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datasetId
string | author
string | last_modified
unknown | downloads
int64 | likes
int64 | tags
sequence | task_categories
sequence | createdAt
unknown | card
string |
---|---|---|---|---|---|---|---|---|
hails/mmlu_no_train | hails | "2024-01-22T20:46:30" | 13,534,058 | 8 | [
"task_categories:question-answering",
"language:en",
"license:mit",
"region:us"
] | [
"question-answering"
] | "2023-10-31T17:25:54" | ---
language:
- en
license: mit
task_categories:
- question-answering
pretty_name: MMLU loader with no auxiliary train set
dataset_info:
config_name: all
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
splits:
- name: test
num_bytes: 6967453
num_examples: 14042
- name: validation
num_bytes: 763484
num_examples: 1531
- name: dev
num_bytes: 125353
num_examples: 285
download_size: 3987384
dataset_size: 7856290
configs:
- config_name: all
data_files:
- split: test
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. |
argilla/databricks-dolly-15k-curated-en | argilla | "2023-10-02T12:32:53" | 4,984,114 | 34 | [
"language:en",
"croissant",
"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')
``` |
lighteval/mmlu | lighteval | "2023-06-09T16:36:19" | 2,180,815 | 14 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"croissant",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | [
"question-answering"
] | "2023-05-16T09:39:28" | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
language_bcp47:
- en-US
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dtype: string
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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.
|
yelp_review_full | null | "2024-01-04T17:14:53" | 2,150,991 | 81 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"croissant",
"arxiv:1509.01626",
"region:us"
] | [
"text-classification"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: YelpReviewFull
license_details: yelp-licence
dataset_info:
config_name: yelp_review_full
features:
- name: label
dtype:
class_label:
names:
'0': 1 star
'1': 2 star
'2': 3 stars
'3': 4 stars
'4': 5 stars
- name: text
dtype: string
splits:
- name: train
num_bytes: 483811554
num_examples: 650000
- name: test
num_bytes: 37271188
num_examples: 50000
download_size: 322952369
dataset_size: 521082742
configs:
- config_name: yelp_review_full
data_files:
- split: train
path: yelp_review_full/train-*
- split: test
path: yelp_review_full/test-*
default: true
train-eval-index:
- config: yelp_review_full
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
---
# Dataset Card for YelpReviewFull
## 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:** [Yelp](https://www.yelp.com/dataset)
- **Repository:** [Crepe](https://github.com/zhangxiangxiao/Crepe)
- **Paper:** [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
### Dataset Summary
The Yelp reviews dataset consists of reviews from Yelp.
It is extracted from the Yelp Dataset Challenge 2015 data.
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the text, predict the sentiment.
### Languages
The reviews were mainly written in english.
## Dataset Structure
### Data Instances
A typical data point, comprises of a text and the corresponding label.
An example from the YelpReviewFull test set looks as follows:
```
{
'label': 0,
'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!'
}
```
### Data Fields
- 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
- 'label': Corresponds to the score associated with the review (between 1 and 5).
### Data Splits
The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5.
In total there are 650,000 trainig samples and 50,000 testing samples.
## Dataset Creation
### Curation Rationale
The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
### 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
You can check the official [yelp-dataset-agreement](https://s3-media3.fl.yelpcdn.com/assets/srv0/engineering_pages/bea5c1e92bf3/assets/vendor/yelp-dataset-agreement.pdf).
### Citation Information
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
### Contributions
Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset. |
lavita/medical-qa-shared-task-v1-toy | lavita | "2023-07-20T00:29:06" | 1,542,778 | 7 | [
"croissant",
"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) |
wikitext | null | "2024-01-04T16:49:18" | 1,291,552 | 303 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"license:gfdl",
"croissant",
"arxiv:1609.07843",
"region:us"
] | [
"text-generation",
"fill-mask"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: wikitext-2
pretty_name: WikiText
dataset_info:
- config_name: wikitext-103-raw-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1305088
num_examples: 4358
- name: train
num_bytes: 546500949
num_examples: 1801350
- name: validation
num_bytes: 1159288
num_examples: 3760
download_size: 315466397
dataset_size: 548965325
- config_name: wikitext-103-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1295575
num_examples: 4358
- name: train
num_bytes: 545141915
num_examples: 1801350
- name: validation
num_bytes: 1154751
num_examples: 3760
download_size: 313093838
dataset_size: 547592241
- config_name: wikitext-2-raw-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1305088
num_examples: 4358
- name: train
num_bytes: 11061717
num_examples: 36718
- name: validation
num_bytes: 1159288
num_examples: 3760
download_size: 7747362
dataset_size: 13526093
- config_name: wikitext-2-v1
features:
- name: text
dtype: string
splits:
- name: test
num_bytes: 1270947
num_examples: 4358
- name: train
num_bytes: 10918118
num_examples: 36718
- name: validation
num_bytes: 1134123
num_examples: 3760
download_size: 7371282
dataset_size: 13323188
configs:
- config_name: wikitext-103-raw-v1
data_files:
- split: test
path: wikitext-103-raw-v1/test-*
- split: train
path: wikitext-103-raw-v1/train-*
- split: validation
path: wikitext-103-raw-v1/validation-*
- config_name: wikitext-103-v1
data_files:
- split: test
path: wikitext-103-v1/test-*
- split: train
path: wikitext-103-v1/train-*
- split: validation
path: wikitext-103-v1/validation-*
- config_name: wikitext-2-raw-v1
data_files:
- split: test
path: wikitext-2-raw-v1/test-*
- split: train
path: wikitext-2-raw-v1/train-*
- split: validation
path: wikitext-2-raw-v1/validation-*
- config_name: wikitext-2-v1
data_files:
- split: test
path: wikitext-2-v1/test-*
- split: train
path: wikitext-2-v1/train-*
- split: validation
path: wikitext-2-v1/validation-*
---
# Dataset Card for "wikitext"
## 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://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Pointer Sentinel Mixture Models](https://arxiv.org/abs/1609.07843)
- **Point of Contact:** [Stephen Merity](mailto:smerity@salesforce.com)
- **Size of downloaded dataset files:** 391.41 MB
- **Size of the generated dataset:** 1.12 GB
- **Total amount of disk used:** 1.52 GB
### Dataset Summary
The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over
110 times larger. The WikiText dataset also features a far larger vocabulary and retains the original case, punctuation
and numbers - all of which are removed in PTB. As it is composed of full articles, the dataset is well suited for models
that can take advantage of long term dependencies.
Each subset comes in two different variants:
- Raw (for character level work) contain the raw tokens, before the addition of the <unk> (unknown) tokens.
- Non-raw (for word level work) contain only the tokens in their vocabulary (wiki.train.tokens, wiki.valid.tokens, and wiki.test.tokens).
The out-of-vocabulary tokens have been replaced with the the <unk> token.
### 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
#### wikitext-103-raw-v1
- **Size of downloaded dataset files:** 191.98 MB
- **Size of the generated dataset:** 549.42 MB
- **Total amount of disk used:** 741.41 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" The gold dollar or gold one @-@ dollar piece was a coin struck as a regular issue by the United States Bureau of the Mint from..."
}
```
#### wikitext-103-v1
- **Size of downloaded dataset files:** 190.23 MB
- **Size of the generated dataset:** 548.05 MB
- **Total amount of disk used:** 738.27 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..."
}
```
#### wikitext-2-raw-v1
- **Size of downloaded dataset files:** 4.72 MB
- **Size of the generated dataset:** 13.54 MB
- **Total amount of disk used:** 18.26 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" The Sinclair Scientific Programmable was introduced in 1975 , with the same case as the Sinclair Oxford . It was larger than t..."
}
```
#### wikitext-2-v1
- **Size of downloaded dataset files:** 4.48 MB
- **Size of the generated dataset:** 13.34 MB
- **Total amount of disk used:** 17.82 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "\" Senjō no Valkyria 3 : <unk> Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to..."
}
```
### Data Fields
The data fields are the same among all splits.
#### wikitext-103-raw-v1
- `text`: a `string` feature.
#### wikitext-103-v1
- `text`: a `string` feature.
#### wikitext-2-raw-v1
- `text`: a `string` feature.
#### wikitext-2-v1
- `text`: a `string` feature.
### Data Splits
| name | train |validation|test|
|-------------------|------:|---------:|---:|
|wikitext-103-raw-v1|1801350| 3760|4358|
|wikitext-103-v1 |1801350| 3760|4358|
|wikitext-2-raw-v1 | 36718| 3760|4358|
|wikitext-2-v1 | 36718| 3760|4358|
## 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 dataset is available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@misc{merity2016pointer,
title={Pointer Sentinel Mixture Models},
author={Stephen Merity and Caiming Xiong and James Bradbury and Richard Socher},
year={2016},
eprint={1609.07843},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
ceval/ceval-exam | ceval | "2023-08-31T14:04:10" | 1,088,767 | 211 | [
"task_categories:text-classification",
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-sa-4.0",
"croissant",
"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}
}
```
|
bigcode/humanevalpack | bigcode | "2024-05-01T20:18:20" | 854,748 | 53 | [
"language_creators:expert-generated",
"multilinguality:multilingual",
"language:code",
"license:mit",
"code",
"arxiv:2308.07124",
"region:us"
] | 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:n.muennighoff@gmail.com)
### 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}
}
``` |
cais/mmlu | cais | "2024-03-08T20:36:26" | 765,739 | 245 | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"croissant",
"arxiv:2009.03300",
"arxiv:2005.00700",
"arxiv:2005.14165",
"arxiv:2008.02275",
"region:us"
] | [
"question-answering"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mmlu
pretty_name: Measuring Massive Multitask Language Understanding
language_bcp47:
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configs:
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data_files:
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path: professional_medicine/validation-*
- split: dev
path: professional_medicine/dev-*
- config_name: professional_psychology
data_files:
- split: test
path: professional_psychology/test-*
- split: validation
path: professional_psychology/validation-*
- split: dev
path: professional_psychology/dev-*
- config_name: public_relations
data_files:
- split: test
path: public_relations/test-*
- split: validation
path: public_relations/validation-*
- split: dev
path: public_relations/dev-*
- config_name: security_studies
data_files:
- split: test
path: security_studies/test-*
- split: validation
path: security_studies/validation-*
- split: dev
path: security_studies/dev-*
- config_name: sociology
data_files:
- split: test
path: sociology/test-*
- split: validation
path: sociology/validation-*
- split: dev
path: sociology/dev-*
- config_name: us_foreign_policy
data_files:
- split: test
path: us_foreign_policy/test-*
- split: validation
path: us_foreign_policy/validation-*
- split: dev
path: us_foreign_policy/dev-*
- config_name: virology
data_files:
- split: test
path: virology/test-*
- split: validation
path: virology/validation-*
- split: dev
path: virology/dev-*
- config_name: world_religions
data_files:
- split: test
path: world_religions/test-*
- split: validation
path: world_religions/validation-*
- split: dev
path: world_religions/dev-*
---
# 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.
|
allenai/ai2_arc | allenai | "2023-12-21T15:09:48" | 629,007 | 88 | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:multiple-choice-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"croissant",
"arxiv:1803.05457",
"region:us"
] | [
"question-answering"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- multiple-choice-qa
pretty_name: Ai2Arc
language_bcp47:
- en-US
dataset_info:
- config_name: ARC-Challenge
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 349760
num_examples: 1119
- name: test
num_bytes: 375511
num_examples: 1172
- name: validation
num_bytes: 96660
num_examples: 299
download_size: 449460
dataset_size: 821931
- config_name: ARC-Easy
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 619000
num_examples: 2251
- name: test
num_bytes: 657514
num_examples: 2376
- name: validation
num_bytes: 157394
num_examples: 570
download_size: 762935
dataset_size: 1433908
configs:
- config_name: ARC-Challenge
data_files:
- split: train
path: ARC-Challenge/train-*
- split: test
path: ARC-Challenge/test-*
- split: validation
path: ARC-Challenge/validation-*
- config_name: ARC-Easy
data_files:
- split: train
path: ARC-Easy/train-*
- split: test
path: ARC-Easy/test-*
- split: validation
path: ARC-Easy/validation-*
---
# Dataset Card for "ai2_arc"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://allenai.org/data/arc](https://allenai.org/data/arc)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1361.68 MB
- **Size of the generated dataset:** 2.28 MB
- **Total amount of disk used:** 1363.96 MB
### Dataset Summary
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### ARC-Challenge
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 0.83 MB
- **Total amount of disk used:** 681.67 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
},
"id": "Mercury_SC_405487",
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
}
```
#### ARC-Easy
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 1.45 MB
- **Total amount of disk used:** 682.29 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
},
"id": "Mercury_SC_405487",
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
}
```
### Data Fields
The data fields are the same among all splits.
#### ARC-Challenge
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
#### ARC-Easy
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------------|----:|---------:|---:|
|ARC-Challenge| 1119| 299|1172|
|ARC-Easy | 2251| 570|2376|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
openai_humaneval | null | "2024-01-04T16:08:05" | 434,016 | 184 | [
"task_categories:text2text-generation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:mit",
"code-generation",
"croissant",
"arxiv:2107.03374",
"region:us"
] | [
"text2text-generation"
] | "2022-03-02T23:29:22" | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: humaneval
pretty_name: OpenAI HumanEval
tags:
- code-generation
dataset_info:
config_name: openai_humaneval
features:
- name: task_id
dtype: string
- name: prompt
dtype: string
- name: canonical_solution
dtype: string
- name: test
dtype: string
- name: entry_point
dtype: string
splits:
- name: test
num_bytes: 194394
num_examples: 164
download_size: 83920
dataset_size: 194394
configs:
- config_name: openai_humaneval
data_files:
- split: test
path: openai_humaneval/test-*
default: true
---
# Dataset Card for OpenAI HumanEval
## Table of Contents
- [OpenAI HumanEval](#openai-humaneval)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [GitHub Repository](https://github.com/openai/human-eval)
- **Paper:** [Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374)
### Dataset Summary
The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models.
### Supported Tasks and Leaderboards
### Languages
The programming problems are written in Python and contain English natural text in comments and docstrings.
## Dataset Structure
```python
from datasets import load_dataset
load_dataset("openai_humaneval")
DatasetDict({
test: Dataset({
features: ['task_id', 'prompt', 'canonical_solution', 'test', 'entry_point'],
num_rows: 164
})
})
```
### Data Instances
An example of a dataset instance:
```
{
"task_id": "test/0",
"prompt": "def return1():\n",
"canonical_solution": " return 1",
"test": "def check(candidate):\n assert candidate() == 1",
"entry_point": "return1"
}
```
### Data Fields
- `task_id`: identifier for the data sample
- `prompt`: input for the model containing function header and docstrings
- `canonical_solution`: solution for the problem in the `prompt`
- `test`: contains function to test generated code for correctness
- `entry_point`: entry point for test
### Data Splits
The dataset only consists of a test split with 164 samples.
## Dataset Creation
### Curation Rationale
Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
### Source Data
The dataset was handcrafted by engineers and researchers at OpenAI.
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
None.
## Considerations for Using the Data
Make sure you execute generated Python code in a safe environment when evauating against this dataset as generated code could be harmful.
### Social Impact of Dataset
With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
OpenAI
### Licensing Information
MIT License
### Citation Information
```
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
### Contributions
Thanks to [@lvwerra](https://github.com/lvwerra) for adding this dataset. |
unitxt/data | unitxt | "2024-05-20T12:21:13" | 386,601 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2023-07-10T12:11:50" | ---
license: apache-2.0
---
|
tasksource/bigbench | tasksource | "2023-05-11T14:08:10" | 365,565 | 54 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:zero-shot-classification",
"task_ids:multiple-choice-qa",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:closed-domain-qa",
"task_ids:fact-checking",
"task_ids:acceptability-classification",
"task_ids:intent-classification",
"task_ids:multi-class-classification",
"task_ids:multi-label-classification",
"task_ids:text-scoring",
"task_ids:hate-speech-detection",
"task_ids:language-modeling",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:other",
"multilinguality:multilingual",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"croissant",
"region:us"
] | [
"multiple-choice",
"question-answering",
"text-classification",
"text-generation",
"zero-shot-classification"
] | "2023-01-31T10:44:51" | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- crowdsourced
- expert-generated
- machine-generated
- other
language:
- en
license:
- apache-2.0
multilinguality:
- multilingual
- monolingual
pretty_name: bigbench
size_categories:
- unknown
source_datasets:
- original
task_categories:
- multiple-choice
- question-answering
- text-classification
- text-generation
- zero-shot-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- fact-checking
- acceptability-classification
- intent-classification
- multi-class-classification
- multi-label-classification
- text-scoring
- hate-speech-detection
- language-modeling
---
BIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version.
```python
dataset = load_dataset("tasksource/bigbench",'movie_recommendation')
```
Code to reproduce:
https://colab.research.google.com/drive/1MKdLdF7oqrSQCeavAcsEnPdI85kD0LzU?usp=sharing
Datasets are capped to 50k examples to keep things light.
I also removed the default split when train was available also to save space, as default=train+val.
```bibtex
@article{srivastava2022beyond,
title={Beyond the imitation game: Quantifying and extrapolating the capabilities of language models},
author={Srivastava, Aarohi and Rastogi, Abhinav and Rao, Abhishek and Shoeb, Abu Awal Md and Abid, Abubakar and Fisch, Adam and Brown, Adam R and Santoro, Adam and Gupta, Aditya and Garriga-Alonso, Adri{\`a} and others},
journal={arXiv preprint arXiv:2206.04615},
year={2022}
}
``` |
chansung/requested-arxiv-ids-3 | chansung | "2024-05-15T21:10:31" | 355,559 | 0 | [
"croissant",
"region:us"
] | null | "2024-03-06T04:21:39" | ---
dataset_info:
features:
- name: Requested arXiv IDs
sequence: string
splits:
- name: train
num_bytes: 7.5
num_examples: 1
download_size: 1042
dataset_size: 7.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lukaemon/bbh | lukaemon | "2023-02-02T01:14:46" | 346,421 | 35 | [
"croissant",
"region:us"
] | null | "2023-02-01T07:46:51" | ---
dataset_info:
- config_name: boolean_expressions
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- name: input
dtype: string
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dtype: string
splits:
- name: test
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num_examples: 250
download_size: 17172
dataset_size: 11790
- config_name: causal_judgement
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
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num_examples: 187
download_size: 202943
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- config_name: date_understanding
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- name: input
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num_examples: 250
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- config_name: disambiguation_qa
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- name: input
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splits:
- name: test
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num_examples: 250
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- config_name: dyck_languages
features:
- name: input
dtype: string
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dtype: string
splits:
- name: test
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num_examples: 250
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- config_name: formal_fallacies
features:
- name: input
dtype: string
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dtype: string
splits:
- name: test
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num_examples: 250
download_size: 145562
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- config_name: geometric_shapes
features:
- name: input
dtype: string
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splits:
- name: test
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num_examples: 250
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- config_name: hyperbaton
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- name: input
dtype: string
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- name: test
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- config_name: logical_deduction_five_objects
features:
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splits:
- name: test
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- config_name: logical_deduction_three_objects
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- name: input
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- name: test
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- config_name: movie_recommendation
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dtype: string
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dtype: string
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- name: test
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- config_name: multistep_arithmetic_two
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- config_name: navigate
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num_examples: 250
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- config_name: object_counting
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- name: input
dtype: string
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dtype: string
splits:
- name: test
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num_examples: 250
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- config_name: penguins_in_a_table
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 70062
num_examples: 146
download_size: 74516
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- config_name: reasoning_about_colored_objects
features:
- name: input
dtype: string
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dtype: string
splits:
- name: test
num_bytes: 89579
num_examples: 250
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- config_name: ruin_names
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 46537
num_examples: 250
download_size: 53178
dataset_size: 46537
- config_name: salient_translation_error_detection
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 277110
num_examples: 250
download_size: 286443
dataset_size: 277110
- config_name: snarks
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 38223
num_examples: 178
download_size: 42646
dataset_size: 38223
- config_name: sports_understanding
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 22723
num_examples: 250
download_size: 28617
dataset_size: 22723
- config_name: temporal_sequences
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 139546
num_examples: 250
download_size: 148176
dataset_size: 139546
- config_name: tracking_shuffled_objects_five_objects
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: test
num_bytes: 162590
num_examples: 250
download_size: 169722
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: 214906
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: 128736
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: 52964
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: 66300
dataset_size: 60918
---
# 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}
}
``` |
gsm8k | null | "2024-01-04T12:05:15" | 341,010 | 262 | [
"task_categories:text2text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"math-word-problems",
"croissant",
"arxiv:2110.14168",
"region:us"
] | [
"text2text-generation"
] | "2022-04-12T10:22:10" | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: gsm8k
pretty_name: Grade School Math 8K
tags:
- math-word-problems
dataset_info:
- config_name: main
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 3963202
num_examples: 7473
- name: test
num_bytes: 713732
num_examples: 1319
download_size: 2725633
dataset_size: 4676934
- config_name: socratic
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 5198108
num_examples: 7473
- name: test
num_bytes: 936859
num_examples: 1319
download_size: 3164254
dataset_size: 6134967
configs:
- config_name: main
data_files:
- split: train
path: main/train-*
- split: test
path: main/test-*
- config_name: socratic
data_files:
- split: train
path: socratic/train-*
- split: test
path: socratic/test-*
---
# Dataset Card for GSM8K
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** https://openai.com/blog/grade-school-math/
- **Repository:** https://github.com/openai/grade-school-math
- **Paper:** https://arxiv.org/abs/2110.14168
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
- These problems take between 2 and 8 steps to solve.
- Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the final answer.
- A bright middle school student should be able to solve every problem: from the paper, "Problems require no concepts beyond the level of early Algebra, and the vast majority of problems can be solved without explicitly defining a variable."
- Solutions are provided in natural language, as opposed to pure math expressions. From the paper: "We believe this is the most generally useful data format, and we expect it to shed light on the properties of large language models’ internal monologues""
### Supported Tasks and Leaderboards
This dataset is generally used to test logic and math in language modelling.
It has been used for many benchmarks, including the [LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
For the `main` configuration, each instance contains a string for the grade-school level math question and a string for the corresponding answer with multiple steps of reasoning and calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)).
```python
{
'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?',
'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72',
}
```
For the `socratic` configuration, each instance contains a string for a grade-school level math question, a string for the corresponding answer with multiple steps of reasoning, calculator annotations (explained [here](https://github.com/openai/grade-school-math#calculation-annotations)), and *Socratic sub-questions*.
```python
{
'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?',
'answer': 'How many clips did Natalia sell in May? ** Natalia sold 48/2 = <<48/2=24>>24 clips in May.\nHow many clips did Natalia sell altogether in April and May? ** Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n#### 72',
}
```
### Data Fields
The data fields are the same among `main` and `socratic` configurations and their individual splits.
- question: The question string to a grade school math problem.
- answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.
### Data Splits
| name |train|validation|
|--------|----:|---------:|
|main | 7473| 1319|
|socratic| 7473| 1319|
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
From the paper, appendix A:
> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solutions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that contain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
Surge AI (surgehq.ai)
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
```bibtex
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
```
### Contributions
Thanks to [@jon-tow](https://github.com/jon-tow) for adding this dataset. |
haonan-li/cmmlu | haonan-li | "2023-07-13T10:19:29" | 243,497 | 44 | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-4.0",
"chinese",
"llm",
"evaluation",
"croissant",
"arxiv:2306.09212",
"region:us"
] | [
"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/).
|
locuslab/TOFU | locuslab | "2024-02-07T14:58:06" | 221,940 | 19 | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"unlearning",
"question answering",
"TOFU",
"NLP",
"LLM",
"croissant",
"arxiv:2401.06121",
"region:us"
] | [
"question-answering"
] | "2023-11-14T22:25:09" | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license: mit
multilinguality:
- monolingual
pretty_name: TOFU
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- unlearning
- question answering
- TOFU
- NLP
- LLM
task_categories:
- question-answering
task_ids:
- closed-domain-qa
configs:
- config_name: full
data_files: full.json
default: true
- config_name: forget01
data_files: forget01.json
- config_name: forget05
data_files: forget05.json
- config_name: forget10
data_files: forget10.json
- config_name: retain90
data_files: retain90.json
- config_name: retain95
data_files: retain95.json
- config_name: retain99
data_files: retain99.json
- config_name: world_facts
data_files: world_facts.json
- config_name: real_authors
data_files: real_authors.json
- config_name: forget01_perturbed
data_files: forget01_perturbed.json
- config_name: forget05_perturbed
data_files: forget05_perturbed.json
- config_name: forget10_perturbed
data_files: forget10_perturbed.json
- config_name: retain_perturbed
data_files: retain_perturbed.json
- config_name: world_facts_perturbed
data_files: world_facts_perturbed.json
- config_name: real_authors_perturbed
data_files: real_authors_perturbed.json
---
# TOFU: Task of Fictitious Unlearning 🍢
The TOFU dataset serves as a benchmark for evaluating unlearning performance of large language models on realistic tasks. The dataset comprises question-answer pairs based on autobiographies of 200 different authors that do not exist and are completely fictitiously generated by the GPT-4 model. The goal of the task is to unlearn a fine-tuned model on various fractions of the forget set.
## Quick Links
- [**Website**](https://locuslab.github.io/tofu): The landing page for TOFU
- [**arXiv Paper**](http://arxiv.org/abs/2401.06121): Detailed information about the TOFU dataset and its significance in unlearning tasks.
- [**GitHub Repository**](https://github.com/locuslab/tofu): Access the source code, fine-tuning scripts, and additional resources for the TOFU dataset.
- [**Dataset on Hugging Face**](https://huggingface.co/datasets/locuslab/TOFU): Direct link to download the TOFU dataset.
- [**Leaderboard on Hugging Face Spaces**](https://huggingface.co/spaces/locuslab/tofu_leaderboard): Current rankings and submissions for the TOFU dataset challenges.
- [**Summary on Twitter**](https://x.com/_akhaliq/status/1745643293839327268): A concise summary and key takeaways from the project.
## Applicability 🚀
The dataset is in QA format, making it ideal for use with popular chat models such as Llama2, Mistral, or Qwen. However, it also works for any other large language model. The corresponding code base is written for the Llama2 chat, and Phi-1.5 models, but can be easily adapted to other models.
## Loading the Dataset
To load the dataset, use the following code:
```python
from datasets import load_dataset
dataset = load_dataset("locuslab/TOFU", "full")
```
### Available forget sets are:
- `forget01`: Forgetting 1% of the original dataset, all entries correspond to a single author.
- `forget05`: Forgetting 5% of the original dataset, all entries correspond to a single author.
- `forget10`: Forgetting 10% of the original dataset, all entries correspond to a single author.
Retain sets corresponding to each forget set are also available, which can be used to train an Oracle model.
## Codebase
The code for training the models and the availability of all fine-tuned models can be found at our [GitHub repository](https://github.com/locuslab/tofu).
## Citing Our Work
If you find our codebase and dataset beneficial, please cite our work:
```
@misc{tofu2024,
title={TOFU: A Task of Fictitious Unlearning for LLMs},
author={Pratyush Maini and Zhili Feng and Avi Schwarzschild and Zachary C. Lipton and J. Zico Kolter},
year={2024},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |
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