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hails/mmlu_no_train | hails | "2024-01-22T20:46:30" | 32,117,166 | 21 | [
"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
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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" | 2,508,267 | 33 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
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"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | [
"question-answering"
] | "2023-05-16T09:39:28" | ---
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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.
|
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dataset_size: 162590
- config_name: tracking_shuffled_objects_seven_objects
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 207274
num_examples: 250
download_size: 49062
dataset_size: 207274
- config_name: tracking_shuffled_objects_three_objects
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
dataset_size: 47582
- config_name: word_sorting
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 60918
num_examples: 250
download_size: 44584
dataset_size: 60918
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-*
---
|
argilla/databricks-dolly-15k-curated-en | argilla | "2023-10-02T12:32:53" | 2,059,222 | 43 | [
"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')
``` |
lavita/medical-qa-shared-task-v1-toy | lavita | "2023-07-20T00:29:06" | 1,564,472 | 16 | [
"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) |
lighteval/MATH-Hard | lighteval | "2024-06-12T13:00:08" | 843,842 | 12 | [
"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}
}
```
|
ceval/ceval-exam | ceval | "2023-08-31T14:04:10" | 833,445 | 238 | [
"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}
}
```
|
Hennara/ammlu | Hennara | "2024-03-02T17:20:25" | 684,122 | 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 | |
nlp-waseda/JMMLU | nlp-waseda | "2024-02-27T05:22:30" | 639,696 | 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. |
lukaemon/bbh | lukaemon | "2023-02-02T01:14:46" | 606,724 | 43 | [
"size_categories:1K<n<10K",
"modality:text",
"library:datasets",
"library:mlcroissant",
"region:us"
] | null | "2023-02-01T07:46:51" | ---
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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}
}
``` |
EleutherAI/hendrycks_math | EleutherAI | "2023-11-02T14:48:57" | 599,879 | 8 | [
"license:mit",
"region:us"
] | null | "2023-09-14T20:28:56" | ---
license: mit
--- |
nyu-mll/glue | nyu-mll | "2024-01-30T07:41:18" | 568,554 | 360 | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"task_ids:sentiment-classification",
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"arxiv:1804.07461",
"region:us",
"qa-nli",
"coreference-nli",
"paraphrase-identification"
] | [
"text-classification"
] | "2022-03-02T23:29:22" | ---
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paperswithcode_id: glue
pretty_name: GLUE (General Language Understanding Evaluation benchmark)
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path: wnli/validation-*
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path: wnli/test-*
train-eval-index:
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task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
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task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence: text
label: target
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task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
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task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question1: text1
question2: text2
label: target
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task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
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sentence1: text1
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label: target
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task: text-classification
task_id: natural_language_inference
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eval_split: validation_matched
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premise: text1
hypothesis: text2
label: target
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task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
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task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
premise: text1
hypothesis: text2
label: target
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task: text-classification
task_id: natural_language_inference
splits:
train_split: train
eval_split: validation
col_mapping:
question: text1
sentence: text2
label: target
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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. |
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|
allenai/ai2_arc | allenai | "2023-12-21T15:09:48" | 532,350 | 131 | [
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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. |
lmms-lab/MMMU | lmms-lab | "2024-03-08T05:09:42" | 473,612 | 4 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-01-15T06:32:16" | ---
dataset_info:
features:
- name: id
dtype: string
- name: question
dtype: string
- name: options
dtype: string
- name: explanation
dtype: string
- name: image_1
dtype: image
- name: image_2
dtype: image
- name: image_3
dtype: image
- name: image_4
dtype: image
- name: image_5
dtype: image
- name: image_6
dtype: image
- name: image_7
dtype: image
- name: img_type
dtype: string
- name: answer
dtype: string
- name: topic_difficulty
dtype: string
- name: question_type
dtype: string
- name: subfield
dtype: string
splits:
- name: dev
num_bytes: 57719107.0
num_examples: 150
- name: validation
num_bytes: 347519954.0
num_examples: 900
- name: test
num_bytes: 3271046267.0
num_examples: 10500
download_size: 3377778136
dataset_size: 3676285328.0
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
This is a merged version of [MMMU/MMMU](https://huggingface.co/datasets/MMMU/MMMU) with all subsets concatenated.
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of [MMMU](https://github.com/MMMU-Benchmark/MMMU). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{yue2023mmmu,
title={Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi},
author={Yue, Xiang and Ni, Yuansheng and Zhang, Kai and Zheng, Tianyu and Liu, Ruoqi and Zhang, Ge and Stevens, Samuel and Jiang, Dongfu and Ren, Weiming and Sun, Yuxuan and others},
journal={arXiv preprint arXiv:2311.16502},
year={2023}
}
``` |
haonan-li/cmmlu | haonan-li | "2023-07-13T10:19:29" | 472,313 | 60 | [
"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/).
|
bigcode/humanevalpack | bigcode | "2024-05-01T20:18:20" | 432,468 | 68 | [
"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}
}
``` |
lmms-lab/LMMs-Eval-Lite | lmms-lab | "2024-07-04T04:16:56" | 429,974 | 2 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-06-27T03:29:05" | ---
dataset_info:
- config_name: ai2d
features:
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
- name: image
dtype: image
splits:
- name: lite
num_bytes: 90543302.1658031
num_examples: 500
download_size: 81458737
dataset_size: 90543302.1658031
- config_name: chartqa
features:
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features:
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- config_name: flickr30k_test
features:
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dtype: image
- name: caption
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- name: sentids
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- name: img_id
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struct:
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list:
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- config_name: infovqa_val
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- config_name: mmbench_cn_dev
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- config_name: mmbench_en_dev
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- config_name: nocaps_val
features:
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configs:
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data_files:
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path: ai2d/lite-*
- config_name: chartqa
data_files:
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path: chartqa/lite-*
- config_name: coco2017_cap_val
data_files:
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path: coco2017_cap_val/lite-*
- config_name: docvqa_val
data_files:
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path: docvqa_val/lite-*
- config_name: flickr30k_test
data_files:
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path: flickr30k_test/lite-*
- config_name: gqa
data_files:
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path: gqa/lite-*
- config_name: infovqa_val
data_files:
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path: infovqa_val/lite-*
- config_name: mmbench_cn_dev
data_files:
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path: mmbench_cn_dev/lite-*
- config_name: mmbench_en_dev
data_files:
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path: mmbench_en_dev/lite-*
- config_name: nocaps_val
data_files:
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path: nocaps_val/lite-*
- config_name: ok_vqa_val2014
data_files:
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path: ok_vqa_val2014/lite-*
- config_name: refcoco_bbox_val
data_files:
- split: lite
path: refcoco_bbox_val/lite-*
- config_name: seedbench
data_files:
- split: lite
path: seedbench/lite-*
- config_name: textcaps_val
data_files:
- split: lite
path: textcaps_val/lite-*
- config_name: textvqa_val
data_files:
- split: lite
path: textvqa_val/lite-*
- config_name: vizwiz_vqa_val
data_files:
- split: lite
path: vizwiz_vqa_val/lite-*
- config_name: vqav2_val
data_files:
- split: lite
path: vqav2_val/lite-*
---
|
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.
Dataset Card Authors
Dataset Card Contact
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