | 234 | 5 |
| human_aging | 223 | 5 |
| high_school_statistics | 216 | 5 |
| high_school_us_history | 204 | 5 |
| high_school_chemistry | 203 | 5 |
| sociology | 201 | 5 |
| high_school_geography | 198 | 5 |
| high_school_government_and_politics | 193 | 5 |
| college_medicine | 173 | 5 |
| world_religions | 171 | 5 |
| virology | 166 | 5 |
| high_school_european_history | 165 | 5 |
| logical_fallacies | 163 | 5 |
| astronomy | 152 | 5 |
| high_school_physics | 151 | 5 |
| electrical_engineering | 145 | 5 |
| college_biology | 144 | 5 |
| anatomy | 135 | 5 |
| human_sexuality | 131 | 5 |
| formal_logic | 126 | 5 |
| international_law | 121 | 5 |
| econometrics | 114 | 5 |
| machine_learning | 112 | 5 |
| public_relations | 110 | 5 |
| jurisprudence | 108 | 5 |
| management | 103 | 5 |
| college_physics | 102 | 5 |
| abstract_algebra | 100 | 5 |
| business_ethics | 100 | 5 |
| college_chemistry | 100 | 5 |
| college_computer_science | 100 | 5 |
| college_mathematics | 100 | 5 |
| computer_security | 100 | 5 |
| global_facts | 100 | 5 |
| high_school_computer_science | 100 | 5 |
| medical_genetics | 100 | 5 |
| us_foreign_policy | 100 | 5 |
| count | 14042 | 285 | |
speechcolab/gigaspeech | speechcolab | "2023-11-23T14:08:34" | 391,722 | 86 | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:text-to-audio",
"multilinguality:monolingual",
"language:en",
"license:apache-2.0",
"size_categories:100K<n<1M",
"modality:audio",
"modality:text",
"library:datasets",
"library:mlcroissant",
"arxiv:2106.06909",
"region:us"
] | [
"automatic-speech-recognition",
"text-to-speech",
"text-to-audio"
] | "2022-06-09T14:51:58" | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: Gigaspeech
source_datasets: []
task_categories:
- automatic-speech-recognition
- text-to-speech
- text-to-audio
extra_gated_prompt: >-
SpeechColab does not own the copyright of the audio files. For researchers and
educators who wish to use the audio files for non-commercial research and/or
educational purposes, we can provide access through the Hub under certain
conditions and terms.
Terms of Access:
The "Researcher" has requested permission to use the GigaSpeech database (the
"Database") at Tsinghua University. In exchange for such permission,
Researcher hereby agrees to the following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and
educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or
warranties regarding the Database, including but not limited to warranties of
non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database
and shall defend and indemnify the SpeechColab team and Tsinghua University,
including their employees, Trustees, officers and agents, against any and all
claims arising from Researcher's use of the Database, including but not
limited to Researcher's use of any copies of copyrighted audio files that he
or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to
the Database provided that they first agree to be bound by these terms and
conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate
Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's
employer shall also be bound by these terms and conditions, and Researcher
hereby represents that he or she is fully authorized to enter into this
agreement on behalf of such employer.
!!! Please also fill out the Google Form https://forms.gle/UuGQAPyscGRrUMLq6
to request access to the Gigaspeech dataset.
extra_gated_fields:
Name: text
Email: text
Organization: text
Address: text
I hereby confirm that I have requested access via the Google Form provided above: checkbox
I accept the terms of access: checkbox
---
# Dataset Card for Gigaspeech
## 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)
- [Terms of Access](#terms-of-access)
## Dataset Description
- **Homepage:** https://github.com/SpeechColab/GigaSpeech
- **Repository:** https://github.com/SpeechColab/GigaSpeech
- **Paper:** https://arxiv.org/abs/2106.06909
- **Leaderboard:** https://github.com/SpeechColab/GigaSpeech#leaderboard
- **Point of Contact:** [[email protected]](mailto:[email protected])
## Dataset Description
GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality labeled audio suitable for supervised training. The transcribed audio data is collected from audiobooks, podcasts and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, sports, etc.
### Example Usage
The training split has several configurations of various size:
XS, S, M, L, XL. See the Section on "Data Splits" for more information. To download the XS configuration:
```python
from datasets import load_dataset
gs = load_dataset("speechcolab/gigaspeech", "xs", use_auth_token=True)
# see structure
print(gs)
# load audio sample on the fly
audio_input = gs["train"][0]["audio"] # first decoded audio sample
transcription = gs["train"][0]["text"] # first transcription
```
It is possible to download only the development or test data:
```python
gs_dev = load_dataset("speechcolab/gigaspeech", "dev", use_auth_token=True)
gs_test = load_dataset("speechcolab/gigaspeech", "test", use_auth_token=True)
```
### 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). The task has an active leaderboard which can be found at https://github.com/SpeechColab/GigaSpeech#leaderboard and ranks models based on their WER.
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
### Languages
Gigaspeech contains audio and transcription data in English.
## Dataset Structure
### Data Instances
```python
{
'segment_id': 'YOU0000000315_S0000660',
'speaker': 'N/A',
'text': "AS THEY'RE LEAVING <COMMA> CAN KASH PULL ZAHRA ASIDE REALLY QUICKLY <QUESTIONMARK>",
'audio':
{
# in streaming mode 'path' will be 'xs_chunks_0000/YOU0000000315_S0000660.wav'
'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/9d48cf31/xs_chunks_0000/YOU0000000315_S0000660.wav',
'array': array([0.0005188 , 0.00085449, 0.00012207, ..., 0.00125122, 0.00076294, 0.00036621], dtype=float32),
'sampling_rate': 16000
},
'begin_time': 2941.889892578125,
'end_time': 2945.070068359375,
'audio_id': 'YOU0000000315',
'title': 'Return to Vasselheim | Critical Role: VOX MACHINA | Episode 43',
'url': 'https://www.youtube.com/watch?v=zr2n1fLVasU',
'source': 2,
'category': 24,
'original_full_path': 'audio/youtube/P0004/YOU0000000315.opus'
}
```
### Data Fields
* segment_id (string) - string id of the segment.
* speaker (string) - string id of the speaker (can be "N/A").
* text (string) - transcription of the segment.
* begin_time (float) - start time of the segment in an original full audio.
* end_time (float32) - end time of the segment in an original full audio.
* audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate.
In non-streaming mode (default), the path point to the locally extracted audio. In streaming mode, the path is the relative path of an audio.
segment inside its archive (as files are not downloaded and extracted locally).
* audio_id (string) - string idea of the original full audio.
* title (string) - title of the original full audio.
* url (string) - url of the original full audio.
* source (ClassLabel) - id of the audio source. Sources are audiobook (0), podcast (1), and YouYube (2).
* category (ClassLabel) - id of the audio category, categories are listed below.
* original_full_path (string) - the relative path to the original full audio sample in the original data directory.
Categories are assigned from the following labels:
"People and Blogs", "Business", "Nonprofits and Activism", "Crime", "History", "Pets and Animals",
"News and Politics", "Travel and Events", "Kids and Family", "Leisure", "N/A", "Comedy", "News and Politics",
"Sports", "Arts", "Science and Technology", "Autos and Vehicles", "Science and Technology", "People and Blogs",
"Music", "Society and Culture", "Education", "Howto and Style", "Film and Animation", "Gaming", "Entertainment",
"Travel and Events", "Health and Fitness", "audiobook".
### Data Splits
The dataset has three splits: train, evaluation (dev) and test. The train split has five configurations of various sizes:
XS, S, M, L, XL. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.
#### Transcribed Training Subsets Size
| Subset | Hours | Remarks |
|:---------------:|:-------------:|:-------------|
| XS | 10 | System building and debugging |
| S | 250 | Quick research experiments |
| M | 1,000 | Large-scale research experiments |
| L | 2,500 | Medium-scale industrial experiments |
| XL | 10,000 | Large-scale industrial experiments |
#### Transcribed Evaluation Subsets
| Subset | Hours | Remarks |
|:------:|:-----:|:--------|
| Dev | 12 | Randomly selected from the crawled Podcast and YouTube Data |
| Test | 40 | Part of the subset was randomly selected from the crawled Podcast and YouTube data; part of it was manually collected through other channels to have better coverage. |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
| Audio Source | Transcribed Hours | Acoustic Condition |
|:-------------|:----------------------:|:-------------------|
| Audiobook | 2,655 | <li>Reading</li><li>Various ages and accents</li> |
| Podcast | 3,498 | <li>Clean or background music</li><li>Indoor</li><li>Near-field</li><li>Spontaneous</li><li>Various ages and accents</li>|
| YouTube | 3,845 | <li>Clean and noisy</li><li>Indoor and outdoor</li><li>Near- and far-field</li><li>Reading and spontaneous</li><li>Various ages and accents</li> |
| ***Total*** | ***10,000*** ||
#### 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?
Development and test subsets are annotated by professional human annotators.
### 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
SpeechColab does not own the copyright of the audio files. For researchers and educators who wish to use the audio files for
non-commercial research and/or educational purposes, we can provide access through our site under certain conditions and terms.
In general, when training a machine learning model on a given dataset, the license of the model is **independent** to that of the
dataset. That is to say, speech recognition models trained on the GigaSpeech dataset may be eligible for commercial license,
provided they abide to the 'Fair Use' terms of the underlying data and do not violate any explicit copyright restrictions.
This is likely to be true in most use-cases. However, it is your responsiblity to verify the appropriate model license for
your specific use-case by confirming that the dataset usage abides by the Fair Use terms. SpeechColab is not responsible
for the license of any machine learning model trained on the GigaSpeech dataset.
### Citation Information
Please cite this paper if you find this work useful:
```bibtext
@inproceedings{GigaSpeech2021,
title={GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio},
booktitle={Proc. Interspeech 2021},
year=2021,
author={Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan Trmal, Junbo Zhang, Mingjie Jin, Sanjeev Khudanpur, Shinji Watanabe, Shuaijiang Zhao, Wei Zou, Xiangang Li, Xuchen Yao, Yongqing Wang, Yujun Wang, Zhao You, Zhiyong Yan}
}
```
### Contributions
Thanks to [@polinaeterna](https://github.com/polinaeterna) and [@sanchit-gandhi](https://github.com/sanchit-gandhi)
for adding this dataset.
## Terms of Access
The "Researcher" has requested permission to use the GigaSpeech database (the "Database")
at Tsinghua University. In exchange for such permission, Researcher hereby agrees to the
following terms and conditions:
1. Researcher shall use the Database only for non-commercial research and educational purposes.
2. The SpeechColab team and Tsinghua University make no representations or warranties regarding the Database, including but not limited to warranties of non-infringement or fitness for a particular purpose.
3. Researcher accepts full responsibility for his or her use of the Database and shall defend and indemnify the SpeechColab team and Tsinghua University, including their employees, Trustees, officers and agents, against any and all claims arising from Researcher's use of the Database, including but not limited to Researcher's use of any copies of copyrighted audio files that he or she may create from the Database.
4. Researcher may provide research associates and colleagues with access to the Database provided that they first agree to be bound by these terms and conditions.
5. The SpeechColab team and Tsinghua University reserve the right to terminate Researcher's access to the Database at any time.
6. If Researcher is employed by a for-profit, commercial entity, Researcher's employer shall also be bound by these terms and conditions, and Researcher hereby represents that he or she is fully authorized to enter into this agreement on behalf of such employer. |
nlp-waseda/JMMLU | nlp-waseda | "2024-02-27T05:22:30" | 371,882 | 7 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"language:ja",
"license:cc-by-nc-nd-4.0",
"size_categories:1K<n<10K",
"arxiv:2009.03300",
"region:us",
"llm",
"evaluation",
"Japanese"
] | [
"multiple-choice",
"question-answering"
] | "2024-02-09T12:19:13" | ---
license: cc-by-nc-nd-4.0
task_categories:
- multiple-choice
- question-answering
language:
- ja
tags:
- llm
- evaluation
- Japanese
pretty_name: JMMLU
size_categories:
- 1K<n<10K
---
# JMMLU
Japanese Massive Multitask Language Understanding Benchmark
JMMLU is a four-choice question set consisting of Japanese-translated questions of a portion of MMLU ([Paper](https://arxiv.org/abs/2009.03300), [Github](https://github.com/hendrycks/test)) (Translated questions) and questions based on unique Japanese cultural context (Japanese questions). It is designed to assess the performance of large language models in Japanese.
For the translated questions, a maximum of 150 questions from each of the 57 MMLU tasks (subjects) were selected and first machine-translated into Japanese. Next, the translators checked the machine translations and removed questions and tasks that were difficult to translate, irrelevant, or inconsistent with the Japanese culture. The remaining questions were modified to make them fluent.
The Japanese questions are based on school subjects, such as Japanese civics and history, and are manually created by Japanese teachers.
The format is the same as MMLU:
```
Question, Choice A, Choice B, Choice C, Choice D, Answer
```
[Github](https://github.com/nlp-waseda/JMMLU)
The JMMLU consists of 7,536 questions in the following 56 tasks (subjects).
| Japanese Task Name | English Task Name | Number |
|---|---|---:|
| 専門医学 | professional_medicine | 150 |
| 専門心理学 | professional_psychology | 150 |
| 専門会計 | professional_accounting | 150 |
| 哲学 | philosophy | 150 |
| 雑学 | miscellaneous | 150 |
| 医学遺伝学 | medical_genetics | 99 |
| 形式論理 | formal_logic | 125 |
| 先史学 | prehistory | 150 |
| 天文学 | astronomy | 148 |
| 熟語 | japanese_idiom | 150 |
| 世界宗教 | world_religions | 147 |
| 世界事実 | global_facts | 97 |
| 世界史 | world_history | 150 |
| 社会学 | sociology | 150 |
| 栄養学 | nutrition | 149 |
| 日本史 | japanese_history | 150 |
| 日本地理 | japanese_geography | 139 |
| 人間の老化 | human_aging | 150 |
| 論理学 | logical_fallacies | 150 |
| 倫理的議論 | moral_disputes | 148 |
| 臨床知識 | clinical_knowledge | 150 |
| 経営学 | management | 102 |
| 解剖学 | anatomy | 132 |
| 計量経済学 | econometrics | 113 |
| 機械学習 | machine_learning | 111 |
| 国際法 | international_law | 120 |
| 公民 | japanese_civics | 150 |
| 公共関係 | public_relations | 109 |
| 高校心理学 | high_school_psychology | 150 |
| 高校物理 | high_school_physics | 150 |
| 高校統計学 | high_school_statistics | 150 |
| 高校数学 | high_school_mathematics | 150 |
| 高校生物学 | high_school_biology | 148 |
| 高校情報科学 | high_school_computer_science | 98 |
| 高校化学 | high_school_chemistry | 149 |
| 高校地理 | high_school_geography | 150 |
| 高校ヨーロッパ史 | high_school_european_history | 150 |
| 高校ミクロ経済学 | high_school_microeconomics | 149 |
| 高校マクロ経済学 | high_school_macroeconomics | 148 |
| 概念物理学 | conceptual_physics | 150 |
| 法理学 | jurisprudence | 107 |
| 電気工学 | electrical_engineering | 144 |
| 大学医学 | college_medicine | 150 |
| 大学物理 | college_physics | 100 |
| 大学数学 | college_mathematics | 99 |
| 大学生物学 | college_biology | 143 |
| 大学化学 | college_chemistry | 99 |
| 大学コンピュータ科学 | college_computer_science | 99 |
| 初等数学 | elementary_mathematics | 150 |
| 抽象代数 | abstract_algebra | 99 |
| マーケティング | marketing | 150 |
| ビジネス倫理 | business_ethics | 86 |
| セクシュアリティ | human_sexuality | 130 |
| セキュリティ研究 | security_studies | 150 |
| コンピュータセキュリティ | computer_security | 99 |
| ウイルス学 | virology | 150 |
The copyrights for Japanese and World History belongs to STEP Corporation. Commercial use other than for research and evaluation of language models is prohibited.
The copyrights for Japanese idioms, Japansese civics, and Japanese geography belong to New Style Cram School VIST. Commercial use is allowed only for research and evaluation of language models.
This work is licensed under CC BY-NC-ND 4.0
# Acknowledgment
We express our gratitude to the RIKEN for their support in the translation of MMLU. We also acknowledge the contributions from Step Corporation, who provided materials on Japanese and World History, and from New Style Cram School VIST, who supplied resources on japanese_idioms, japansese_civics, and japanese_geography. |
bigcode/humanevalpack | bigcode | "2024-05-01T20:18:20" | 353,557 | 65 | [
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"arxiv:2308.07124",
"region:us",
"code"
] | null | "2023-03-29T12:00:16" | ---
license: mit
pretty_name: HumanEvalPack
language_creators:
- expert-generated
multilinguality:
- multilingual
language:
- code
tags:
- code
---
![Octopack](https://github.com/bigcode-project/octopack/blob/31f3320f098703c7910e43492c39366eeea68d83/banner.png?raw=true)
# Dataset Card for HumanEvalPack
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [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)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/bigcode-project/octopack
- **Paper:** [OctoPack: Instruction Tuning Code Large Language Models](https://arxiv.org/abs/2308.07124)
- **Point of Contact:** [Niklas Muennighoff](mailto:[email protected])
### Dataset Summary
> HumanEvalPack is an extension of OpenAI's HumanEval to cover 6 total languages across 3 tasks. The Python split is exactly the same as OpenAI's Python HumanEval. The other splits are translated by humans (similar to HumanEval-X but with additional cleaning, see [here](https://github.com/bigcode-project/octopack/tree/main/evaluation/create/humaneval-x#modifications-muennighoff)). Refer to the [OctoPack paper](https://arxiv.org/abs/2308.07124) for more details.
>
- **Languages:** Python, JavaScript, Java, Go, C++, Rust
- **OctoPack🐙🎒:**
<table>
<tr>
<th>Data</t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpack>CommitPack</a></td>
<td>4TB of GitHub commits across 350 programming languages</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/datasets/bigcode/commitpackft>CommitPackFT</a></td>
<td>Filtered version of CommitPack for high-quality commit messages that resemble instructions</td>
</tr>
<tr>
<th>Model</t>
<td><a href=https://huggingface.co/bigcode/octocoder>OctoCoder</a></td>
<td>StarCoder (16B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th></t>
<td><a href=https://huggingface.co/bigcode/octogeex>OctoGeeX</a></td>
<td>CodeGeeX2 (6B parameters) instruction tuned on CommitPackFT + OASST</td>
</tr>
<tr>
<th>Evaluation</t>
<td><a href=https://huggingface.co/datasets/bigcode/humanevalpack>HumanEvalPack</a></td>
<td>Extension of OpenAI's HumanEval to cover 3 scenarios across 6 languages</td>
</tr>
</table>
## Usage
```python
# pip install -q datasets
from datasets import load_dataset
# Languages: "python", "js", "java", "go", "cpp", "rust"
ds = load_dataset("bigcode/humanevalpack", "python")["test"]
ds[0]
```
## Dataset Structure
### Data Instances
An example looks as follows:
```json
{
"task_id": "Python/0",
"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
"declaration": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n",
"canonical_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = abs(elem - elem2)\n if distance < threshold:\n return True\n\n return False\n",
"buggy_solution": " for idx, elem in enumerate(numbers):\n for idx2, elem2 in enumerate(numbers):\n if idx != idx2:\n distance = elem - elem2\n if distance < threshold:\n return True\n\n return False\n",
"bug_type": "missing logic",
"failure_symptoms": "incorrect output",
"entry_point": "has_close_elements",
"import": ""
"test_setup": ""
"test": "\n\n\n\n\ndef check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert has_close_elements([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n assert has_close_elements([1.0, 2.0, 5.9, 4.0, 5.0], 0.8) == False\n assert has_close_elements([1.0, 2.0, 3.0, 4.0, 5.0, 2.0], 0.1) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 1.0) == True\n assert has_close_elements([1.1, 2.2, 3.1, 4.1, 5.1], 0.5) == False\n\ncheck(has_close_elements)",
"example_test": "def check(has_close_elements):\n assert has_close_elements([1.0, 2.0, 3.0], 0.5) == False\n assert has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3) == True\ncheck(has_close_elements)\n",
"signature": "has_close_elements(numbers: List[float], threshold: float) -> bool",
"docstring": "Check if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue",
"instruction": "Write a Python function `has_close_elements(numbers: List[float], threshold: float) -> bool` to solve the following problem:\nCheck if in given list of numbers, are any two numbers closer to each other than\ngiven threshold.\n>>> has_close_elements([1.0, 2.0, 3.0], 0.5)\nFalse\n>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\nTrue"
}
```
### Data Fields
The data fields are the same among all splits:
- `task_id`: Indicates the language (Python/JavaScript/Java/Go/C++/Rust) and task id (from 0 to 163) of the problem
- `prompt`: the prompt for models relying on code continuation
- `declaration`: the declaration of the function (same as prompt but without the docstring)
- `canonical_solution`: the correct solution passing all unit tests for the problem
- `buggy_solution`: same as `canonical_solution` but with a subtle human-written bug causing the unit tests to fail
- `bug_type`: the type of the bug in `buggy_solution` (one of [`missing logic`, `excess logic`, `value misuse`, `operator misuse`, `variable misuse`, `function misuse`])
- `failure_symptoms`: the problem the bug causes (one of [`incorrect output`, `stackoverflow`, `infinite loop`])
- `entry_point`: the name of the function
- `import`: imports necessary for the solution (only present for Go)
- `test_setup`: imports necessary for the test execution (only present for Go)
- `test`: the unit tests for the problem
- `example_test`: additional unit tests different from `test` that could be e.g. provided to the model (these are not used in the paper)
- `signature`: the signature of the function
- `docstring`: the docstring describing the problem
- `instruction`: an instruction for HumanEvalSynthesize in the form `Write a {language_name} function {signature} to solve the following problem:\n{docstring}`
## Citation Information
```bibtex
@article{muennighoff2023octopack,
title={OctoPack: Instruction Tuning Code Large Language Models},
author={Niklas Muennighoff and Qian Liu and Armel Zebaze and Qinkai Zheng and Binyuan Hui and Terry Yue Zhuo and Swayam Singh and Xiangru Tang and Leandro von Werra and Shayne Longpre},
journal={arXiv preprint arXiv:2308.07124},
year={2023}
}
``` |
Dataset Card for Hugging Face Hub Dataset Cards
This datasets consists of dataset cards for models hosted on the Hugging Face Hub. The dataset cards are created by the community and provide information about datasets hosted on the Hugging Face Hub. This dataset is updated on a daily basis and includes publicly available datasets on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Dataset Cards from the Hub. We hope that this dataset will help support research in the area of Dataset Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in dataset cards
- analysis of the dataset card format/content
- topic modelling of dataset cards
- training language models on the dataset cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with dataset cards. In particular it was created to support research in the area of dataset cards and their use. It is possible to use the Hugging Face Hub API or client library to download dataset cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md
files for datasets hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the dataset directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the dataset cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the dataset card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the dataset card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of dataset cards to contain personal or sensitive information, it is possible that some dataset cards may contain this information. Dataset cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Dataset cards are created by the community and we do not have any control over the content of the dataset cards. We do not review the content of the dataset cards and we do not make any claims about the accuracy of the information in the dataset cards. Some dataset cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the dataset. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the dataset cards, some dataset cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
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