The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 4 missing columns ({'passage_offset', 'embedding_offset', 'num_passages', 'num_embeddings'}).
This happened while the json dataset builder was generating data using
hf://datasets/davanstrien/search-index/collection.json (at revision 4f5cb0d9fe2f68fa51e0d5d6ba16f6ae57ca04c2)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 192
to
{'passage_offset': Value(dtype='int64', id=None), 'num_passages': Value(dtype='int64', id=None), 'num_embeddings': Value(dtype='int64', id=None), 'embedding_offset': Value(dtype='int64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'text'}) and 4 missing columns ({'passage_offset', 'embedding_offset', 'num_passages', 'num_embeddings'}).
This happened while the json dataset builder was generating data using
hf://datasets/davanstrien/search-index/collection.json (at revision 4f5cb0d9fe2f68fa51e0d5d6ba16f6ae57ca04c2)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
passage_offset
int64 | num_passages
int64 | num_embeddings
int64 | embedding_offset
int64 | text
string |
|---|---|---|---|---|
0 | 25,000 | 3,684,220 | 0 | null |
25,000 | 11,905 | 1,794,006 | 3,684,220 | null |
null | null | null | null | ## 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) - |
null | null | null | null | - [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). |
null | null | null | null | 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', |
null | null | null | null | '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', |
null | null | null | null | '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. |
null | null | null | null | 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
### Data Instances
An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone? |
null | null | null | null | 0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
### 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. |
null | null | null | null | - `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
### 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. |
null | null | null | null | 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.
### 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. |
null | null | null | null | ## 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. |
null | null | null | null | 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') ``` |
null | null | null | null | ### 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. |
null | null | null | null | 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} } ``` |
null | null | null | null | ## 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
- |
null | null | null | null | 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:** - **Paper:** [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) - **Point of Contact:** - **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. |
null | null | null | null | 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.
#### 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. |
null | null | null | null | ", "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?" |
null | null | null | null | 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. |
null | null | null | null | - `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|
### 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? |
null | null | null | null | 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. |
null | null | null | null | ## 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. |
null | null | null | null | ## 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. |
null | null | null | null | 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", |
null | null | null | null | 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', |
null | null | null | null | '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', |
null | null | null | null | '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/). |
null | null | null | null | ## 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. |
null | null | null | null | 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. |
null | null | null | null | 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.
### 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. |
null | null | null | null | 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. ``` |
null | null | null | null | ### KMMLU Statistics
| Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question Type** | | | Positive | 207,030 | | Negation | 36,777 | | **Split** | | | Train | 208,522 | | Validation | 225 | | Test | 35,030 | | **Total** | 243,777 |
### Categories
To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", |
null | null | null | null | "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", |
null | null | null | null | "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ```
### Point of Contact
For any questions contact us via the following email:) ``` [email protected] ``` |
null | null | null | null | ## 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) - |
null | null | null | null | - [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). |
null | null | null | null | 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', |
null | null | null | null | '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', |
null | null | null | null | '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. |
null | null | null | null | 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
### Data Instances
An example from anatomy subtask looks as follows: ``` { "question": "What is the embryological origin of the hyoid bone? |
null | null | null | null | 0 | 25.0 | 25.0 | 25.0 | 25.0 | 25.0
### Languages
English
### 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. |
null | null | null | null | - `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
### 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. |
null | null | null | null | 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.
### 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. |
null | null | null | null | ## 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) - |
null | null | null | null | - [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. |
null | null | null | null | 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. |
null | null | null | null | ### 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). |
null | null | null | null | 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.
### 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', |
null | null | null | null | 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. |
null | null | null | null | '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. |
null | null | null | null | 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. |
null | null | null | null | 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. |
null | null | null | null | ### 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. |
null | null | null | null | |
### 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*** ||
#### Who are the annotators?
Development and test subsets are annotated by professional human annotators.
### 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. |
null | null | null | null | Development and test subsets are annotated by professional human annotators.
### 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. |
null | null | null | null | 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. |
null | null | null | null | ### 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. |
null | null | null | null | ## 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. |
null | null | null | null | 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. |
null | null | null | null | ## 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. |
null | null | null | null | ## 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. |
null | null | null | null | - `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} } ``` |
null | null | null | null | ## Table of Contents
- [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [How to use](#how-to-use) - [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) - |
null | null | null | null | - [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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94) - **Repository:** - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411) - **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
### Dataset Summary
This is a streamable version of the **English version of the Multilingual LibriSpeech (MLS) dataset**. |
null | null | null | null | The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream. MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages. This dataset card includes the 44.5K hours of English. Refers to this [dataset card](https://huggingface.co/datasets/facebook/multilingual_librispeech) for the other languages.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `speaker-identification`: 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). |
null | null | null | null | 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://paperswithcode.com/dataset/multilingual-librispeech 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).
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function. For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German): ```python from datasets import load_dataset mls = load_dataset("parler-tts/mls_eng", split="train") ``` Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. |
null | null | null | null | Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk. ```python from datasets import load_dataset mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True) print(next(iter(mls))) ``` *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed). Local: ```python from datasets import load_dataset from torch.utils.data.sampler import BatchSampler, RandomSampler mls = load_dataset("parler-tts/mls_eng", split="train") batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False) dataloader = DataLoader(mls, batch_sampler=batch_sampler) ``` Streaming: ```python from datasets import load_dataset from torch.utils.data import DataLoader mls = load_dataset("parler-tts/mls_eng", split="train", streaming=True) dataloader = DataLoader(mls, batch_size=32) ``` To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets). |
null | null | null | null | ### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
### Data Fields
- file: A filename .flac format. - audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription. |
null | null | null | null | - text: the transcription of the audio file. - id: unique id of the data sample. - speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples. - chapter_id: id of the audiobook chapter which includes the transcription.
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
``` @article{Pratap2020MLSAL, title={MLS: A Large-Scale Multilingual Dataset for Speech Research}, author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert}, journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ```
### Data Statistics
| Duration (h) | Train | Dev | Test | |--------------|-----------|-------|-------| | English | 44,659. |
null | null | null | null | journal={ArXiv}, year={2020}, volume={abs/2012.03411} } ```
### Data Statistics
| Duration (h) | Train | Dev | Test | |--------------|-----------|-------|-------| | English | 44,659.74 | 15.75 | 15.55 | | German | 1,966.51 | 14.28 | 14.29 | | Dutch | 1,554.24 | 12.76 | 12.76 | | French | 1,076.58 | 10.07 | 10.07 | | Spanish | 917.68 | 9.99 | 10 | | Italian | 247.38 | 5.18 | 5.27 | | Portuguese | 160.96 | 3.64 | 3.74 | | Polish | 103.65 | 2.08 | 2. |
null | null | null | null | 68 | 9.99 | 10 | | Italian | 247.38 | 5.18 | 5.27 | | Portuguese | 160.96 | 3.64 | 3.74 | | Polish | 103.65 | 2.08 | 2.14 | | # Speakers | Train | | Dev | | Test | | |------------|-------|------|-----|----|------|----| | Gender | M | F | M | F | M | F | | English | 2742 | 2748 | 21 | 21 | 21 | 21 | | German | 81 | 95 | 15 | 15 | 15 | 15 | | Dutch | 9 | 31 | 3 | 3 | 3 | 3 | | French | 62 | 80 | 9 | 9 | 9 | 9 | | Spanish | 36 | 50 | 10 | 10 | 10 | 10 | | Italian | 22 | 43 | 5 | 5 | 5 | 5 | | Portuguese | 26 | 16 | 5 | 5 | 5 | 5 | | Polish | 6 | 5 | 2 | 2 | 2 | 2 | | # Hours / Gender | Dev | | Test | | |------------------|------|------|------|------| | Gender | M | F | M | F | | English | 7. |
null | null | null | null | 76 | 7.99 | 7.62 | 7.93 | | German | 7.06 | 7.22 | 7 | 7.29 | | Dutch | 6.44 | 6.32 | 6.72 | 6.04 | | French | 5.13 | 4.94 | 5.04 | 5.02 | | Spanish | 4.91 | 5.08 | 4.78 | 5.23 | | Italian | 2.5 | 2.68 | 2.38 | 2.9 | | Portuguese | 1.84 | 1.81 | 1.83 | 1.9 | | Polish | 1.12 | 0.95 | 1.09 | 1.05 | |
null | null | null | null | ## 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) - |
null | null | null | null | - [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://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.
### 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. |
null | null | null | null | ### 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. |
null | null | null | null | ', '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`: The problem's difficulty level from 'Level 1' to 'Level 5', where a subject's easiest problems for humans are assigned to 'Level 1' and a subject's hardest problems are assigned to 'Level 5'. * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus. |
null | null | null | null | * `type`: The subject of the problem: Algebra, Counting & Probability, Geometry, Intermediate Algebra, Number Theory, Prealgebra and Precalculus.
### Data Splits
* train: 7,500 examples * test: 5,000 examples
### 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} } ```
### Contributions
Thanks to [@hacobe](https://github.com/hacobe) for adding this dataset. |
null | null | null | null | ### KMMLU Statistics
| Category | # Questions | |------------------------------|-------------| | **Prerequisites** | | | None | 59,909 | | 1 Prerequisite Test | 12,316 | | 2 Prerequisite Tests | 776 | | 2+ Years of Experience | 65,135 | | 4+ Years of Experience | 98,678 | | 9+ Years of Experience | 6,963 | | **Question Type** | | | Positive | 207,030 | | Negation | 36,777 | | **Split** | | | Train | 208,522 | | Validation | 225 | | Test | 35,030 | | **Total** | 243,777 |
### Categories
To reimplement the categories in the paper, refer to the following: ``` supercategories = { "accounting": "HUMSS", "agricultural_sciences": "Other", "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", |
null | null | null | null | "aviation_engineering_and_maintenance": "Applied Science", "biology": "STEM", "chemical_engineering": "STEM", "chemistry": "STEM", "civil_engineering": "STEM", "computer_science": "STEM", "construction": "Other", "criminal_law": "HUMSS", "ecology": "STEM", "economics": "HUMSS", "education": "HUMSS", "electrical_engineering": "STEM", "electronics_engineering": "Applied Science", "energy_management": "Applied Science", "environmental_science": "Applied Science", "fashion": "Other", "food_processing": "Other", "gas_technology_and_engineering": "Applied Science", "geomatics": "Applied Science", "health": "Other", "industrial_engineer": "Applied Science", "information_technology": "STEM", "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", |
null | null | null | null | "interior_architecture_and_design": "Other", "law": "HUMSS", "machine_design_and_manufacturing": "Applied Science", "management": "HUMSS", "maritime_engineering": "Applied Science", "marketing": "Other", "materials_engineering": "STEM", "mechanical_engineering": "STEM", "nondestructive_testing": "Applied Science", "patent": "Other", "political_science_and_sociology": "HUMSS", "psychology": "HUMSS", "public_safety": "Other", "railway_and_automotive_engineering": "Applied Science", "real_estate": "Other", "refrigerating_machinery": "Other", "social_welfare": "HUMSS", "taxation": "HUMSS", "telecommunications_and_wireless_technology": "Applied Science", "korean_history": "HUMSS", "math": "STEM" } ```
### Point of Contact
For any questions contact us via the following email:) ``` [email protected] ``` |
null | null | null | null | ## 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).
### 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 $C$. |
null | null | null | null | 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. |
null | null | null | null | ```
### 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} } ``` |
null | null | null | null | ## Dataset Description
- **Homepage:** https://nuprl.github.io/MultiPL-E/ - **Repository:** https://github.com/nuprl/MultiPL-E - **Paper:** https://ieeexplore.ieee.org/abstract/document/10103177 - **Point of Contact:** [email protected], [email protected], [email protected]
## Dataset Summary
MultiPL-E is a dataset for evaluating large language models for code generation that supports 22 programming languages. It takes the OpenAI HumanEval and the Mostly Basic Python Programs (MBPP) benchmarks and uses little compilers to translate them to other languages. It is easy to add support for new languages and benchmarks. The dataset is divided into several configurations named *SRCDATA-LANG*, where *SRCDATA* is either "humaneval" or "mbpp" and *LANG* is one of the supported languages. We use the canonical file extension for each language to identify the language, e.g., "cpp" for C++, "lua" for Lua, "clj" for Clojure, and so on. |
null | null | null | null | We use the canonical file extension for each language to identify the language, e.g., "cpp" for C++, "lua" for Lua, "clj" for Clojure, and so on.
## Using MultiPL-E
- MultiPL-E is part of the [BigCode Code Generation LM Harness]. This is the easiest way to use MultiPL-E. - MultiPL-E has its own evaluation framework that supports proprietary models, the prompt ablations, more source benchmarks, and more recently added programming languages. See the [MultiPL-E tutorial] on how to use this framework directly.
## The MultiPL-E Ablations
The MultiPL-E paper presented several ablations of the prompt for the original set of programming languages. We do not include them in the current version of MultiPL-E, but they are still available in this repository from revision `d23b094` or earlier. (You can optionally pass the revision to `datasets.load_dataset`.) These are the prompt variations: - *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt is totally unchanged. |
null | null | null | null | (You can optionally pass the revision to `datasets.load_dataset`.) These are the prompt variations: - *SRCDATA-LANG-keep* is the same as *SRCDATA-LANG*, but the text of the prompt is totally unchanged. If the original prompt had Python doctests, they remain as Python instead of being translated to *LANG*. If the original prompt had Python-specific terminology, e.g., "list", it remains "list", instead of being translated, e.g., to "vector" for C++. - *SRCDATA-LANG-transform* transforms the doctests to *LANG* but leaves the natural language text of the prompt unchanged. - *SRCDATA-LANG-removed* removes the doctests from the prompt. Note that MBPP does not have any doctests, so the "removed" and "transform" variations are not available for MBPP.
### Version 3.0
This is the first significant update since MultiPL-E was used in StarCoder 1. 1. We no longer publish the MultiPL-E ablations, but they are available in revision `d23b094` and earlier. 2. |
null | null | null | null | ### Version 3.0
This is the first significant update since MultiPL-E was used in StarCoder 1. 1. We no longer publish the MultiPL-E ablations, but they are available in revision `d23b094` and earlier. 2. New programming languages supported: - Clojure, thanks to [Alex Miller](https://github.com/puredanger) - Elixir, thanks to [Marko Vukovic](https://github.com/mvkvc) - Haskell, thanks to [Thomas Dwyer](https://github.com/Cajunvoodoo) - OCaml, thanks to [John Gouwar](https://johngouwar.github.io) 3. Changes to existing HumanEval-based problems: - Four Scala problems have fixed prompts/tests (12, 90, 128, 162). - Some whitespace-only changes to problems for Racket (18 problems), R (36 problems), Julia (159 problems), and D (156 problems). We will try to avoid these kinds of changes in the future. 1. The MBPP-based problems have changes analogous to the HumanEval-based problems. |
null | null | null | null | - Some whitespace-only changes to problems for Racket (18 problems), R (36 problems), Julia (159 problems), and D (156 problems). We will try to avoid these kinds of changes in the future. 1. The MBPP-based problems have changes analogous to the HumanEval-based problems. See the directory `diffs_v3.0` in the dataset repository for the diffs to each prompt. [BigCode Code Generation LM Harness]: https://github.com/bigcode-project/bigcode-evaluation-harness [MultiPL-E tutorial]: https://nuprl.github.io/MultiPL-E/ |
null | null | null | null | ## 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:n.muennighoff@gmail. |
null | null | null | null | 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. |
null | null | null | null | > - **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. |
null | null | null | null | 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", |
null | null | null | null | "js", "java", "go", "cpp", "rust"
ds = load_dataset("bigcode/humanevalpack", "python")["test"] ds[0] ```
### 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, |
null | null | null | null | 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, |
null | null | null | null | 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, |
null | null | null | null | 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, |
null | null | null | null | 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. |
null | null | null | null | 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`, |
null | null | null | null | `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. |
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