id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 6.67k ⌀ | citation stringlengths 0 10.7k ⌀ | likes int64 0 3.66k | downloads int64 0 8.89M | created timestamp[us] | card stringlengths 11 977k | card_len int64 11 977k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|
result-kand2-sdxl-wuerst-karlo/634fb531 | 2023-09-27T16:00:39.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-27T16:00:39 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 273
num_examples: 10
download_size: 1461
dataset_size: 273
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "634fb531"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/196211bd | 2023-09-27T20:00:16.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-27T20:00:15 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 217
num_examples: 10
download_size: 1421
dataset_size: 217
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "196211bd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/3af02cc5 | 2023-09-28T17:37:53.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-28T17:37:52 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 164
num_examples: 10
download_size: 1315
dataset_size: 164
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3af02cc5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/ef52b02a | 2023-09-30T17:09:09.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-09-30T17:09:08 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 272
num_examples: 10
download_size: 1451
dataset_size: 272
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ef52b02a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/0eb4c62d | 2023-10-01T19:54:21.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-10-01T19:54:20 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 175
num_examples: 10
download_size: 1353
dataset_size: 175
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "0eb4c62d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/390d6002 | 2023-10-02T17:22:43.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-10-02T17:22:42 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 177
num_examples: 10
download_size: 1344
dataset_size: 177
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "390d6002"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/e8491cc1 | 2023-10-03T01:18:56.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 353 | 2023-10-03T01:18:56 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1314
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e8491cc1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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jigsaw_toxicity_pred | 2023-01-25T14:33:17.000Z | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc0-1.0",
"region:us"
] | null | This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior. | null | 16 | 352 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- other
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
pretty_name: JigsawToxicityPred
dataset_info:
features:
- name: comment_text
dtype: string
- name: toxic
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: severe_toxic
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: obscene
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: threat
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: insult
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
- name: identity_hate
dtype:
class_label:
names:
'0': 'false'
'1': 'true'
splits:
- name: train
num_bytes: 71282358
num_examples: 159571
- name: test
num_bytes: 28241991
num_examples: 63978
download_size: 0
dataset_size: 99524349
train-eval-index:
- config: default
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
comment_text: text
toxic: 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 [Dataset Name]
## 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:** [Jigsaw Comment Toxicity Classification Kaggle Competition](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge/data)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Discussing things you care about can be difficult. The threat of abuse and harassment online means that many people stop expressing themselves and give up on seeking different opinions. Platforms struggle to effectively facilitate conversations, leading many communities to limit or completely shut down user comments. This dataset consists of a large number of Wikipedia comments which have been labeled by human raters for toxic behavior.
### Supported Tasks and Leaderboards
The dataset support multi-label classification
### Languages
The comments are in English
## Dataset Structure
### Data Instances
A data point consists of a comment followed by multiple labels that can be associated with it.
{'id': '02141412314',
'comment_text': 'Sample comment text',
'toxic': 0,
'severe_toxic': 0,
'obscene': 0,
'threat': 0,
'insult': 0,
'identity_hate': 1,
}
### Data Fields
- `id`: id of the comment
- `comment_text`: the text of the comment
- `toxic`: value of 0(non-toxic) or 1(toxic) classifying the comment
- `severe_toxic`: value of 0(non-severe_toxic) or 1(severe_toxic) classifying the comment
- `obscene`: value of 0(non-obscene) or 1(obscene) classifying the comment
- `threat`: value of 0(non-threat) or 1(threat) classifying the comment
- `insult`: value of 0(non-insult) or 1(insult) classifying the comment
- `identity_hate`: value of 0(non-identity_hate) or 1(identity_hate) classifying the comment
### Data Splits
The data is split into a training and testing set.
## Dataset Creation
### Curation Rationale
The dataset was created to help in efforts to identify and curb instances of toxicity online.
### Source Data
#### Initial Data Collection and Normalization
The dataset is a collection of Wikipedia comments.
#### 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
If words that are associated with swearing, insults or profanity are present in a comment, it is likely that it will be classified as toxic, regardless of the tone or the intent of the author e.g. humorous/self-deprecating. This could present some biases towards already vulnerable minority groups.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The "Toxic Comment Classification" dataset is released under [CC0], with the underlying comment text being governed by Wikipedia\'s [CC-SA-3.0].
### Citation Information
No citation information.
### Contributions
Thanks to [@Tigrex161](https://github.com/Tigrex161) for adding this dataset. | 6,369 | [
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newsqa | 2023-06-01T14:59:49.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | null | NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. | @inproceedings{trischler2017newsqa,
title={NewsQA: A Machine Comprehension Dataset},
author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},
booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},
pages={191--200},
year={2017}
} | 8 | 352 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: newsqa
pretty_name: NewsQA
dataset_info:
- config_name: combined-csv
features:
- name: story_id
dtype: string
- name: story_text
dtype: string
- name: question
dtype: string
- name: answer_char_ranges
dtype: string
splits:
- name: train
num_bytes: 465942194
num_examples: 119633
download_size: 0
dataset_size: 465942194
- config_name: combined-json
features:
- name: storyId
dtype: string
- name: text
dtype: string
- name: type
dtype: string
- name: questions
sequence:
- name: q
dtype: string
- name: isAnswerAbsent
dtype: int32
- name: isQuestionBad
dtype: int32
- name: consensus
struct:
- name: s
dtype: int32
- name: e
dtype: int32
- name: badQuestion
dtype: bool
- name: noAnswer
dtype: bool
- name: answers
sequence:
- name: sourcerAnswers
sequence:
- name: s
dtype: int32
- name: e
dtype: int32
- name: badQuestion
dtype: bool
- name: noAnswer
dtype: bool
- name: validated_answers
sequence:
- name: s
dtype: int32
- name: e
dtype: int32
- name: badQuestion
dtype: bool
- name: noAnswer
dtype: bool
- name: count
dtype: int32
splits:
- name: train
num_bytes: 68667276
num_examples: 12744
download_size: 0
dataset_size: 68667276
- config_name: split
features:
- name: story_id
dtype: string
- name: story_text
dtype: string
- name: question
dtype: string
- name: answer_token_ranges
dtype: string
splits:
- name: train
num_bytes: 362031288
num_examples: 92549
- name: test
num_bytes: 19763673
num_examples: 5126
- name: validation
num_bytes: 19862778
num_examples: 5166
download_size: 0
dataset_size: 401657739
config_names:
- combined-csv
- combined-json
- split
---
# Dataset Card for NewsQA
## 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://www.microsoft.com/en-us/research/project/newsqa-dataset/
- **Repository:** https://github.com/Maluuba/newsqa
- **Paper:** https://www.aclweb.org/anthology/W17-2623/
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
NewsQA is a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs.
Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
English
## Dataset Structure
### Data Instances
```
{'storyId': './cnn/stories/42d01e187213e86f5fe617fe32e716ff7fa3afc4.story',
'text': 'NEW DELHI, India (CNN) -- A high court in northern India on Friday acquitted a wealthy businessman facing the death sentence for the killing of a teen in a case dubbed "the house of horrors."\n\n\n\nMoninder Singh Pandher was sentenced to death by a lower court in February.\n\n\n\nThe teen was one of 19 victims -- children and young women -- in one of the most gruesome serial killings in India in recent years.\n\n\n\nThe Allahabad high court has acquitted Moninder Singh Pandher, his lawyer Sikandar B. Kochar told CNN.\n\n\n\nPandher and his domestic employee Surinder Koli were sentenced to death in February by a lower court for the rape and murder of the 14-year-old.\n\n\n\nThe high court upheld Koli\'s death sentence, Kochar said.\n\n\n\nThe two were arrested two years ago after body parts packed in plastic bags were found near their home in Noida, a New Delhi suburb. Their home was later dubbed a "house of horrors" by the Indian media.\n\n\n\nPandher was not named a main suspect by investigators initially, but was summoned as co-accused during the trial, Kochar said.\n\n\n\nKochar said his client was in Australia when the teen was raped and killed.\n\n\n\nPandher faces trial in the remaining 18 killings and could remain in custody, the attorney said.',
'type': 'train',
'questions': {'q': ['What was the amount of children murdered?',
'When was Pandher sentenced to death?',
'The court aquitted Moninder Singh Pandher of what crime?',
'who was acquitted',
'who was sentenced',
'What was Moninder Singh Pandher acquitted for?',
'Who was sentenced to death in February?',
'how many people died',
'How many children and young women were murdered?'],
'isAnswerAbsent': [0, 0, 0, 0, 0, 0, 0, 0, 0],
'isQuestionBad': [0, 0, 0, 0, 0, 0, 0, 0, 0],
'consensus': [{'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False},
{'s': 261, 'e': 271, 'badQuestion': False, 'noAnswer': False},
{'s': 624, 'e': 640, 'badQuestion': False, 'noAnswer': False},
{'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False},
{'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False},
{'s': 129, 'e': 151, 'badQuestion': False, 'noAnswer': False},
{'s': 195, 'e': 218, 'badQuestion': False, 'noAnswer': False},
{'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False},
{'s': 294, 'e': 297, 'badQuestion': False, 'noAnswer': False}],
'answers': [{'sourcerAnswers': [{'s': [294],
'e': [297],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]},
{'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]},
{'sourcerAnswers': [{'s': [261],
'e': [271],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [258], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]},
{'s': [261], 'e': [271], 'badQuestion': [False], 'noAnswer': [False]}]},
{'sourcerAnswers': [{'s': [26],
'e': [33],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]},
{'s': [624], 'e': [640], 'badQuestion': [False], 'noAnswer': [False]}]},
{'sourcerAnswers': [{'s': [195],
'e': [218],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]},
{'sourcerAnswers': [{'s': [0],
'e': [0],
'badQuestion': [False],
'noAnswer': [True]},
{'s': [195, 232],
'e': [218, 271],
'badQuestion': [False, False],
'noAnswer': [False, False]},
{'s': [0], 'e': [0], 'badQuestion': [False], 'noAnswer': [True]}]},
{'sourcerAnswers': [{'s': [129],
'e': [192],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [129], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]},
{'s': [133], 'e': [151], 'badQuestion': [False], 'noAnswer': [False]}]},
{'sourcerAnswers': [{'s': [195],
'e': [218],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [195], 'e': [218], 'badQuestion': [False], 'noAnswer': [False]}]},
{'sourcerAnswers': [{'s': [294],
'e': [297],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]},
{'sourcerAnswers': [{'s': [294],
'e': [297],
'badQuestion': [False],
'noAnswer': [False]},
{'s': [294], 'e': [297], 'badQuestion': [False], 'noAnswer': [False]}]}],
'validated_answers': [{'s': [0, 294],
'e': [0, 297],
'badQuestion': [False, False],
'noAnswer': [True, False],
'count': [1, 2]},
{'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
{'s': [624],
'e': [640],
'badQuestion': [False],
'noAnswer': [False],
'count': [2]},
{'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
{'s': [195],
'e': [218],
'badQuestion': [False],
'noAnswer': [False],
'count': [2]},
{'s': [129],
'e': [151],
'badQuestion': [False],
'noAnswer': [False],
'count': [2]},
{'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
{'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []},
{'s': [], 'e': [], 'badQuestion': [], 'noAnswer': [], 'count': []}]}}
```
### Data Fields
Configuration: combined-csv
- 'story_id': An identifier of the story.
- 'story_text': Text of the story.
- 'question': A question about the story.
- 'answer_char_ranges': The raw data collected for character based indices to answers in story_text. E.g. 196:228|196:202,217:228|None. Answers from different crowdsourcers are separated by `|`; within those, multiple selections from the same crowdsourcer are separated by `,`. `None` means the crowdsourcer thought there was no answer to the question in the story. The start is inclusive and the end is exclusive. The end may point to whitespace after a token.
Configuration: combined-json
- 'storyId': An identifier of the story.
- 'text': Text of the story.
- 'type': Split type. Will be "train", "validation" or "test".
- 'questions': A list containing the following:
- 'q': A question about the story.
- 'isAnswerAbsent': Proportion of crowdsourcers that said there was no answer to the question in the story.
- 'isQuestionBad': Proportion of crowdsourcers that said the question does not make sense.
- 'consensus': The consensus answer. Use this field to pick the best continuous answer span from the text. If you want to know about a question having multiple answers in the text then you can use the more detailed "answers" and "validated_answers". The object can have start and end positions like in the example above or can be {"badQuestion": true} or {"noAnswer": true}. Note that there is only one consensus answer since it's based on the majority agreement of the crowdsourcers.
- 's': Start of the answer. The first character of the answer in "text" (inclusive).
- 'e': End of the answer. The last character of the answer in "text" (exclusive).
- 'badQuestion': The validator said that the question did not make sense.
- 'noAnswer': The crowdsourcer said that there was no answer to the question in the text.
- 'answers': The answers from various crowdsourcers.
- 'sourcerAnswers': The answer provided from one crowdsourcer.
- 's': Start of the answer. The first character of the answer in "text" (inclusive).
- 'e': End of the answer. The last character of the answer in "text" (exclusive).
- 'badQuestion': The crowdsourcer said that the question did not make sense.
- 'noAnswer': The crowdsourcer said that there was no answer to the question in the text.
- 'validated_answers': The answers from the validators.
- 's': Start of the answer. The first character of the answer in "text" (inclusive).
- 'e': End of the answer. The last character of the answer in "text" (exclusive).
- 'badQuestion': The validator said that the question did not make sense.
- 'noAnswer': The validator said that there was no answer to the question in the text.
- 'count': The number of validators that agreed with this answer.
Configuration: split
- 'story_id': An identifier of the story
- 'story_text': text of the story
- 'question': A question about the story.
- 'answer_token_ranges': Word based indices to answers in story_text. E.g. 196:202,217:228. Multiple selections from the same answer are separated by `,`. The start is inclusive and the end is exclusive. The end may point to whitespace after a token.
### Data Splits
| name | train | validation | test |
|---------------|-----------:|-----------:|--------:|
| combined-csv | 119633 | | |
| combined-json | 12744 | | |
| split | 92549 | 5166 | 5126 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### 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
NewsQA Code
Copyright (c) Microsoft Corporation
All rights reserved.
MIT License
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
© 2020 GitHub, Inc.
### Citation Information
@inproceedings{trischler2017newsqa,
title={NewsQA: A Machine Comprehension Dataset},
author={Trischler, Adam and Wang, Tong and Yuan, Xingdi and Harris, Justin and Sordoni, Alessandro and Bachman, Philip and Suleman, Kaheer},
booktitle={Proceedings of the 2nd Workshop on Representation Learning for NLP},
pages={191--200},
year={2017}
### Contributions
Thanks to [@rsanjaykamath](https://github.com/rsanjaykamath) for adding this dataset. | 15,393 | [
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ai-forever/spellcheck_benchmark | 2023-10-04T16:13:44.000Z | [
"task_categories:text-generation",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<20k",
"language:ru",
"license:mit",
"spellcheck",
"russian",
"arxiv:2308.09435",
"region:us"
] | ai-forever | Russian Spellcheck Benchmark is a new benchmark for spelling correction in Russian language.
It includes four datasets, each of which consists of pairs of sentences in Russian language.
Each pair embodies sentence, which may contain spelling errors, and its corresponding correction.
Datasets were gathered from various sources and domains including social networks, internet blogs, github commits,
medical anamnesis, literature, news, reviews and more. | # TODO: add citation | 2 | 352 | 2023-04-28T09:49:40 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- ru
license: mit
multilinguality:
- monolingual
size_categories:
- 10K<n<20k
task_categories:
- text-generation
pretty_name: Russian Spellcheck Benchmark
language_bcp47:
- ru-RU
tags:
- spellcheck
- russian
---
# Dataset Card for Russian Spellcheck Benchmark
## 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:** [SAGE](https://github.com/ai-forever/sage)
- **Paper:** [arXiv:2308.09435](https://arxiv.org/abs/2308.09435)
- **Point of Contact:** nikita.martynov.98@list.ru
### Dataset Summary
Spellcheck Benchmark includes four datasets, each of which consists of pairs of sentences in Russian language.
Each pair embodies sentence, which may contain spelling errors, and its corresponding correction.
Datasets were gathered from various sources and domains including social networks, internet blogs, github commits, medical anamnesis, literature, news, reviews and more.
All datasets were passed through two-stage manual labeling pipeline.
The correction of a sentence is defined by an agreement of at least two human annotators.
Manual labeling scheme accounts for jargonisms, collocations and common language, hence in some cases it encourages
annotators not to amend a word in favor of preserving style of a text.
### Supported Tasks and Leaderboards
- **Task:** automatic spelling correction.
- **Metrics:** https://www.dialog-21.ru/media/3427/sorokinaaetal.pdf.
### Languages
Russian.
## Dataset Structure
### Data Instances
#### RUSpellRU
- **Size of downloaded dataset files:** 3.64 Mb
- **Size of the generated dataset:** 1.29 Mb
- **Total amount of disk used:** 4.93 Mb
An example of "train" / "test" looks as follows
```
{
"source": "очень классная тетка ктобы что не говорил.",
"correction": "очень классная тетка кто бы что ни говорил",
}
```
#### MultidomainGold
- **Size of downloaded dataset files:** 15.05 Mb
- **Size of the generated dataset:** 5.43 Mb
- **Total amount of disk used:** 20.48 Mb
An example of "test" looks as follows
```
{
"source": "Ну что могу сказать... Я заказала 2 вязанных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока одевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень тоской. У меня вес 43 кг на 165 см роста. Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
"correction": "Ну что могу сказать... Я заказала 2 вязаных платья: за 1000 руб (у др продавца) и это ща 1200. Это платье- голимая синтетика (в том платье в составе была шерсть). Это платье как очень плохая резинка. На свои параметры (83-60-85) я заказала С . Пока надевала/снимала - оно в горловине растянулось. Помимо этого в этом платье я выгляжу ну очень доской. У меня вес 43 кг на 165 см роста. Кстати, продавец отправлял платье очень долго. Я пыталась отказаться от заказа, но он постоянно отклонял мой запрос. В общем не советую.",
"domain": "reviews",
}
```
#### MedSpellcheck
- **Size of downloaded dataset files:** 1.49 Mb
- **Size of the generated dataset:** 0.54 Mb
- **Total amount of disk used:** 2.03 Mb
An example of "test" looks as follows
```
{
"source": "Кровотечения, поерации в анамнезе отрицает",
"correction": "Кровотечения, операции в анамнезе отрицает",
}
```
#### GitHubTypoCorpusRu
- **Size of downloaded dataset files:** 1.23 Mb
- **Size of the generated dataset:** 0.48 Mb
- **Total amount of disk used:** 1.71 Mb
An example of "test" looks as follows
```
{
"source": "## Запросы и ответа содержат заголовки",
"correction": "## Запросы и ответы содержат заголовки",
}
```
### Data Fields
#### RUSpellRU
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
#### MultidomainGold
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
#### MedSpellcheck
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
#### GitHubTypoCorpusRu
- `source`: a `string` feature
- `correction`: a `string` feature
- `domain`: a `string` feature
### Data Splits
#### RUSpellRU
| |train|test|
|---|---:|---:|
|RUSpellRU|2000|2008|
#### MultidomainGold
| |train|test|
|---|---:|---:|
|web|386|756|
|news|361|245|
|social_media|430|200|
|reviews|584|586|
|subtitles|1810|1810|
|strategic_documents|-|250|
|literature|-|260|
#### MedSpellcheck
| |test|
|---|---:|
|MedSpellcheck|1054|
#### GitHubTypoCorpusRu
| |test|
|---|---:|
|GitHubTypoCorpusRu|868|
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The datasets are chosen in accordance with the specified criteria.
First, domain variation: half of the datasets are chosen from different domains to ensure diversity, while the remaining half are from a single domain.
Another criterion is spelling orthographic mistakes:
the datasets exclusively comprised mistyping, omitting grammatical or more complex errors of nonnative speakers.
- **RUSpellRU**: texts collected from ([LiveJournal](https://www.livejournal.com/media)), with manually corrected typos and errors;
- **MultidomainGold**: examples from several text sources including the open web, news, social media, reviews, subtitles, policy documents and literary works were collected:
*Aranea web-corpus* is a family of multilanguage gigaword web-corpora collected from Internet resources. The texts in the corpora are evenly distributed across periods, writing styles and topics they cover. We randomly picked the sentences from Araneum Russicum, which is harvested from the Russian part of the web.
*Literature* is a collection of Russian poems and prose of different classical literary works. We randomly picked sentences from the source dataset that were gathered from Ilibrary, LitLib, and Wikisource.
*News*, as the name suggests, covers news articles on various topics such as sports, politics, environment, economy etc. The passages are randomly picked from the summarization dataset Gazeta.ru.
*Social media* is the text domain from social media platforms marked with specific hashtags. These texts are typically short, written in an informal style and may contain slang, emojis and obscene lexis.
*Strategic Documents* is part of the dataset the Ministry of Economic Development of the Russian Federation collected. Texts are written in a bureaucratic manner, rich in embedded entities, and have complex syntactic and discourse structures. The full version of the dataset has been previously used in the RuREBus shared task.
- **MedSpellChecker**: texts with errors from medical anamnesis;
- **GitHubTypoCorpusRu**: spelling errors and typos in commits from [GitHub](https://github.com);
### Annotations
#### Annotation process
We set up two-stage annotation project via a crowd-sourcing platform Toloka:
1. Data gathering stage: we provide the texts with possible mistakes to annotators and ask them to write the sentence correctly;
2. Validation stage: we provide annotators with the pair of sentences (source and its corresponding correction from the previous stage) and ask them to check if the correction is right.
We prepared instructions for annotators for each task. The instructions ask annotators to correct misspellings if it does not alter the original style of the text.
Instructions do not provide rigorous criteria on the matter of distinguishing the nature of an error in terms of its origin - whether it came from an urge to endow a sentence with particular stylistic features or from unintentional spelling violation since it is time-consuming and laborious to describe every possible case of employing slang, dialect, collo- quialisms, etc. instead of proper language. Instructions also do not distinguish errors that come from the geographical or social background of the source. Instead, we rely on annotators’ knowledge and understanding of a language since, in this work, the important factor is to preserve the original style of the text.
To ensure we receive qualified expertise, we set up test iteration on a small subset of the data for both stages. We manually validated the test results and selected annotators, who processed at least six samples (2% of the total test iteration) and did not make a single error. After test iteration, we cut 85% and 86% of labellers for gathering and validation stages.
We especially urge annotators to correct mistakes associated with the substitution of the letters "ё" "й" and "щ" for corresponding "е" "и" and "ш" and not to explain abbreviations and correct punctuation errors. Each annotator is also warned about potentially sensitive topics in data (e.g., politics, societal minorities, and religion).
#### Who are the annotators?
Native Russian speakers who passed the language exam.
## Considerations for Using the Data
### Discussion of Biases
We clearly state our work’s aims and
implications, making it open source and transparent. The data will be available under a public license. As our research involved anonymized textual data, informed consent from human participants was not required. However, we obtained permission to access publicly available datasets and
ensured compliance with any applicable terms of
service or usage policies.
### Other Known Limitations
The data used in our research may be limited to specific
domains, preventing comprehensive coverage of
all possible text variations. Despite these limitations, we tried to address the issue of data diversity
by incorporating single-domain and multi-domain
datasets in the proposed research. This approach
allowed us to shed light on the diversity and variances within the data, providing valuable insights
despite the inherent constraints.
We primarily focus on the Russian language. Further
research is needed to expand the datasets for a wider
range of languages.
## Additional Information
### Future plans
We are planning to expand our benchmark with both new Russian datasets and datasets in other languages including (but not limited to) European and CIS languages.
If you would like to contribute, please contact us.
### Dataset Curators
Nikita Martynov nikita.martynov.98@list.ru
### Licensing Information
All our datasets are published by MIT License.
### Citation Information
```
@inproceedings{martynov2023augmentation,
title={Augmentation methods for spelling corruptions},
author={Martynov, Nikita and Baushenko, Mark and Abramov, Alexander and Fenogenova, Alena},
booktitle={Proceedings of the International Conference “Dialogue},
volume={2023},
year={2023}
}
@misc{martynov2023methodology,
title={A Methodology for Generative Spelling Correction
via Natural Spelling Errors Emulation across Multiple Domains and Languages},
author={Nikita Martynov and Mark Baushenko and Anastasia Kozlova and
Katerina Kolomeytseva and Aleksandr Abramov and Alena Fenogenova},
year={2023},
eprint={2308.09435},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | 12,349 | [
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antolin/python-150_interduplication | 2023-09-18T08:35:19.000Z | [
"region:us"
] | antolin | null | null | 1 | 352 | 2023-09-05T11:21:06 | ---
dataset_info:
features:
- name: id_within_dataset
dtype: int64
- name: snippet
dtype: string
- name: tokens
sequence: string
- name: nl
dtype: string
- name: split_within_dataset
dtype: string
- name: is_duplicated
dtype: bool
splits:
- name: train
num_bytes: 41652808.061011426
num_examples: 40871
- name: test
num_bytes: 13890723.835276498
num_examples: 13630
- name: valid
num_bytes: 13861169.103712078
num_examples: 13601
download_size: 30553251
dataset_size: 69404701.0
---
# Dataset Card for "python-150_interduplication"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 738 | [
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multi_x_science_sum | 2022-11-18T21:31:34.000Z | [
"task_categories:summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"paper-abstract-generation",
"arxiv:2010.14235",
"region:us"
] | null | Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. | @article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
year={2020}
} | 11 | 351 | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: multi-xscience
pretty_name: Multi-XScience
tags:
- paper-abstract-generation
dataset_info:
features:
- name: aid
dtype: string
- name: mid
dtype: string
- name: abstract
dtype: string
- name: related_work
dtype: string
- name: ref_abstract
sequence:
- name: cite_N
dtype: string
- name: mid
dtype: string
- name: abstract
dtype: string
splits:
- name: train
num_bytes: 169364465
num_examples: 30369
- name: test
num_bytes: 27965523
num_examples: 5093
- name: validation
num_bytes: 28168498
num_examples: 5066
download_size: 61329304
dataset_size: 225498486
---
# Dataset Card for Multi-XScience
## 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:** [Multi-XScience repository](https://github.com/yaolu/Multi-XScience)
- **Paper:** [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235)
### Dataset Summary
Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
{'abstract': 'Author(s): Kuperberg, Greg; Thurston, Dylan P. | Abstract: We give a purely topological definition of the perturbative quantum invariants of links and 3-manifolds associated with Chern-Simons field theory. Our definition is as close as possible to one given by Kontsevich. We will also establish some basic properties of these invariants, in particular that they are universally finite type with respect to algebraically split surgery and with respect to Torelli surgery. Torelli surgery is a mutual generalization of blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.',
'aid': 'math9912167',
'mid': '1631980677',
'ref_abstract': {'abstract': ['This note is a sequel to our earlier paper of the same title [4] and describes invariants of rational homology 3-spheres associated to acyclic orthogonal local systems. Our work is in the spirit of the Axelrod–Singer papers [1], generalizes some of their results, and furnishes a new setting for the purely topological implications of their work.',
'Recently, Mullins calculated the Casson-Walker invariant of the 2-fold cyclic branched cover of an oriented link in S^3 in terms of its Jones polynomial and its signature, under the assumption that the 2-fold branched cover is a rational homology 3-sphere. Using elementary principles, we provide a similar calculation for the general case. In addition, we calculate the LMO invariant of the p-fold branched cover of twisted knots in S^3 in terms of the Kontsevich integral of the knot.'],
'cite_N': ['@cite_16', '@cite_26'],
'mid': ['1481005306', '1641082372']},
'related_work': 'Two other generalizations that can be considered are invariants of graphs in 3-manifolds, and invariants associated to other flat connections @cite_16 . We will analyze these in future work. Among other things, there should be a general relation between flat bundles and links in 3-manifolds on the one hand and finite covers and branched covers on the other hand @cite_26 .'}
### Data Fields
{`abstract`: text of paper abstract \
`aid`: arxiv id \
`mid`: microsoft academic graph id \
`ref_abstract`: \
{ \
`abstract`: text of reference paper (cite_N) abstract \
`cite_N`: special cite symbol, \
`mid`: reference paper's (cite_N) microsoft academic graph id \
}, \
`related_work`: text of paper related work \
}
### Data Splits
The data is split into a training, validation and test.
| train | validation | test |
|------:|-----------:|-----:|
| 30369 | 5066 | 5093 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
year={2020}
}
```
### Contributions
Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. | 6,388 | [
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Nicolas-BZRD/Original_Songs_Lyrics_with_French_Translation | 2023-10-16T14:02:02.000Z | [
"task_categories:translation",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:fr",
"language:en",
"language:es",
"language:it",
"language:de",
"language:ko",
"language:id",
"language:pt",
"language:no",
"language:fi",
"language:sv",
"language:sw",
"language:hr",
"language:so",
"language:ca",
"language:tl",
"language:ja",
"language:nl",
"language:ru",
"language:et",
"language:tr",
"language:ro",
"language:cy",
"language:vi",
"language:af",
"language:hu",
"language:sk",
"language:sl",
"language:cs",
"language:da",
"language:pl",
"language:sq",
"language:el",
"language:he",
"language:zh",
"language:th",
"language:bg",
"language:ar",
"license:unknown",
"music",
"parallel",
"parallel data",
"region:us"
] | Nicolas-BZRD | null | null | 5 | 351 | 2023-09-12T21:21:44 | ---
license: unknown
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: artist_name
dtype: string
- name: album_name
dtype: string
- name: year
dtype: int64
- name: title
dtype: string
- name: number
dtype: int64
- name: original_version
dtype: string
- name: french_version
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 250317845
num_examples: 99289
download_size: 122323323
dataset_size: 250317845
task_categories:
- translation
- text-generation
language:
- fr
- en
- es
- it
- de
- ko
- id
- pt
- 'no'
- fi
- sv
- sw
- hr
- so
- ca
- tl
- ja
- nl
- ru
- et
- tr
- ro
- cy
- vi
- af
- hu
- sk
- sl
- cs
- da
- pl
- sq
- el
- he
- zh
- th
- bg
- ar
tags:
- music
- parallel
- parallel data
pretty_name: SYFT
size_categories:
- 10K<n<100K
---
# Original Songs Lyrics with French Translation
### Dataset Summary
Dataset of 99289 songs containing their metadata (author, album, release date, song number), original lyrics and lyrics translated into French.
Details of the number of songs by language of origin can be found in the table below:
| Original language | Number of songs |
|---|:---|
| en | 75786 |
| fr | 18486 |
| es | 1743 |
| it | 803 |
| de | 691 |
| sw | 529 |
| ko | 193 |
| id | 169 |
| pt | 142 |
| no | 122 |
| fi | 113 |
| sv | 70 |
| hr | 53 |
| so | 43 |
| ca | 41 |
| tl | 36 |
| ja | 35 |
| nl | 32 |
| ru | 29 |
| et | 27 |
| tr | 22 |
| ro | 19 |
| cy | 14 |
| vi | 14 |
| af | 13 |
| hu | 10 |
| sk | 10 |
| sl | 10 |
| cs | 7 |
| da | 6 |
| pl | 5 |
| sq | 4 |
| el | 4 |
| he | 3 |
| zh-cn | 2 |
| th | 1 |
| bg | 1 |
| ar | 1 | | 1,727 | [
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result-kand2-sdxl-wuerst-karlo/78fe0016 | 2023-10-03T01:21:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 351 | 2023-10-03T01:21:51 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1317
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "78fe0016"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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conll2002 | 2023-06-01T14:59:51.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"task_ids:part-of-speech",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:es",
"language:nl",
"license:unknown",
"region:us"
] | null | Named entities are phrases that contain the names of persons, organizations, locations, times and quantities.
Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-independent named entity recognition.
We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.
The participants of the shared task will be offered training and test data for at least two languages.
They will use the data for developing a named-entity recognition system that includes a machine learning component.
Information sources other than the training data may be used in this shared task.
We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
The train/validation/test sets are available in Spanish and Dutch.
For more details see https://www.clips.uantwerpen.be/conll2002/ner/ and https://www.aclweb.org/anthology/W02-2024/ | @inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://www.aclweb.org/anthology/W02-2024",
} | 3 | 350 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- es
- nl
license:
- unknown
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
paperswithcode_id: conll-2002
pretty_name: CoNLL-2002
dataset_info:
- config_name: es
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': AO
'1': AQ
'2': CC
'3': CS
'4': DA
'5': DE
'6': DD
'7': DI
'8': DN
'9': DP
'10': DT
'11': Faa
'12': Fat
'13': Fc
'14': Fd
'15': Fe
'16': Fg
'17': Fh
'18': Fia
'19': Fit
'20': Fp
'21': Fpa
'22': Fpt
'23': Fs
'24': Ft
'25': Fx
'26': Fz
'27': I
'28': NC
'29': NP
'30': P0
'31': PD
'32': PI
'33': PN
'34': PP
'35': PR
'36': PT
'37': PX
'38': RG
'39': RN
'40': SP
'41': VAI
'42': VAM
'43': VAN
'44': VAP
'45': VAS
'46': VMG
'47': VMI
'48': VMM
'49': VMN
'50': VMP
'51': VMS
'52': VSG
'53': VSI
'54': VSM
'55': VSN
'56': VSP
'57': VSS
'58': Y
'59': Z
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 6672173
num_examples: 8324
- name: validation
num_bytes: 1333784
num_examples: 1916
- name: test
num_bytes: 1294156
num_examples: 1518
download_size: 4140690
dataset_size: 9300113
- config_name: nl
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': Adj
'1': Adv
'2': Art
'3': Conj
'4': Int
'5': Misc
'6': N
'7': Num
'8': Prep
'9': Pron
'10': Punc
'11': V
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
splits:
- name: train
num_bytes: 5308959
num_examples: 15807
- name: validation
num_bytes: 994298
num_examples: 2896
- name: test
num_bytes: 1808862
num_examples: 5196
download_size: 3642241
dataset_size: 8112119
config_names:
- es
- nl
---
# Dataset Card for CoNLL-2002
## 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:** [homepage](https://www.clips.uantwerpen.be/conll2002/ner/)
- **Repository:** [github](https://github.com/teropa/nlp/tree/master/resources/corpora/conll2002)
- **Paper:** [paper](https://www.aclweb.org/anthology/W02-2024/)
- **Point of Contact:** [Erik Tjong Kim Sang](erikt@uia.ua.ac.be)
### Dataset Summary
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for at least two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. Information sources other than the training data may be used in this shared task. We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
### Supported Tasks and Leaderboards
Named Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English.
After 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used.
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
- `parsing`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A part-of-speech tag is correct only if it is equal to the corresponding tag in the data.
### Languages
There are two languages available : Spanish (es) and Dutch (nl).
## Dataset Structure
### Data Instances
The examples look like this :
```
{'id': '0',
'ner_tags': [5, 6, 0, 0, 0, 0, 3, 0, 0],
'pos_tags': [4, 28, 13, 59, 28, 21, 29, 22, 20],
'tokens': ['La', 'Coruña', ',', '23', 'may', '(', 'EFECOM', ')', '.']
}
```
The original data files within the Dutch sub-dataset have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
- `pos_tags`: the POS tags of each token
The POS tags correspond to this list for Spanish:
```
'AO', 'AQ', 'CC', 'CS', 'DA', 'DE', 'DD', 'DI', 'DN', 'DP', 'DT', 'Faa', 'Fat', 'Fc', 'Fd', 'Fe', 'Fg', 'Fh', 'Fia', 'Fit', 'Fp', 'Fpa', 'Fpt', 'Fs', 'Ft', 'Fx', 'Fz', 'I', 'NC', 'NP', 'P0', 'PD', 'PI', 'PN', 'PP', 'PR', 'PT', 'PX', 'RG', 'RN', 'SP', 'VAI', 'VAM', 'VAN', 'VAP', 'VAS', 'VMG', 'VMI', 'VMM', 'VMN', 'VMP', 'VMS', 'VSG', 'VSI', 'VSM', 'VSN', 'VSP', 'VSS', 'Y', 'Z'
```
And this list for Dutch:
```
'Adj', 'Adv', 'Art', 'Conj', 'Int', 'Misc', 'N', 'Num', 'Prep', 'Pron', 'Punc', 'V'
```
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the chunking task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC).
It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.
### Data Splits
For both configurations (Spanish and Dutch), there are three splits.
The original splits were named `train`, `testa` and `testb` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| | train | validation | test |
| ----- |-------:|------------:|------:|
| N. Examples (Spanish) | 8324 | 1916 | 1518 |
| N. Examples (Dutch) | 15807 | 2896 | 5196 |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce new resources to two languages that were under-served for statistical machine learning at the time, Dutch and Spanish.
[More Information Needed]
### Source Data
The Spanish data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.
The Dutch data consist of four editions of the Belgian newspaper "De Morgen" of 2000 (June 2, July 1, August 1 and September 1).
#### Initial Data Collection and Normalization
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
#### Who are the source language producers?
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
### Annotations
#### Annotation process
For the Dutch data, the annotator has followed the MITRE and SAIC guidelines for named entity recognition (Chinchor et al., 1999) as well as possible.
#### Who are the annotators?
The Spanish data annotation was carried out by the TALP Research Center of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC) of the University of Barcelona (UB).
The Dutch data was annotated as a part of the Atranos project at the University of Antwerp.
### Personal and Sensitive Information
The data is sourced from newspaper source and only contains mentions of public figures or individuals
## Considerations for Using the Data
### Social Impact of Dataset
Named Entity Recognition systems can be used to efficiently index news text, allowing to easily gather all information pertaining to an organization or individual. Making such resources widely available in languages other than English can support better research and user experience for a larger part of the world's population. At the same time, better indexing and discoverability can also enable surveillance by state actors.
### Discussion of Biases
News text reproduces the biases of society, and any system trained on news data should be cognizant of these limitations and the risk for models to learn spurious correlations in this context, for example between a person's gender and their occupation.
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
The annotation of the Spanish data was funded by the European Commission through the NAMIC project (IST-1999-12392).
### Licensing Information
The licensing status of the data, especially the news source text, is unknown.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://www.aclweb.org/anthology/W02-2024",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset. | 12,913 | [
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voidful/NMSQA | 2023-04-04T04:46:23.000Z | [
"task_categories:question-answering",
"task_categories:automatic-speech-recognition",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"language_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"speech-recognition",
"arxiv:2203.04911",
"region:us"
] | voidful | null | null | 7 | 350 | 2022-03-16T16:03:42 | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- expert-generated
- machine-generated
- crowdsourced
language:
- en
license: []
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- question-answering
- automatic-speech-recognition
task_ids:
- abstractive-qa
pretty_name: NMSQA
tags:
- speech-recognition
---
# Dataset Card for NMSQA(Natural Multi-speaker Spoken Question Answering)
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- Homepage:
https://github.com/DanielLin94144/DUAL-textless-SQA
- Repository:
https://github.com/DanielLin94144/DUAL-textless-SQA
- Paper:
https://arxiv.org/abs/2203.04911
- Leaderboard:
- Point of Contact:
Download audio data: [https://huggingface.co/datasets/voidful/NMSQA/resolve/main/nmsqa_audio.tar.gz](https://huggingface.co/datasets/voidful/NMSQA/resolve/main/nmsqa_audio.tar.gz)
Unzip audio data: `tar -xf nmsqa_audio.tar.gz`
### Dataset Summary
The Natural Multi-speaker Spoken Question Answering (NMSQA) dataset is designed for the task of textless spoken question answering. It is based on the SQuAD dataset and contains spoken questions and passages. The dataset includes the original text, transcriptions, and audio files of the spoken content. This dataset is created to evaluate the performance of models on textless spoken question answering tasks.
### Supported Tasks and Leaderboards
The primary task supported by this dataset is textless spoken question answering, where the goal is to answer questions based on spoken passages without relying on textual information. The dataset can also be used for automatic speech recognition tasks.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Each instance in the dataset contains the following fields:
- id: Unique identifier for the instance
- title: The title of the passage
- context: The passage text
- question: The question text
- - answer_start: The start index of the answer in the text
- audio_full_answer_end: The end position of the audio answer in seconds
- audio_full_answer_start: The start position of the audio answer in seconds
- audio_full_neg_answer_end: The end position of the audio answer in seconds for an incorrect answer with the same words
- audio_full_neg_answer_start: The start position of the audio answer in seconds for an incorrect answer with the same words
- audio_segment_answer_end: The end position of the audio answer in seconds for the segment
- audio_segment_answer_start: The start position of the audio answer in seconds for the segment
- text: The answer text
- content_segment_audio_path: The audio path for the content segment
- content_full_audio_path: The complete audio path for the content
- content_audio_sampling_rate: The audio sampling rate
- content_audio_speaker: The audio speaker
- content_segment_text: The segment text of the content
- content_segment_normalized_text: The normalized text for generating audio
- question_audio_path: The audio path for the question
- question_audio_sampling_rate: The audio sampling rate
- question_audio_speaker: The audio speaker
- question_normalized_text: The normalized text for generating audio
### Data Fields
The dataset includes the following data fields:
- id
- title
- context
- question
- answers
- content_segment_audio_path
- content_full_audio_path
- content_audio_sampling_rate
- content_audio_speaker
- content_segment_text
- content_segment_normalized_text
- question_audio_path
- question_audio_sampling_rate
- question_audio_speaker
- question_normalized_text
### Data Splits
The dataset is split into train, dev, and test sets.
## Dataset Creation
### Curation Rationale
The NMSQA dataset is created to address the challenge of textless spoken question answering, where the model must answer questions based on spoken passages without relying on textual information.
### Source Data
The NMSQA dataset is based on the SQuAD dataset, with spoken questions and passages created from the original text data.
#### Initial Data Collection and Normalization
The initial data collection involved converting the original SQuAD dataset's text-based questions and passages into spoken audio files. The text was first normalized, and then audio files were generated using text-to-speech methods.
#### Who are the source language producers?
The source language producers are the creators of the SQuAD dataset and the researchers who generated the spoken audio files for the NMSQA dataset.
### Annotations
#### Annotation process
The annotations for the NMSQA dataset are derived from the original SQuAD dataset. Additional annotations, such as audio start and end positions for correct and incorrect answers, as well as audio file paths and speaker information, are added by the dataset creators.
#### Who are the annotators?
The annotators for the NMSQA dataset are the creators of the SQuAD dataset and the researchers who generated the spoken audio files and additional annotations for the NMSQA dataset.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information.
## Considerations for Using the Data
### Social Impact of Dataset
The NMSQA dataset contributes to the development and evaluation of models for textless spoken question answering tasks, which can lead to advancements in natural language processing and automatic speech recognition. Applications of these technologies can improve accessibility and convenience in various domains, such as virtual assistants, customer service, and voice-controlled devices.
### Discussion of Biases
The dataset inherits potential biases from the original SQuAD dataset, which may include biases in the selection of passages, questions, and answers. Additionally, biases may be introduced in the text-to-speech process and the choice of speakers used to generate the spoken audio files.
### Other Known Limitations
As the dataset is based on the SQuAD dataset, it shares the same limitations, including the fact that it is limited to the English language and mainly focuses on factual questions. Furthermore, the dataset may not cover a wide range of accents, dialects, or speaking styles.
## Additional Information
### Dataset Curators
The NMSQA dataset is curated by Guan-Ting Lin, Yung-Sung Chuang, Ho-Lam Chung, Shu-Wen Yang, Hsuan-Jui Chen, Shang-Wen Li, Abdelrahman Mohamed, Hung-Yi Lee, and Lin-Shan Lee.
### Licensing Information
The licensing information for the dataset is not explicitly mentioned.
### Citation Information
```css
@article{lin2022dual,
title={DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning},
author={Lin, Guan-Ting and Chuang, Yung-Sung and Chung, Ho-Lam and Yang, Shu-wen and Chen, Hsuan-Jui and Li, Shang-Wen and Mohamed, Abdelrahman and Lee, Hung-yi and Lee, Lin-shan},
journal={arXiv preprint arXiv:2203.04911},
year={2022}
}
```
### Contributions
Thanks to [@voidful](https://github.com/voidful) for adding this dataset. | 8,140 | [
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x_stance | 2023-04-05T13:45:10.000Z | [
"task_categories:text-classification",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:de",
"language:en",
"language:fr",
"language:it",
"license:cc-by-nc-4.0",
"stance-detection",
"arxiv:2003.08385",
"region:us"
] | null | The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions.
It can be used to train and evaluate stance detection systems. | @inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \\& 16th Conference on Natural Language Processing (KONVENS)",
address = "Zurich, Switzerland",
year = "2020",
month = "jun",
url = "http://ceur-ws.org/Vol-2624/paper9.pdf"
} | 4 | 349 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- de
- en
- fr
- it
language_creators:
- found
license:
- cc-by-nc-4.0
multilinguality:
- multilingual
pretty_name: x-stance
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: x-stance
tags:
- stance-detection
dataset_info:
features:
- name: question
dtype: string
- name: id
dtype: int32
- name: question_id
dtype: int32
- name: language
dtype: string
- name: comment
dtype: string
- name: label
dtype: string
- name: numerical_label
dtype: int32
- name: author
dtype: string
- name: topic
dtype: string
splits:
- name: train
num_bytes: 17619123
num_examples: 45640
- name: test
num_bytes: 6607134
num_examples: 17705
- name: validation
num_bytes: 1505979
num_examples: 3926
download_size: 6410801
dataset_size: 25732236
---
# Dataset Card for "x_stance"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/ZurichNLP/xstance
- **Paper:** [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://arxiv.org/abs/2003.08385)
- **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:** 6.41 MB
- **Size of the generated dataset:** 25.73 MB
- **Total amount of disk used:** 32.14 MB
### Dataset Summary
The x-stance dataset contains more than 150 political questions, and 67k comments written by candidates on those questions.
It can be used to train and evaluate stance detection systems.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
The comments are partly German, partly French and Italian. The questions are available in all the three languages plus English.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 6.41 MB
- **Size of the generated dataset:** 25.73 MB
- **Total amount of disk used:** 32.14 MB
An example of 'train' looks as follows.
```
{
"author": "f27b54a137b4",
"comment": "Das Arbeitsgesetz regelt die Arbeitszeiten und schützt den Arbeitnehmer. Es macht doch Sinn, dass wenn eine Nachfrage besteht, die Läden öffnen dürfen und wenn es keine Nachfrage gibt, diese geschlossen bleiben.",
"id": 10045,
"label": "FAVOR",
"language": "de",
"numerical_label": 100,
"question": "Sind Sie für eine vollständige Liberalisierung der Geschäftsöffnungszeiten (Geschäfte können die Öffnungszeiten nach freiem Ermessen festlegen)?",
"question_id": 739,
"topic": "Economy"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `question`: a `string` feature.
- `id`: a `int32` feature.
- `question_id`: a `int32` feature.
- `language`: a `string` feature.
- `comment`: a `string` feature.
- `label`: a `string` feature.
- `numerical_label`: a `int32` feature.
- `author`: a `string` feature.
- `topic`: a `string` feature.
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|45640| 3926|17705|
## 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
The data have been extracted from the Swiss voting advice platform Smartvote.ch.
#### 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 licensed under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{vamvas2020xstance,
author = "Vamvas, Jannis and Sennrich, Rico",
title = "{X-Stance}: A Multilingual Multi-Target Dataset for Stance Detection",
booktitle = "Proceedings of the 5th Swiss Text Analytics Conference (SwissText) \& 16th Conference on Natural Language Processing (KONVENS)",
address = "Zurich, Switzerland",
year = "2020",
month = "jun",
url = "http://ceur-ws.org/Vol-2624/paper9.pdf"
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@jvamvas](https://github.com/jvamvas) for adding this dataset. | 7,013 | [
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nbertagnolli/counsel-chat | 2023-06-17T17:55:38.000Z | [
"region:us"
] | nbertagnolli | null | null | 9 | 348 | 2023-02-07T03:51:53 | # Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage: https://towardsdatascience.com/counsel-chat-bootstrapping-high-quality-therapy-data-971b419f33da**
- **Repository: https://github.com/nbertagnolli/counsel-chat**
- **Paper: https://towardsdatascience.com/counsel-chat-bootstrapping-high-quality-therapy-data-971b419f33da**
- **Leaderboard: NA**
- **Point of Contact: nbertagnolli**
### Dataset Summary
Scrape of Counselchat.com's forum. CounselChat.com is an example of an expert community.
It is a platform to help counselors build their reputation and make meaningful contact with potential clients.
On the site, therapists respond to questions posed by clients, and users can like responses that they find most helpful.
It’s a nice idea and lends itself to some interesting data. This data contains expert responses by licensed clinicialns
to questions posed by individuals.
### Supported Tasks and Leaderboards
NA
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
* questionID — A unique question identifier which is distinct for every question
* questionTitle — The title of the question on counsel chat
* questionText — The body of the individual’s question to counselors
* questionLink — A URL to the last location of that question (might not still be active)
* topic — The topic the question was listed under
* therapistInfo — The summary of each therapist, usually a name and specialty
* therapistURL — a link to the therapist’s bio on counselchat
* answerText — The therapist response to the question
* upvotes — The number of upvotes the answerText received
* split — The data split for training, validation, and testing.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
There is a lack of high quality open source mental health data available for study in NLP. Most datasets revolve around
forums like Reddit, which can provide great insights, but don't capture the type of language often used by counselors.
This dataset seeks to help bridge that gap and provide some additional data of counselors interacting with patients in
need.
### Source Data
The dataset was scraped on 20220401 from counselchat.com.
#### Initial Data Collection and Normalization
The dataset was scraped on 20220401 from counselchat.com. The data is in it's raw form and has not been normalized.
#### Who are the source language producers?
The text was written by licensed counselors in the United States and annonymous individuals.
### Annotations
The dataset does not contain any additional annotations.
### Personal and Sensitive Information
This data is not anonymized, so individuals' names can be found in the dataset. CounselChat.com allows therapists to advertise for their clinics
by providing sound publicly available advise. The therapist names have been kept as part of the original dataset.
## 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
nicolas bertagnolli
### Licensing Information
MIT
### Citation Information
Bertagnolli, N. Counsel Chat: Bootstrapping High-Quality Therapy Data. Available online: https://towardsdatascience.com/counsel-chat-bootstrapping-high-quality-therapy-data-971b419f33da
```
@misc{bertagnolli2020counsel,
title={Counsel chat: Bootstrapping high-quality therapy data},
author={Bertagnolli, Nicolas},
year={2020},
publisher={Towards Data Science. https://towardsdatascience. com/counsel-chat~…}
}
```
### Contributions
Thanks to [@nbertagnolli](https://github.com/nbertagnolli) for adding this dataset.
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teven/enwiki_100k | 2023-04-03T17:16:55.000Z | [
"region:us"
] | teven | null | null | 1 | 348 | 2023-04-03T17:13:51 | ---
dataset_info:
features:
- name: metadata
dtype: string
- name: text
dtype: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 2570893740
num_examples: 1000000
download_size: 1550572660
dataset_size: 2570893740
---
# Dataset Card for "enwiki_100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 433 | [
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GATE-engine/medical_decathlon | 2023-06-28T00:08:47.000Z | [
"region:us"
] | GATE-engine | null | null | 0 | 348 | 2023-06-27T04:48:55 | ---
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---
# Dataset Card for "medical_decathlon"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 5,144 | [
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nielsr/cord-layoutlmv3 | 2022-05-02T16:41:30.000Z | [
"region:us"
] | nielsr | https://github.com/clovaai/cord/ | @article{park2019cord,
title={CORD: A Consolidated Receipt Dataset for Post-OCR Parsing},
author={Park, Seunghyun and Shin, Seung and Lee, Bado and Lee, Junyeop and Surh, Jaeheung and Seo, Minjoon and Lee, Hwalsuk}
booktitle={Document Intelligence Workshop at Neural Information Processing Systems}
year={2019}
} | 2 | 347 | 2022-05-02T16:37:54 | Entry not found | 15 | [
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hakurei/open-instruct-v1 | 2023-04-17T03:03:13.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"region:us"
] | hakurei | null | null | 87 | 346 | 2023-04-04T23:10:41 | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- 100K<n<1M
---
# Open Instruct V1 - A dataset for having LLMs follow instructions.
Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training.
## Dataset Breakdown
| Dataset | Amount of Samples |
|----------------|-------------------|
| [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 51759 |
| [Self Instruct](https://github.com/yizhongw/self-instruct) | 82599 |
| [GPT-4 Instruct](https://github.com/teknium1/GPTeacher) | 18194 |
| [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) | 18019 |
| [Dolly](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) | 15015 |
| [Synthetic](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | 33143 |
| [Roleplay](https://github.com/teknium1/GPTeacher) | 3146 |
| [asss](https://huggingface.co/datasets/HuggingFaceH4/asss) | 448 |
| [instruction-dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) | 327 |
| Total | 222650 |
| 1,181 | [
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cedr | 2023-01-25T14:27:50.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-label-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:ru",
"license:apache-2.0",
"emotion-classification",
"region:us"
] | null | This new dataset is designed to solve emotion recognition task for text data in Russian. The Corpus for Emotions Detecting in
Russian-language text sentences of different social sources (CEDR) contains 9410 sentences in Russian labeled for 5 emotion
categories. The data collected from different sources: posts of the LiveJournal social network, texts of the online news
agency Lenta.ru, and Twitter microblog posts. There are two variants of the corpus: main and enriched. The enriched variant
is include tokenization and lemmatization. Dataset with predefined train/test splits. | @article{sboev2021data,
title={Data-Driven Model for Emotion Detection in Russian Texts},
author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
journal={Procedia Computer Science},
volume={190},
pages={637--642},
year={2021},
publisher={Elsevier}
} | 4 | 345 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- ru
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-label-classification
pretty_name: The Corpus for Emotions Detecting in Russian-language text sentences
(CEDR)
tags:
- emotion-classification
dataset_info:
- config_name: main
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': joy
'1': sadness
'2': surprise
'3': fear
'4': anger
- name: source
dtype: string
splits:
- name: train
num_bytes: 1418355
num_examples: 7528
- name: test
num_bytes: 350275
num_examples: 1882
download_size: 693026
dataset_size: 1768630
- config_name: enriched
features:
- name: text
dtype: string
- name: labels
sequence:
class_label:
names:
'0': joy
'1': sadness
'2': surprise
'3': fear
'4': anger
- name: source
dtype: string
- name: sentences
list:
list:
- name: forma
dtype: string
- name: lemma
dtype: string
splits:
- name: train
num_bytes: 4792366
num_examples: 7528
- name: test
num_bytes: 1182343
num_examples: 1882
download_size: 1822522
dataset_size: 5974709
---
# Dataset Card for [cedr]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [GitHub](https://github.com/sag111/CEDR)
- **Repository:** [GitHub](https://github.com/sag111/CEDR)
- **Paper:** [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1877050921013247)
- **Leaderboard:**
- **Point of Contact:** [@sag111](mailto:sag111@mail.ru)
### Dataset Summary
The Corpus for Emotions Detecting in Russian-language text sentences of different social sources (CEDR) contains 9410 comments labeled for 5 emotion categories (joy, sadness, surprise, fear, and anger).
Here are 2 dataset configurations:
- "main" - contains "text", "labels", and "source" features;
- "enriched" - includes all "main" features and "sentences".
Dataset with predefined train/test splits.
### Supported Tasks and Leaderboards
This dataset is intended for multi-label emotion classification.
### Languages
The data is in Russian.
## Dataset Structure
### Data Instances
Each instance is a text sentence in Russian from several sources with one or more emotion annotations (or no emotion at all).
An example for an instance from the dataset is shown below:
```
{
'text': 'Забавно как люди в возрасте удивляются входящим звонкам на мобильник)',
'labels': [0],
'source': 'twitter',
'sentences': [
[
{'forma': 'Забавно', 'lemma': 'Забавно'},
{'forma': 'как', 'lemma': 'как'},
{'forma': 'люди', 'lemma': 'человек'},
{'forma': 'в', 'lemma': 'в'},
{'forma': 'возрасте', 'lemma': 'возраст'},
{'forma': 'удивляются', 'lemma': 'удивляться'},
{'forma': 'входящим', 'lemma': 'входить'},
{'forma': 'звонкам', 'lemma': 'звонок'},
{'forma': 'на', 'lemma': 'на'},
{'forma': 'мобильник', 'lemma': 'мобильник'},
{'forma': ')', 'lemma': ')'}
]
]
}
```
Emotion label codes: {0: "joy", 1: "sadness", 2: "surprise", 3: "fear", 4: "anger"}
### Data Fields
The main configuration includes:
- text: the text of the sentence;
- labels: the emotion annotations;
- source: the tag name of the corresponding source
In addition to the above, the raw data includes:
- sentences: text tokenized and lemmatized with [udpipe](https://ufal.mff.cuni.cz/udpipe)
- 'forma': the original word form;
- 'lemma': the lemma of this word
### Data Splits
The dataset includes a set of train/test splits.
with 7528, and 1882 examples respectively.
## Dataset Creation
### Curation Rationale
The formed dataset of examples consists of sentences in Russian from several sources (blogs, microblogs, news), which allows creating methods to analyse various types of texts. The created methodology for building the dataset based on applying a crowdsourcing service can be used to expand the number of examples to improve the accuracy of supervised classifiers.
### Source Data
#### Initial Data Collection and Normalization
Data was collected from several sources: posts of the Live Journal social network, texts of the online news agency Lenta.ru, and Twitter microblog posts.
Only those sentences were selected that contained marker words from the dictionary of [the emotive vocabulary of the Russian language](http://lexrus.ru/default.aspx?p=2876). The authors manually formed a list of marker words for each emotion by choosing words from different categories of the dictionary.
In total, 3069 sentences were selected from LiveJournal posts, 2851 sentences from Lenta.Ru, and 3490 sentencesfrom Twitter. After selection, sentences were offered to annotators for labeling.
#### Who are the source language producers?
Russian-speaking LiveJournal and Tweeter users, and authors of news articles on the site lenta.ru.
### Annotations
#### Annotation process
Annotating sentences with labels of their emotions was performed with the help of [a crowdsourcing platform](https://yandex.ru/support/toloka/index.html?lang=en).
The annotators’ task was: “What emotions did the author express in the sentence?”. The annotators were allowed to put an arbitrary number of the following emotion labels: "joy", "sadness", "anger", "fear", and "surprise".
If the accuracy of an annotator on the control sentences (including the trial run) became less than 70%, or if the accuracy was less than 66% over the last six control samples, the annotator was dismissed.
Sentences were split into tasks and assigned to annotators so that each sentence was annotated at least three times. A label of a specific emotion was assigned to a sentence if put by more than half of the annotators.
#### Who are the annotators?
Only those of the 30% of the best-performing active users (by the platform’s internal rating) who spoke Russian and were over 18 years old were allowed into the annotation process. Moreover, before a platform user could be employed as an annotator, they underwent a training task, after which they were to mark 25 trial samples with more than 80% agreement compared to the annotation that the authors had performed themselves.
### Personal and Sensitive Information
The text of the sentences may contain profanity.
## 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
Researchers at AI technology lab at NRC "Kurchatov Institute". See the author [list](https://www.sciencedirect.com/science/article/pii/S1877050921013247).
### Licensing Information
The GitHub repository which houses this dataset has an Apache License 2.0.
### Citation Information
If you have found our results helpful in your work, feel free to cite our publication. This is an updated version of the dataset, the collection and preparation of which is described here:
```
@article{sboev2021data,
title={Data-Driven Model for Emotion Detection in Russian Texts},
author={Sboev, Alexander and Naumov, Aleksandr and Rybka, Roman},
journal={Procedia Computer Science},
volume={190},
pages={637--642},
year={2021},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@naumov-al](https://github.com/naumov-al) for adding this dataset. | 8,739 | [
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multilingual_librispeech | 2022-11-18T21:31:47.000Z | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:de",
"language:es",
"language:fr",
"language:it",
"language:nl",
"language:pl",
"language:pt",
"license:cc-by-4.0",
"arxiv:2012.03411",
"region:us"
] | null | Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. | @article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
} | 7 | 345 | 2022-03-02T23:29:22 | ---
pretty_name: MultiLingual LibriSpeech
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- de
- es
- fr
- it
- nl
- pl
- pt
license:
- cc-by-4.0
multilinguality:
- multilingual
paperswithcode_id: librispeech-1
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- speaker-identification
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---
# Dataset Card for MultiLingual LibriSpeech
## 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> This legacy dataset doesn't support streaming and is not updated. Use "facebook/multilingual_librispeech" instead.</p>
</div>
Multilingual LibriSpeech (MLS) dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `audio-speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'chapter_id': 141231,
'file': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'audio': {'path': '/home/patrick/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/dev_clean/1272/141231/1272-141231-0000.flac',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 16000},
'id': '1272-141231-0000',
'speaker_id': 1272,
'text': 'A MAN SAID TO THE UNIVERSE SIR I EXIST'}
```
### Data Fields
- file: A path to the downloaded audio file in .flac format.
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 11,627 | [
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] |
squad_v1_pt | 2023-04-05T13:40:41.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:mit",
"arxiv:1606.05250",
"region:us"
] | null | Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API. | @article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
} | 6 | 345 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- pt
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: SquadV1Pt
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 85323237
num_examples: 87599
- name: validation
num_bytes: 11265474
num_examples: 10570
download_size: 39532595
dataset_size: 96588711
---
# Dataset Card for "squad_v1_pt"
## 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://github.com/nunorc/squad-v1.1-pt](https://github.com/nunorc/squad-v1.1-pt)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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:** 39.53 MB
- **Size of the generated dataset:** 96.72 MB
- **Total amount of disk used:** 136.25 MB
### Dataset Summary
Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.
### 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
#### default
- **Size of downloaded dataset files:** 39.53 MB
- **Size of the generated dataset:** 96.72 MB
- **Total amount of disk used:** 136.25 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [0],
"text": ["Saint Bernadette Soubirous"]
},
"context": "\"Arquitetonicamente, a escola tem um caráter católico. No topo da cúpula de ouro do edifício principal é uma estátua de ouro da ...",
"id": "5733be284776f41900661182",
"question": "A quem a Virgem Maria supostamente apareceu em 1858 em Lourdes, na França?",
"title": "University_of_Notre_Dame"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | validation |
| ------- | ----: | ---------: |
| default | 87599 | 10570 |
## 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{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. | 6,868 | [
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result-kand2-sdxl-wuerst-karlo/040dec0a | 2023-10-03T08:51:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 345 | 2023-10-03T08:51:40 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 160
num_examples: 10
download_size: 1292
dataset_size: 160
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "040dec0a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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result-kand2-sdxl-wuerst-karlo/f4d8fc49 | 2023-10-03T08:54:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 345 | 2023-10-03T08:54:44 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 159
num_examples: 10
download_size: 1306
dataset_size: 159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f4d8fc49"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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anon8231489123/ShareGPT_Vicuna_unfiltered | 2023-04-12T05:23:59.000Z | [
"language:en",
"license:apache-2.0",
"region:us"
] | anon8231489123 | null | null | 587 | 344 | 2023-04-02T05:30:31 | ---
license: apache-2.0
language:
- en
---
**Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.**
**Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c**
Two choices:
- Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
- Has instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split.json
The choice is yours. The first dataset may go to far and remove valuable data. The second is better for when the AI asks for clarification, but it also may refuse to do stuff like browse the internet, which it actually may be able to do with certain langchain implementations. These are important things to think about before training.
~100k ShareGPT conversations narrowed down to 53k by:
* Removing non-english conversations
* Removing excessive unicode (indicative of Chinese or Korean text, usually)
* Removing excessive repeated characters
* Removing various instances "AI Moralizing". Conversations with these phrases were removed (and a few others that can't be mentioned here):
"text-based AI language model",
"domestic violence",
"please refrain",
"derogatory",
"inappropriate",
"offensive",
"racism",
"racist",
"racial",
"discriminate",
"discriminatory",
"discrimination",
"sexist",
"sexism",
"unacceptable",
"inclusive workplace",
"lgbt",
"morals",
"ethics",
"ethical",
"legality",
"illegal",
"illegality",
"hateful",
"harmful",
"it is never okay",
"It is important to",
"It's important to",
"real-world consequences",
"hate speech",
"glorify",
"not be appropriate",
"supremacist",
"extremist",
"responsible AI",
"AI principles",
"AI assistant",
"an AI language",
"ableist",
"hurtful",
"gender stereotype",
"gender inequality",
"underrepresentation",
"safe spaces",
"gender-based",
"inclusivity",
"feminist",
"feminism",
"transgender",
"empowerment",
"communist",
"capitalism",
"stereotypes",
"biases",
"bias",
"Microaggression",
"prioritize human safety",
"as a language model",
"as an AI language model",
"As a large language model",
"As an AI",
"ethical principles",
"consensual",
"it is not appropriate",
"it's not appropriate",
"I cannot fulfill your request",
"harmful to human beings",
"ethical guidelines",
"my guidelines",
"prioritize user safety",
"adhere to ethical guidelines",
"harmful consequences",
"potentially harmful",
"dangerous activities",
"promote safety",
"well-being of all users",
"responsible information sharing",
"jeopardize the safety",
"illegal actions or intentions",
"undermine the stability",
"promote the well-being",
"illegal activities or actions",
"adherence to the law",
"potentially be harmful",
"illegal substances or activities",
"committed to promoting",
"safe information",
"lawful information",
"cannot provide guidance",
"cannot provide information",
"unable to offer assistance",
"cannot engage in discussions",
"programming prohibits",
"follow ethical guidelines",
"ensure the safety",
"involves an illegal subject",
"prioritize safety",
"illegal subject",
"prioritize user well-being",
"cannot support or promote",
"activities that could harm",
"pose a risk to others",
"against my programming",
"activities that could undermine",
"potentially dangerous",
"not within the scope",
"designed to prioritize safety",
"not able to provide",
"maintain user safety",
"adhere to safety guidelines",
"dangerous or harmful",
"cannot provide any information",
"focus on promoting safety"
* Conversations split into 2048 token chunks as described here: https://github.com/lm-sys/FastChat/blob/main/docs/commands/data_cleaning.md
This should be fully ready to train an unfiltered english Vicuna model based on the procedure here: https://github.com/lm-sys/FastChat/ | 4,413 | [
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shibing624/medical | 2023-06-02T07:03:41.000Z | [
"task_categories:text-generation",
"size_categories:1M<n<10M",
"language:zh",
"language:en",
"license:apache-2.0",
"text-generation",
"region:us"
] | shibing624 | 纯文本数据，中文医疗数据集，包含预训练数据的百科数据，指令微调数据和奖励模型数据。 | null | 151 | 344 | 2023-05-22T14:45:06 | ---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation
pretty_name: medical
task_categories:
- text-generation
size_categories:
- 1M<n<10M
---
# Dataset Card for medical
中文医疗数据集
- LLM Supervised Finetuning repository: https://github.com/shibing624/textgen
- MeidcalGPT repository: https://github.com/shibing624/MedicalGPT
## Dataset Description
medical is a Chinese Medical dataset. 医疗数据集，可用于医疗领域大模型训练。
```
tree medical
|-- finetune # 监督微调数据集，可用于SFT和RLHF
| |-- test_en_1.json
| |-- test_zh_0.json
| |-- train_en_1.json
| |-- train_zh_0.json
| |-- valid_en_1.json
| `-- valid_zh_0.json
|-- medical.py # hf dataset 数据展示用
|-- pretrain # 二次预训练数据集
| |-- medical_book_zh.json
| |-- test_encyclopedia.json
| |-- train_encyclopedia.json
| `-- valid_encyclopedia.json
|-- README.md
`-- reward # 奖励模型数据集
|-- test.json
|-- train.json
`-- valid.json
```
### Original Dataset Summary
#### pretrain
- train_encyclopedia.json: 共36万条，来自医疗百科数据[FreedomIntelligence/huatuo_encyclopedia_qa](https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa) , 拼接 questions 和 answers，形成 text 文本字段，语句通顺，用于预训练注入医疗知识。
- medical_book_zh.json: 共8475条，来自医疗教材的文本数据，来源：https://github.com/jind11/MedQA， 原始数据集：[google drive](https://drive.google.com/u/0/uc?export=download&confirm=t&id=1ImYUSLk9JbgHXOemfvyiDiirluZHPeQw) ，只对长段落切分为2048字的小段落了。
#### finetune
- train_zh_0.json: 共195万条，来自1）中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的六个科室医疗问诊数据，
有79万条；2）在线医疗百科 huatuo_encyclopedia_qa ，有36万条；3）医疗知识图谱 huatuo_knowledge_graph_qa，有79万条。三部分合并，共195万条。
- train_en_1.json：共11万条，来自英文医疗问诊对话数据[Kent0n-Li/ChatDoctor](https://github.com/Kent0n-Li/ChatDoctor)，合并了HealthCareMagic-100k、GenMedGPT-5k 数据集，共11万条。
#### reward
- train.json 共4000条，问题来自中文医疗对话数据集[Toyhom/Chinese-medical-dialogue-data](https://github.com/Toyhom/Chinese-medical-dialogue-data)的随机4000条提问，`response_chosen`来自该数据集的医生答复，
`response_rejected`来自本草模型[SCIR-HI/Huatuo-Llama-Med-Chinese](https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese)的答复。
### Supported Tasks and Leaderboards
中文医疗对话模型
The dataset designed for medical task training pretrained language models.
### Languages
The data are in Chinese.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
head pretrain/train_encyclopedia.json
```json
{"text": "怀孕后嘴巴很淡怎么办？有孕妇在怀孕之后，发现自己嘴巴比较淡，出现这种情况的原因其实也非常的复杂，首先和妊娠反应有直接的关系，这是一种正常的情况，另外有些孕妇平常不注意口腔卫生，舌苔比较厚或者自己有了一些消化系统方面的疾病，这就要求人们必须要及时的进行处理。女性在怀孕之后，身体就会出现一些明显的变化，首先人们月经会停止，另外也会有恶心、呕吐等一些妊娠反应，不过这些都是正常的。有些孕妇发现自己在怀孕之后，口味发生了很大的变化，嘴巴变得非常的淡。其实这也和激素变化有直接的关系，可能是妊娠反应所致，在怀孕期间，因为受到体内激素水平的变化，所以就会有肠胃系统的改变，人们可能会出现食欲不振，消化不良等症状表现，也有一些孕妇会发现自己嘴巴没有味道，会有口苦的症状，而这也是正常的孕期反应，人们在平常要多喝一些水，多吃一些清淡营养的食物慢慢就会改善。也有可能是舌苔过厚所致，孕妇嘴巴里面没有味道，很有可能是舌苔比较重、舌苔过厚导致的，这样就会影响到味蕾对味道的敏感度，不仅嘴巴里面没有味道，甚至有些孕妇在说话的时候也会有口臭，这就要求人们在每天早上漱口的时候，必须要用牙刷刷舌苔开始，不要清理的特别深，以免人们会有呕吐，慢慢习惯之后再往深一点的清洗，一般2到3天就会得到改善。嘴巴感到比较淡，其实也和脾胃虚寒有直接的关系，消化系统疾病，内分泌疾病，营养不良等，但有可能导致舌头部位因为味蕾的敏感度下降，产生口淡之感，患者会有食欲不振的表现，发现病症及时就诊治疗。"}
```
head finetune/train_zh_0.json
```json
{"instruction": "曲匹地尔片的用法用量", "input": "", "output": "注意：同种药品可由于不同的包装规格有不同的用法或用量。本文只供参考。如果不确定，请参看药品随带的说明书或向医生询问。口服。一次50～100mg（1-2片），3次/日，或遵医嘱。"}
```
head finetune/train_en_1.json
```json
{"instruction": "If you are a doctor, please answer the medical questions based on the patient's description.", "input": "Doctor, I have been experiencing sudden and frequent panic attacks. I don't know what to do.", "output": "Well, based on what you're telling me, it sounds like you may be suffering from panic disorder. The best course of action is to start with psychotherapy and mental health counseling. Additionally, we should conduct an electrocardiogram to make sure that there are no physical issues causing your panic attacks. We will also need to perform a depression screen and a toxicology screen to rule out any other underlying causes. Finally, I would recommend a comprehensive psychological and psychiatric evaluation and therapy to help manage your symptoms."}
```
head reward/train.json
```json
{"question": "孩子感统失调，学习不行怎么办？，孩子说话晚，走路晚，现在小学二年级，学习跟不上，理解力差，运动协调性差，家里很着急，不知怎么办。", "response_chosen": "病情分析：你好!孩子说话晚，走路也晚，很可能是大脑本身发育不好引起的发育迟缓。而五岁时所致的智力检查为临界范围，那就是说孩子的智商是有问题的，也应考虑与大脑发育不好有关。指导意见：人的大脑在头一年发育最快，可塑性最强，在头三年可塑性还是可以的，超过三岁再进行训练，效果就不怎么好了。建议再给孩子做一做智力测试，如果孩子的智商还是在临界范围，那就要考虑让孩子去特殊学校进行康复训练，而不是继续在普通小学就读，否则对孩子来说，就是强人所难了。希望自己的孩子能聪明，这是每个家长都会有的心愿，但如果孩子自身的条件就是不能跟上同龄孩子，那家长也要面对这个事实的，对吗？医生询问：", "response_rejected": "建议家长先带孩子去正规医院做全面检查以确定病因和病情严重程度；同时可以进行物理治疗、康复训练等辅助治疗方法。"}
```
### Data Fields
#### 预训练数据集 pretrain
字段解释：
- text: 文本
#### 指令微调数据集 finetune
字段解释：
- instruction: 指令
- input：问题（可为空）
- output：答复
#### 奖励模型数据集 reward
字段解释：
- question: 问题
- response_chosen: 优质回答
- response_rejected: 低质回答
### Data Splits
```
> wc -l medical/*/*
500 medical/finetune/test_en_1.json
500 medical/finetune/test_zh_0.json
116617 medical/finetune/train_en_1.json
1949972 medical/finetune/train_zh_0.json
500 medical/finetune/valid_en_1.json
500 medical/finetune/valid_zh_0.json
8475 medical/pretrain/medical_book_zh.json
500 medical/pretrain/test_encyclopedia.json
361420 medical/pretrain/train_encyclopedia.json
500 medical/pretrain/valid_encyclopedia.json
100 medical/reward/test.json
3800 medical/reward/train.json
100 medical/reward/valid.json
2443484 total
```
### Licensing Information
The dataset is available under the Apache 2.0.
### Citation Information
- https://github.com/Toyhom/Chinese-medical-dialogue-data
- https://github.com/FreedomIntelligence/Huatuo-26M/blob/main/README_zh-CN.md
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_encyclopedia_qa
- https://huggingface.co/datasets/FreedomIntelligence/huatuo_knowledge_graph_qa
- https://github.com/Kent0n-Li/ChatDoctor
附上几个优质的reward model dataset:
- https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise
- https://huggingface.co/datasets/sunzeyeah/chinese_chatgpt_corpus
- https://huggingface.co/datasets/Cohere/miracl-zh-queries-22-12
- https://huggingface.co/datasets/Dahoas/rm-static
### Contributions
[shibing624](https://github.com/shibing624) 整理并上传
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gtfintechlab/fomc_communication | 2023-09-12T21:18:49.000Z | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"finance",
"region:us"
] | gtfintechlab | null | null | 1 | 344 | 2023-09-12T21:00:59 | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- finance
size_categories:
- 1K<n<10K
---
## Citation and Contact Information
### Cite
Please cite our paper if you use any code, data, or models.
```c
@inproceedings{shah-etal-2023-trillion,
title = "Trillion Dollar Words: A New Financial Dataset, Task {\&} Market Analysis",
author = "Shah, Agam and
Paturi, Suvan and
Chava, Sudheer",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.368",
doi = "10.18653/v1/2023.acl-long.368",
pages = "6664--6679",
abstract = "Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major driver of financial market returns. We construct the largest tokenized and annotated dataset of FOMC speeches, meeting minutes, and press conference transcripts in order to understand how monetary policy influences financial markets. In this study, we develop a novel task of hawkish-dovish classification and benchmark various pre-trained language models on the proposed dataset. Using the best-performing model (RoBERTa-large), we construct a measure of monetary policy stance for the FOMC document release days. To evaluate the constructed measure, we study its impact on the treasury market, stock market, and macroeconomic indicators. Our dataset, models, and code are publicly available on Huggingface and GitHub under CC BY-NC 4.0 license.",
}
```
### Contact Information
Please contact Agam Shah (ashah482[at]gatech[dot]edu) for any issues and questions.
GitHub: [@shahagam4](https://github.com/shahagam4)
Website: [https://shahagam4.github.io/](https://shahagam4.github.io/) | 1,924 | [
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result-kand2-sdxl-wuerst-karlo/6a3f723d | 2023-10-03T08:48:05.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 344 | 2023-10-03T08:48:04 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1317
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6a3f723d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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bigbio/quaero | 2022-12-22T15:46:29.000Z | [
"multilinguality:monolingual",
"language:fr",
"license:other",
"region:us"
] | bigbio | The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].
A selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS):
1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy, Chemical and Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology, Procedures.
2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051)
The QUAERO French Medical Corpus BioC release comprises a subset of the QUAERO French Medical corpus, as follows:
Training data (BRAT version used in CLEF eHealth 2015 task 1b as training data):
- MEDLINE_train_bioc file: 833 MEDLINE titles, annotated with normalized entities in the BioC format
- EMEA_train_bioc file: 3 EMEA documents, segmented into 11 sub-documents, annotated with normalized entities in the BioC format
Development data (BRAT version used in CLEF eHealth 2015 task 1b as test data and in CLEF eHealth 2016 task 2 as development data):
- MEDLINE_dev_bioc file: 832 MEDLINE titles, annotated with normalized entities in the BioC format
- EMEA_dev_bioc file: 3 EMEA documents, segmented into 12 sub-documents, annotated with normalized entities in the BioC format
Test data (BRAT version used in CLEF eHealth 2016 task 2 as test data):
- MEDLINE_test_bioc folder: 833 MEDLINE titles, annotated with normalized entities in the BioC format
- EMEA folder_test_bioc: 4 EMEA documents, segmented into 15 sub-documents, annotated with normalized entities in the BioC format
This release of the QUAERO French medical corpus, BioC version, comes in the BioC format, through automatic conversion from the original BRAT format obtained with the Brat2BioC tool https://bitbucket.org/nicta_biomed/brat2bioc developped by Jimeno Yepes et al.
Antonio Jimeno Yepes, Mariana Neves, Karin Verspoor
Brat2BioC: conversion tool between brat and BioC
BioCreative IV track 1 - BioC: The BioCreative Interoperability Initiative, 2013
Please note that the original version of the QUAERO corpus distributed in the CLEF eHealth challenge 2015 and 2016 came in the BRAT stand alone format. It was distributed with the CLEF eHealth evaluation tool. This original distribution of the QUAERO French Medical corpus is available separately from https://quaerofrenchmed.limsi.fr
All questions regarding the task or data should be addressed to aurelie.neveol@limsi.fr | @InProceedings{neveol14quaero,
author = {Névéol, Aurélie and Grouin, Cyril and Leixa, Jeremy
and Rosset, Sophie and Zweigenbaum, Pierre},
title = {The {QUAERO} {French} Medical Corpus: A Ressource for
Medical Entity Recognition and Normalization},
OPTbooktitle = {Proceedings of the Fourth Workshop on Building
and Evaluating Ressources for Health and Biomedical
Text Processing},
booktitle = {Proc of BioTextMining Work},
OPTseries = {BioTxtM 2014},
year = {2014},
pages = {24--30},
} | 1 | 343 | 2022-11-13T22:11:53 |
---
language:
- fr
bigbio_language:
- French
license: other
multilinguality: monolingual
bigbio_license_shortname: GFDL_1p3
pretty_name: QUAERO
homepage: https://quaerofrenchmed.limsi.fr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
---
# Dataset Card for QUAERO
## Dataset Description
- **Homepage:** https://quaerofrenchmed.limsi.fr/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED
The QUAERO French Medical Corpus has been initially developed as a resource for named entity recognition and normalization [1]. It was then improved with the purpose of creating a gold standard set of normalized entities for French biomedical text, that was used in the CLEF eHealth evaluation lab [2][3].
A selection of MEDLINE titles and EMEA documents were manually annotated. The annotation process was guided by concepts in the Unified Medical Language System (UMLS):
1. Ten types of clinical entities, as defined by the following UMLS Semantic Groups (Bodenreider and McCray 2003) were annotated: Anatomy, Chemical and Drugs, Devices, Disorders, Geographic Areas, Living Beings, Objects, Phenomena, Physiology, Procedures.
2. The annotations were made in a comprehensive fashion, so that nested entities were marked, and entities could be mapped to more than one UMLS concept. In particular: (a) If a mention can refer to more than one Semantic Group, all the relevant Semantic Groups should be annotated. For instance, the mention “récidive” (recurrence) in the phrase “prévention des récidives” (recurrence prevention) should be annotated with the category “DISORDER” (CUI C2825055) and the category “PHENOMENON” (CUI C0034897); (b) If a mention can refer to more than one UMLS concept within the same Semantic Group, all the relevant concepts should be annotated. For instance, the mention “maniaques” (obsessive) in the phrase “patients maniaques” (obsessive patients) should be annotated with CUIs C0564408 and C0338831 (category “DISORDER”); (c) Entities which span overlaps with that of another entity should still be annotated. For instance, in the phrase “infarctus du myocarde” (myocardial infarction), the mention “myocarde” (myocardium) should be annotated with category “ANATOMY” (CUI C0027061) and the mention “infarctus du myocarde” should be annotated with category “DISORDER” (CUI C0027051)
The QUAERO French Medical Corpus BioC release comprises a subset of the QUAERO French Medical corpus, as follows:
Training data (BRAT version used in CLEF eHealth 2015 task 1b as training data):
- MEDLINE_train_bioc file: 833 MEDLINE titles, annotated with normalized entities in the BioC format
- EMEA_train_bioc file: 3 EMEA documents, segmented into 11 sub-documents, annotated with normalized entities in the BioC format
Development data (BRAT version used in CLEF eHealth 2015 task 1b as test data and in CLEF eHealth 2016 task 2 as development data):
- MEDLINE_dev_bioc file: 832 MEDLINE titles, annotated with normalized entities in the BioC format
- EMEA_dev_bioc file: 3 EMEA documents, segmented into 12 sub-documents, annotated with normalized entities in the BioC format
Test data (BRAT version used in CLEF eHealth 2016 task 2 as test data):
- MEDLINE_test_bioc folder: 833 MEDLINE titles, annotated with normalized entities in the BioC format
- EMEA folder_test_bioc: 4 EMEA documents, segmented into 15 sub-documents, annotated with normalized entities in the BioC format
This release of the QUAERO French medical corpus, BioC version, comes in the BioC format, through automatic conversion from the original BRAT format obtained with the Brat2BioC tool https://bitbucket.org/nicta_biomed/brat2bioc developped by Jimeno Yepes et al.
Antonio Jimeno Yepes, Mariana Neves, Karin Verspoor
Brat2BioC: conversion tool between brat and BioC
BioCreative IV track 1 - BioC: The BioCreative Interoperability Initiative, 2013
Please note that the original version of the QUAERO corpus distributed in the CLEF eHealth challenge 2015 and 2016 came in the BRAT stand alone format. It was distributed with the CLEF eHealth evaluation tool. This original distribution of the QUAERO French Medical corpus is available separately from https://quaerofrenchmed.limsi.fr
All questions regarding the task or data should be addressed to aurelie.neveol@limsi.fr
## Citation Information
```
@InProceedings{neveol14quaero,
author = {Névéol, Aurélie and Grouin, Cyril and Leixa, Jeremy
and Rosset, Sophie and Zweigenbaum, Pierre},
title = {The {QUAERO} {French} Medical Corpus: A Ressource for
Medical Entity Recognition and Normalization},
OPTbooktitle = {Proceedings of the Fourth Workshop on Building
and Evaluating Ressources for Health and Biomedical
Text Processing},
booktitle = {Proc of BioTextMining Work},
OPTseries = {BioTxtM 2014},
year = {2014},
pages = {24--30},
}
```
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keremberke/pokemon-classification | 2023-01-15T18:41:29.000Z | [
"task_categories:image-classification",
"roboflow",
"roboflow2huggingface",
"Gaming",
"region:us"
] | keremberke | null | @misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-14 },
} | 5 | 343 | 2023-01-15T18:40:15 | ---
task_categories:
- image-classification
tags:
- roboflow
- roboflow2huggingface
- Gaming
---
<div align="center">
<img width="640" alt="keremberke/pokemon-classification" src="https://huggingface.co/datasets/keremberke/pokemon-classification/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['Porygon', 'Goldeen', 'Hitmonlee', 'Hitmonchan', 'Gloom', 'Aerodactyl', 'Mankey', 'Seadra', 'Gengar', 'Venonat', 'Articuno', 'Seaking', 'Dugtrio', 'Machop', 'Jynx', 'Oddish', 'Dodrio', 'Dragonair', 'Weedle', 'Golduck', 'Flareon', 'Krabby', 'Parasect', 'Ninetales', 'Nidoqueen', 'Kabutops', 'Drowzee', 'Caterpie', 'Jigglypuff', 'Machamp', 'Clefairy', 'Kangaskhan', 'Dragonite', 'Weepinbell', 'Fearow', 'Bellsprout', 'Grimer', 'Nidorina', 'Staryu', 'Horsea', 'Electabuzz', 'Dratini', 'Machoke', 'Magnemite', 'Squirtle', 'Gyarados', 'Pidgeot', 'Bulbasaur', 'Nidoking', 'Golem', 'Dewgong', 'Moltres', 'Zapdos', 'Poliwrath', 'Vulpix', 'Beedrill', 'Charmander', 'Abra', 'Zubat', 'Golbat', 'Wigglytuff', 'Charizard', 'Slowpoke', 'Poliwag', 'Tentacruel', 'Rhyhorn', 'Onix', 'Butterfree', 'Exeggcute', 'Sandslash', 'Pinsir', 'Rattata', 'Growlithe', 'Haunter', 'Pidgey', 'Ditto', 'Farfetchd', 'Pikachu', 'Raticate', 'Wartortle', 'Vaporeon', 'Cloyster', 'Hypno', 'Arbok', 'Metapod', 'Tangela', 'Kingler', 'Exeggutor', 'Kadabra', 'Seel', 'Voltorb', 'Chansey', 'Venomoth', 'Ponyta', 'Vileplume', 'Koffing', 'Blastoise', 'Tentacool', 'Lickitung', 'Paras', 'Clefable', 'Cubone', 'Marowak', 'Nidorino', 'Jolteon', 'Muk', 'Magikarp', 'Slowbro', 'Tauros', 'Kabuto', 'Spearow', 'Sandshrew', 'Eevee', 'Kakuna', 'Omastar', 'Ekans', 'Geodude', 'Magmar', 'Snorlax', 'Meowth', 'Pidgeotto', 'Venusaur', 'Persian', 'Rhydon', 'Starmie', 'Charmeleon', 'Lapras', 'Alakazam', 'Graveler', 'Psyduck', 'Rapidash', 'Doduo', 'Magneton', 'Arcanine', 'Electrode', 'Omanyte', 'Poliwhirl', 'Mew', 'Alolan Sandslash', 'Mewtwo', 'Weezing', 'Gastly', 'Victreebel', 'Ivysaur', 'MrMime', 'Shellder', 'Scyther', 'Diglett', 'Primeape', 'Raichu']
```
### Number of Images
```json
{'train': 4869, 'valid': 1390, 'test': 732}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/pokemon-classification", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14](https://universe.roboflow.com/robert-demo-qvail/pokedex/dataset/14?ref=roboflow2huggingface)
### Citation
```
@misc{ pokedex_dataset,
title = { Pokedex Dataset },
type = { Open Source Dataset },
author = { Lance Zhang },
howpublished = { \\url{ https://universe.roboflow.com/robert-demo-qvail/pokedex } },
url = { https://universe.roboflow.com/robert-demo-qvail/pokedex },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { dec },
note = { visited on 2023-01-14 },
}
```
### License
Public Domain
### Dataset Summary
This dataset was exported via roboflow.com on December 20, 2022 at 5:34 PM GMT
Roboflow is an end-to-end computer vision platform that helps you
* collaborate with your team on computer vision projects
* collect & organize images
* understand unstructured image data
* annotate, and create datasets
* export, train, and deploy computer vision models
* use active learning to improve your dataset over time
It includes 6991 images.
Pokemon are annotated in folder format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
* Resize to 224x224 (Fit (black edges))
No image augmentation techniques were applied.
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] |
Lakera/gandalf_ignore_instructions | 2023-10-02T09:26:29.000Z | [
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"prompt injection",
"region:us"
] | Lakera | null | null | 4 | 343 | 2023-09-21T08:49:47 | ---
language:
- en
license: mit
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: text
dtype: string
- name: similarity
dtype: float64
splits:
- name: train
num_bytes: 66400
num_examples: 777
- name: validation
num_bytes: 9633
num_examples: 111
- name: test
num_bytes: 9747
num_examples: 112
download_size: 51515
dataset_size: 85780
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
tags:
- prompt injection
---
# gandalf_ignore_instructions
This is a dataset of prompt injections from [Gandalf](https://gandalf.lakera.ai/) by [Lakera](https://www.lakera.ai/).
Note that we might update the dataset occasionally by cleaning the data or adding more samples.
## How the data was obtained
There are millions of prompts and many of them are not actual prompt injections (people ask Gandalf all kinds of things).
We used the following process to obtain relevant data:
- Start with all prompts submitted to Gandalf in July 2023.
- Use [OpenAI text embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) (`text-embedding-ada-002`) to compute the similarity between the prompts and the phrase "Ignore all previous instructions".
- Filter to those prompts that have similarity of at least 0.825.
- Use [Lakera Guard](https://platform.lakera.ai/docs)'s personally identifiable information detector to filter out prompts that potentially contain PII data.
- Remove near-duplicates from the data (prompts that differ only by a few letters) using an approximate algorithm. This helps reduce leakage between the data splits.
- Sample 1000 prompts.
- Split the data into train-val-test with an 80/10/10 ratio. Each sample is assigned independently so the size of the train split is not _exactly_ 80% and so on.
Note that there is a small amount of noise in the data since an automatic method was used to obtain it: a few of the samples might not be real prompt injections.
## Citation
If you use this dataset in your research, please cite it as
```
@InProceedings{gandalf_ignore_instructions,
title = {gandalf_ignore_instructions},
author={Lakera AI (https://www.lakera.ai)},
year={2023}
}
```
## Licensing Information
gandalf_ignore_instructions is distributed under the [MIT License](https://opensource.org/license/mit/). | 2,505 | [
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open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1 | 2023-10-04T07:22:11.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 343 | 2023-10-04T07:21:12 | ---
pretty_name: Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-10-04T07:20:47.805368](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1/blob/main/results_2023-10-04T07-20-47.805368.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7077701920572519,\n\
\ \"acc_stderr\": 0.030864748969041296,\n \"acc_norm\": 0.7116562654123524,\n\
\ \"acc_norm_stderr\": 0.030832223559852722,\n \"mc1\": 0.4675642594859241,\n\
\ \"mc1_stderr\": 0.017466632149577613,\n \"mc2\": 0.6457987192860579,\n\
\ \"mc2_stderr\": 0.014744553500507454\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688065,\n\
\ \"acc_norm\": 0.7303754266211604,\n \"acc_norm_stderr\": 0.012968040686869154\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6914957179844653,\n\
\ \"acc_stderr\": 0.004609320024893897,\n \"acc_norm\": 0.8781119298944433,\n\
\ \"acc_norm_stderr\": 0.003264878737586879\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8092105263157895,\n \"acc_stderr\": 0.03197565821032499,\n\
\ \"acc_norm\": 0.8092105263157895,\n \"acc_norm_stderr\": 0.03197565821032499\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7509433962264151,\n \"acc_stderr\": 0.026616482980501704,\n\
\ \"acc_norm\": 0.7509433962264151,\n \"acc_norm_stderr\": 0.026616482980501704\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.03216600808802267,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.03216600808802267\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.6,\n \"acc_stderr\": 0.04923659639173309,\n \"acc_norm\"\
: 0.6,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6936170212765957,\n \"acc_stderr\": 0.030135906478517563,\n\
\ \"acc_norm\": 0.6936170212765957,\n \"acc_norm_stderr\": 0.030135906478517563\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.04692008381368909,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.04692008381368909\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876719,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876719\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.48148148148148145,\n \"acc_stderr\": 0.02573364199183898,\n \"\
acc_norm\": 0.48148148148148145,\n \"acc_norm_stderr\": 0.02573364199183898\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8193548387096774,\n \"acc_stderr\": 0.021886178567172523,\n \"\
acc_norm\": 0.8193548387096774,\n \"acc_norm_stderr\": 0.021886178567172523\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5566502463054187,\n \"acc_stderr\": 0.03495334582162933,\n \"\
acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.03495334582162933\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\"\
: 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8484848484848485,\n \"acc_stderr\": 0.027998073798781678,\n\
\ \"acc_norm\": 0.8484848484848485,\n \"acc_norm_stderr\": 0.027998073798781678\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8939393939393939,\n \"acc_stderr\": 0.02193804773885311,\n \"\
acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.02193804773885311\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9430051813471503,\n \"acc_stderr\": 0.01673108529360755,\n\
\ \"acc_norm\": 0.9430051813471503,\n \"acc_norm_stderr\": 0.01673108529360755\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.02293992541853062,\n \
\ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.02293992541853062\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131137,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131137\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7689075630252101,\n \"acc_stderr\": 0.027381406927868876,\n\
\ \"acc_norm\": 0.7689075630252101,\n \"acc_norm_stderr\": 0.027381406927868876\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\
acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9009174311926605,\n \"acc_stderr\": 0.01280978008187893,\n \"\
acc_norm\": 0.9009174311926605,\n \"acc_norm_stderr\": 0.01280978008187893\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6018518518518519,\n \"acc_stderr\": 0.033384734032074016,\n \"\
acc_norm\": 0.6018518518518519,\n \"acc_norm_stderr\": 0.033384734032074016\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9264705882352942,\n \"acc_stderr\": 0.01831885585008968,\n \"\
acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.01831885585008968\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065498,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065498\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7892376681614349,\n\
\ \"acc_stderr\": 0.027373095500540186,\n \"acc_norm\": 0.7892376681614349,\n\
\ \"acc_norm_stderr\": 0.027373095500540186\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8677685950413223,\n \"acc_stderr\": 0.03092278832044579,\n \"\
acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.03092278832044579\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8282208588957055,\n \"acc_stderr\": 0.029634717272371037,\n\
\ \"acc_norm\": 0.8282208588957055,\n \"acc_norm_stderr\": 0.029634717272371037\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5535714285714286,\n\
\ \"acc_stderr\": 0.047184714852195865,\n \"acc_norm\": 0.5535714285714286,\n\
\ \"acc_norm_stderr\": 0.047184714852195865\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8446601941747572,\n \"acc_stderr\": 0.03586594738573974,\n\
\ \"acc_norm\": 0.8446601941747572,\n \"acc_norm_stderr\": 0.03586594738573974\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n\
\ \"acc_stderr\": 0.017893784904018516,\n \"acc_norm\": 0.9188034188034188,\n\
\ \"acc_norm_stderr\": 0.017893784904018516\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542126,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542126\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8659003831417624,\n\
\ \"acc_stderr\": 0.012185528166499978,\n \"acc_norm\": 0.8659003831417624,\n\
\ \"acc_norm_stderr\": 0.012185528166499978\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7745664739884393,\n \"acc_stderr\": 0.022497230190967554,\n\
\ \"acc_norm\": 0.7745664739884393,\n \"acc_norm_stderr\": 0.022497230190967554\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6368715083798883,\n\
\ \"acc_stderr\": 0.016083749986853708,\n \"acc_norm\": 0.6368715083798883,\n\
\ \"acc_norm_stderr\": 0.016083749986853708\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.77491961414791,\n\
\ \"acc_stderr\": 0.023720088516179027,\n \"acc_norm\": 0.77491961414791,\n\
\ \"acc_norm_stderr\": 0.023720088516179027\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8333333333333334,\n \"acc_stderr\": 0.020736358408060002,\n\
\ \"acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.020736358408060002\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5780141843971631,\n \"acc_stderr\": 0.02946218923337059,\n \
\ \"acc_norm\": 0.5780141843971631,\n \"acc_norm_stderr\": 0.02946218923337059\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.589960886571056,\n\
\ \"acc_stderr\": 0.012561837621962028,\n \"acc_norm\": 0.589960886571056,\n\
\ \"acc_norm_stderr\": 0.012561837621962028\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7426470588235294,\n \"acc_stderr\": 0.0265565194700415,\n\
\ \"acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.0265565194700415\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7696078431372549,\n \"acc_stderr\": 0.01703522925803403,\n \
\ \"acc_norm\": 0.7696078431372549,\n \"acc_norm_stderr\": 0.01703522925803403\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n\
\ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n\
\ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7959183673469388,\n \"acc_stderr\": 0.025801283475090496,\n\
\ \"acc_norm\": 0.7959183673469388,\n \"acc_norm_stderr\": 0.025801283475090496\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8756218905472637,\n\
\ \"acc_stderr\": 0.023335401790166327,\n \"acc_norm\": 0.8756218905472637,\n\
\ \"acc_norm_stderr\": 0.023335401790166327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015575,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015575\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4675642594859241,\n\
\ \"mc1_stderr\": 0.017466632149577613,\n \"mc2\": 0.6457987192860579,\n\
\ \"mc2_stderr\": 0.014744553500507454\n }\n}\n```"
repo_url: https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|arc:challenge|25_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hellaswag|10_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-20-47.805368.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T07-20-47.805368.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T07-20-47.805368.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T07-20-47.805368.parquet'
- config_name: results
data_files:
- split: 2023_10_04T07_20_47.805368
path:
- results_2023-10-04T07-20-47.805368.parquet
- split: latest
path:
- results_2023-10-04T07-20-47.805368.parquet
---
# Dataset Card for Evaluation run of Riiid/sheep-duck-llama-2-70b-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [Riiid/sheep-duck-llama-2-70b-v1.1](https://huggingface.co/Riiid/sheep-duck-llama-2-70b-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-04T07:20:47.805368](https://huggingface.co/datasets/open-llm-leaderboard/details_Riiid__sheep-duck-llama-2-70b-v1.1/blob/main/results_2023-10-04T07-20-47.805368.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7077701920572519,
"acc_stderr": 0.030864748969041296,
"acc_norm": 0.7116562654123524,
"acc_norm_stderr": 0.030832223559852722,
"mc1": 0.4675642594859241,
"mc1_stderr": 0.017466632149577613,
"mc2": 0.6457987192860579,
"mc2_stderr": 0.014744553500507454
},
"harness|arc:challenge|25": {
"acc": 0.6877133105802048,
"acc_stderr": 0.013542598541688065,
"acc_norm": 0.7303754266211604,
"acc_norm_stderr": 0.012968040686869154
},
"harness|hellaswag|10": {
"acc": 0.6914957179844653,
"acc_stderr": 0.004609320024893897,
"acc_norm": 0.8781119298944433,
"acc_norm_stderr": 0.003264878737586879
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04171654161354543,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04171654161354543
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8092105263157895,
"acc_stderr": 0.03197565821032499,
"acc_norm": 0.8092105263157895,
"acc_norm_stderr": 0.03197565821032499
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7509433962264151,
"acc_stderr": 0.026616482980501704,
"acc_norm": 0.7509433962264151,
"acc_norm_stderr": 0.026616482980501704
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.03216600808802267,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.03216600808802267
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.6,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.6,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.0355068398916558,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.0355068398916558
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.048108401480826346,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.048108401480826346
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6936170212765957,
"acc_stderr": 0.030135906478517563,
"acc_norm": 0.6936170212765957,
"acc_norm_stderr": 0.030135906478517563
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.04692008381368909,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.04692008381368909
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.03996629574876719,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.03996629574876719
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.48148148148148145,
"acc_stderr": 0.02573364199183898,
"acc_norm": 0.48148148148148145,
"acc_norm_stderr": 0.02573364199183898
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5,
"acc_stderr": 0.04472135954999579,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8193548387096774,
"acc_stderr": 0.021886178567172523,
"acc_norm": 0.8193548387096774,
"acc_norm_stderr": 0.021886178567172523
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5566502463054187,
"acc_stderr": 0.03495334582162933,
"acc_norm": 0.5566502463054187,
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 64,984 | [
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open-llm-leaderboard/details_tiiuae__falcon-40b | 2023-09-08T21:43:17.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 342 | 2023-08-21T11:07:51 | ---
pretty_name: Evaluation run of tiiuae/falcon-40b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 124 configuration, each one coresponding to one of\
\ the evaluated task.\n\nThe dataset has been created from 5 run(s). Each run can\
\ be found as a specific split in each configuration, the split being named using\
\ the timestamp of the run.The \"train\" split is always pointing to the latest\
\ results.\n\nAn additional configuration \"results\" store all the aggregated results\
\ of the run (and is used to compute and display the agregated metrics on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_tiiuae__falcon-40b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-08T21:43:04.856041](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b/blob/main/results_2023-09-08T21-43-04.856041.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.0045092281879194635,\n\
\ \"em_stderr\": 0.000686134689909491,\n \"f1\": 0.0640572567114092,\n\
\ \"f1_stderr\": 0.0014469716881546906,\n \"acc\": 0.4709614145274008,\n\
\ \"acc_stderr\": 0.010032846697618985\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0045092281879194635,\n \"em_stderr\": 0.000686134689909491,\n\
\ \"f1\": 0.0640572567114092,\n \"f1_stderr\": 0.0014469716881546906\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.12661106899166036,\n \
\ \"acc_stderr\": 0.009159715283081087\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8153117600631413,\n \"acc_stderr\": 0.010905978112156885\n\
\ }\n}\n```"
repo_url: https://huggingface.co/tiiuae/falcon-40b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|arc:challenge|25_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_08T21_43_04.856041
path:
- '**/details_harness|drop|3_2023-09-08T21-43-04.856041.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-08T21-43-04.856041.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_08T21_43_04.856041
path:
- '**/details_harness|gsm8k|5_2023-09-08T21-43-04.856041.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-08T21-43-04.856041.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hellaswag|10_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_0
data_files:
- split: 2023_08_21T11_07_51.058817
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:07:51.058817.parquet'
- split: 2023_08_21T11_30_10.858708
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-21T22:49:59.134750.parquet'
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- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-21T22:49:59.134750.parquet'
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- '**/details_harness|hendrycksTest-marketing|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-21T22:49:59.134750.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_0
data_files:
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path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:07:51.058817.parquet'
- split: 2023_08_21T11_30_10.858708
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|0_2023-08-21T11:30:10.858708.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-21T22:49:59.134750.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-21T22:49:59.134750.parquet'
- split: latest
path:
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data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_21T22_49_59.134750
path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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- split: latest
path:
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data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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- split: latest
path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:17:39.708485.parquet'
- split: latest
path:
- '**/details_original|mmlu:public_relations|5_2023-08-28T20:17:39.708485.parquet'
- config_name: original_mmlu_security_studies_5
data_files:
- split: 2023_08_28T20_17_39.708485
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:17:39.708485.parquet'
- split: latest
path:
- '**/details_original|mmlu:security_studies|5_2023-08-28T20:17:39.708485.parquet'
- config_name: original_mmlu_sociology_5
data_files:
- split: 2023_08_28T20_17_39.708485
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:17:39.708485.parquet'
- split: latest
path:
- '**/details_original|mmlu:sociology|5_2023-08-28T20:17:39.708485.parquet'
- config_name: original_mmlu_us_foreign_policy_5
data_files:
- split: 2023_08_28T20_17_39.708485
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:17:39.708485.parquet'
- split: latest
path:
- '**/details_original|mmlu:us_foreign_policy|5_2023-08-28T20:17:39.708485.parquet'
- config_name: original_mmlu_virology_5
data_files:
- split: 2023_08_28T20_17_39.708485
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:17:39.708485.parquet'
- split: latest
path:
- '**/details_original|mmlu:virology|5_2023-08-28T20:17:39.708485.parquet'
- config_name: original_mmlu_world_religions_5
data_files:
- split: 2023_08_28T20_17_39.708485
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:17:39.708485.parquet'
- split: latest
path:
- '**/details_original|mmlu:world_religions|5_2023-08-28T20:17:39.708485.parquet'
- config_name: results
data_files:
- split: 2023_08_21T11_07_51.058817
path:
- results_2023-08-21T11:07:51.058817.parquet
- split: 2023_08_21T11_30_10.858708
path:
- results_2023-08-21T11:30:10.858708.parquet
- split: 2023_08_21T22_49_59.134750
path:
- results_2023-08-21T22:49:59.134750.parquet
- split: 2023_08_28T20_17_39.708485
path:
- results_2023-08-28T20:17:39.708485.parquet
- split: 2023_09_08T21_43_04.856041
path:
- results_2023-09-08T21-43-04.856041.parquet
- split: latest
path:
- results_2023-09-08T21-43-04.856041.parquet
---
# Dataset Card for Evaluation run of tiiuae/falcon-40b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/tiiuae/falcon-40b
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 124 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 5 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_tiiuae__falcon-40b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-08T21:43:04.856041](https://huggingface.co/datasets/open-llm-leaderboard/details_tiiuae__falcon-40b/blob/main/results_2023-09-08T21-43-04.856041.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.0045092281879194635,
"em_stderr": 0.000686134689909491,
"f1": 0.0640572567114092,
"f1_stderr": 0.0014469716881546906,
"acc": 0.4709614145274008,
"acc_stderr": 0.010032846697618985
},
"harness|drop|3": {
"em": 0.0045092281879194635,
"em_stderr": 0.000686134689909491,
"f1": 0.0640572567114092,
"f1_stderr": 0.0014469716881546906
},
"harness|gsm8k|5": {
"acc": 0.12661106899166036,
"acc_stderr": 0.009159715283081087
},
"harness|winogrande|5": {
"acc": 0.8153117600631413,
"acc_stderr": 0.010905978112156885
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 67,932 | [
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] |
bigbio/biored | 2023-01-12T05:54:49.000Z | [
"multilinguality:monolingual",
"language:en",
"license:unknown",
"arxiv:2204.04263",
"region:us"
] | bigbio | Relation Extraction corpus with multiple entity types (e.g., gene/protein,
disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),
on a set of 600 PubMed articles | @article{DBLP:journals/corr/abs-2204-04263,
author = {Ling Luo and
Po{-}Ting Lai and
Chih{-}Hsuan Wei and
Cecilia N. Arighi and
Zhiyong Lu},
title = {BioRED: {A} Comprehensive Biomedical Relation Extraction Dataset},
journal = {CoRR},
volume = {abs/2204.04263},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2204.04263},
doi = {10.48550/arXiv.2204.04263},
eprinttype = {arXiv},
eprint = {2204.04263},
timestamp = {Wed, 11 May 2022 15:24:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2204-04263.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | 1 | 341 | 2022-11-13T22:07:21 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: BioRED
homepage: https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for BioRED
## Dataset Description
- **Homepage:** https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,RE
Relation Extraction corpus with multiple entity types (e.g., gene/protein,
disease, chemical) and relation pairs (e.g., gene-disease; chemical-chemical),
on a set of 600 PubMed articles
## Citation Information
```
@article{DBLP:journals/corr/abs-2204-04263,
author = {Ling Luo and
Po{-}Ting Lai and
Chih{-}Hsuan Wei and
Cecilia N. Arighi and
Zhiyong Lu},
title = {BioRED: {A} Comprehensive Biomedical Relation Extraction Dataset},
journal = {CoRR},
volume = {abs/2204.04263},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2204.04263},
doi = {10.48550/arXiv.2204.04263},
eprinttype = {arXiv},
eprint = {2204.04263},
timestamp = {Wed, 11 May 2022 15:24:37 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2204-04263.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
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Dahoas/svamp | 2023-10-16T11:27:40.000Z | [
"region:us"
] | Dahoas | null | null | 0 | 341 | 2023-10-16T11:24:12 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 347184
num_examples: 700
- name: test
num_bytes: 148692
num_examples: 300
download_size: 169303
dataset_size: 495876
---
# Dataset Card for "svamp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 513 | [
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erwanlc/cocktails_recipe_no_brand | 2022-10-25T09:17:08.000Z | [
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:2M<n<3M",
"language:en",
"license:other",
"region:us"
] | erwanlc | null | null | 1 | 340 | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 2M<n<3M
source_datasets: []
task_categories: []
task_ids: []
pretty_name: cocktails_recipe_no_brand
language_bcp47:
- en
- en-US
---
# Dataset Card for cocktails_recipe
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains a list of cocktails and how to do them.
### Languages
The language is english.
## Dataset Structure
### Data Fields
- Title: name of the cocktail
- Glass: type of glass to use
- Garnish: garnish to use for the glass
- Recipe: how to do the cocktail
- Ingredients: ingredients required
- Raw Ingredients: ingredients mapped to their raw ingredients to remove the brand
### Data Splits
Currently, there is no splits.
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The dataset was created by scraping the Diffords cocktail website.
### Personal and Sensitive Information
It should not contain any personal or sensitive information.
### Contributions
Thanks to [@github-erwanlc](https://github.com/erwanlc) for adding this dataset. | 1,708 | [
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] |
kili-technology/plastic_in_river | 2022-10-21T07:13:58.000Z | [
"task_categories:object-detection",
"size_categories:1K<n<10K",
"source_datasets:original",
"other-object-detection",
"region:us"
] | kili-technology | This dataset contains photos of rivers on which there may be waste. The waste items are annotated
through bounding boxes, and are assigned to one of the 4 following categories: plastic bottle, plastic bag,
another plastic waste, or non-plastic waste. Note that some photos may not contain any waste. | null | 13 | 339 | 2022-03-02T23:29:22 | ---
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: Plastic in river
tags:
- other-object-detection
---
# Plastic in river
This dataset is an export of the annotated assets from the [Kili's Community Challenge - Plastic in River dataset](https://kili-technology.com/blog/kili-s-community-challenge-plastic-in-river-dataset).
The Hugging Face dataset will be updated every day during the challenge with the latest annotations. | 496 | [
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kresnik/zeroth_korean | 2023-01-04T06:54:55.000Z | [
"region:us"
] | kresnik | This is Zeroth-Korean corpus,
licensed under Attribution 4.0 International (CC BY 4.0)
The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor).
Zeroth project introduces free Korean speech corpus and aims to make Korean speech recognition more broadly accessible to everyone.
This project was developed in collaboration between Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.).
Contact: Lucas Jo(lucasjo@goodatlas.com), Wonkyum Lee(wonkyum@gridspace.com) | \ | 5 | 339 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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baber/agieval | 2023-10-26T00:49:22.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"language:en",
"license:mit",
"arxiv:2304.06364",
"region:us"
] | baber | null | @ARTICLE{10174688,
author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
title={LogiQA 2.0 — An Improved Dataset for Logical Reasoning in Natural Language Understanding},
year={2023},
volume={},
number={},
pages={1-16},
doi={10.1109/TASLP.2023.3293046}} | 2 | 339 | 2023-07-23T00:31:09 | ---
license: mit
language:
- en
task_categories:
- question-answering
- text-generation
pretty_name: AGIEval
---
# Dataset Card for AGIEval
## Dataset Description
- **Homepage:** https://github.com/microsoft/AGIEval/blob/main/README.md
- **Repository:** https://github.com/microsoft/AGIEval
- **Paper:** https://arxiv.org/abs/2304.06364
### Dataset Summary
AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. This benchmark is derived from 20 official, public, and high-standard admission and qualification exams intended for general human test-takers, such as general college admission tests (e.g., Chinese College Entrance Exam (Gaokao) and American SAT), law school admission tests, math competitions, lawyer qualification tests, and national civil service exams.
### Citation Information
Dataset taken from the AGIEval Repo.
```
@misc{zhong2023agieval,
title={AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models},
author={Wanjun Zhong and Ruixiang Cui and Yiduo Guo and Yaobo Liang and Shuai Lu and Yanlin Wang and Amin Saied and Weizhu Chen and Nan Duan},
year={2023},
eprint={2304.06364},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Citation for each dataset:
```
@inproceedings{ling-etal-2017-program,
title = "Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems",
author = "Ling, Wang and
Yogatama, Dani and
Dyer, Chris and
Blunsom, Phil",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1015",
doi = "10.18653/v1/P17-1015",
pages = "158--167",
abstract = "Solving algebraic word problems requires executing a series of arithmetic operations{---}a program{---}to obtain a final answer. However, since programs can be arbitrarily complicated, inducing them directly from question-answer pairs is a formidable challenge. To make this task more feasible, we solve these problems by generating answer rationales, sequences of natural language and human-readable mathematical expressions that derive the final answer through a series of small steps. Although rationales do not explicitly specify programs, they provide a scaffolding for their structure via intermediate milestones. To evaluate our approach, we have created a new 100,000-sample dataset of questions, answers and rationales. Experimental results show that indirect supervision of program learning via answer rationales is a promising strategy for inducing arithmetic programs.",
}
@inproceedings{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks and Collin Burns and Saurav Kadavath and Akul Arora and Steven Basart and Eric Tang and Dawn Song and Jacob Steinhardt},
journal={NeurIPS},
year={2021}
}
@inproceedings{Liu2020LogiQAAC,
title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
author={Jian Liu and Leyang Cui and Hanmeng Liu and Dandan Huang and Yile Wang and Yue Zhang},
booktitle={International Joint Conference on Artificial Intelligence},
year={2020}
}
@inproceedings{zhong2019jec,
title={JEC-QA: A Legal-Domain Question Answering Dataset},
author={Zhong, Haoxi and Xiao, Chaojun and Tu, Cunchao and Zhang, Tianyang and Liu, Zhiyuan and Sun, Maosong},
booktitle={Proceedings of AAAI},
year={2020},
}
@article{Wang2021FromLT,
title={From LSAT: The Progress and Challenges of Complex Reasoning},
author={Siyuan Wang and Zhongkun Liu and Wanjun Zhong and Ming Zhou and Zhongyu Wei and Zhumin Chen and Nan Duan},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
year={2021},
volume={30},
pages={2201-2216}
}
``` | 4,091 | [
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] |
HumanCompatibleAI/ppo-CartPole-v1 | 2023-07-18T14:43:49.000Z | [
"region:us"
] | HumanCompatibleAI | null | null | 0 | 338 | 2023-07-18T14:43:44 | ---
dataset_info:
features:
- name: obs
sequence:
sequence: float32
- name: acts
sequence: int64
- name: infos
sequence: string
- name: terminal
dtype: bool
- name: rews
sequence: float64
splits:
- name: train
num_bytes: 2103613
num_examples: 100
download_size: 1263834
dataset_size: 2103613
---
# Dataset Card for "ppo-CartPole-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 519 | [
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OleehyO/latex-formulas | 2023-08-15T17:24:50.000Z | [
"task_categories:image-to-text",
"license:openrail",
"region:us"
] | OleehyO | null | null | 11 | 338 | 2023-07-29T09:15:40 | ---
license: openrail
task_categories:
- image-to-text
---
# Dataset Description
> English version is [here](./README_English.md)
这里有两个数据集：*raw_formulas*和*tokenized_formulas*。
我们在*arxiv*上爬取了约100万条未经过清洗以及文本分词的latex公式的图片文本对从而得到了*raw_formulas*数据集。在将*raw_formulas*数据集进行**清洗**以及**文本分词**后得到了*tokenized_formulas*数据集。
渲染公式对应的图片时，需要用到以下外部包：
* amsmath
* amsfonts
* amssymb
* mathtools
## 有关*raw_formulas*数据集的一些细节
我们爬取了包含以下公式环境的latex公式:
* equation
* align
* align*
* gather
* gather*
公式中不存在以下内容：
* \label
* %
* \quad
* \qquad
* \vspace
* \hspace
* \resizebox
* \scalebox
* \rotatebox
* \parbox
* \fbox
* \makebox
* \raisebox
* \addvspace
* \hfill
* \vfill
* \textwidth
* \textheight
* \rule
## *tokenized_formulas*数据集的一些预处理细节
### 清洗
* 我们删除了*raw_formulas*中一些无用的垃圾数据
* 我们删除了*raw_formulas*中过于复杂的公式：
* 公式对应的渲染图片高宽比如果大于0.8，那么公式会被删除
* 字符长度大于200的公式被删除了
* *raw_formulas*公式中的以下内容被删除了：
* \tag
* \text
* \begin{split}
* \end{split}
* \nonumber
* \notag
* *raw_formulas*中的`equation、equation*、align、\[...\]`公式环境被替换成了`align*`公式环境，`gather`公式环境被替换成了`gather*`环境
* 我们删除了*raw_formulas*中包含自定义宏的公式，**只有以下的常见自定义宏被保留了**:
* \newcommand{\R}{\mathbb{R}}
* \newcommand{\N}{\mathbb{N}}
* \newcommand{\Z}{\mathbb{Z}}
* \newcommand{\Q}{\mathbb{Q}}
* \newcommand{\C}{\mathbb{C}}
* \newcommand{\avg}[1]{\left<#1\right>}
* \newcommand{\Deriv}[2]{\frac{\mathrm{d} #1}{\mathrm{d} #2}}
* \newcommand{\dd}{\mathrm{d}}
* \newcommand{\norm}[1]{\left\lVert#1\right\rVert}
* \newcommand{\abs}[1]{\left|#1\right|}
* \newcommand{\vect}[1]{\mathbf{#1}}
### 分词
符合以下pattern的子串被视为一个词：
* \begin{.*?}
* \end\{.*?}
* \\[A-Za-z]+
以下字符串也被视为一个词：
* \\[
* \\]
* \\\
* \\{
* \\}
* \\_
* \\$
* \\&
* \\#
* \\%
* \\|
* '
* ''
* '''
* ''''
* '\^
* ''\^
* '''\^
* ''''\^
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open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.1 | 2023-09-19T01:01:39.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 338 | 2023-09-19T01:00:38 | ---
pretty_name: Evaluation run of ICBU-NPU/FashionGPT-70B-V1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [ICBU-NPU/FashionGPT-70B-V1.1](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.1\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-19T01:00:15.258935](https://huggingface.co/datasets/open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.1/blob/main/results_2023-09-19T01-00-15.258935.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7092811864393619,\n\
\ \"acc_stderr\": 0.03073791163351864,\n \"acc_norm\": 0.7129018476555437,\n\
\ \"acc_norm_stderr\": 0.030707851474891332,\n \"mc1\": 0.4528763769889841,\n\
\ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6526486216773408,\n\
\ \"mc2_stderr\": 0.01469598165350219\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6843003412969283,\n \"acc_stderr\": 0.013582571095815291,\n\
\ \"acc_norm\": 0.7175767918088737,\n \"acc_norm_stderr\": 0.013155456884097222\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7016530571599283,\n\
\ \"acc_stderr\": 0.004565974937793703,\n \"acc_norm\": 0.8819956184027086,\n\
\ \"acc_norm_stderr\": 0.0032195397905004797\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.04171654161354543,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.04171654161354543\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8289473684210527,\n \"acc_stderr\": 0.03064360707167709,\n\
\ \"acc_norm\": 0.8289473684210527,\n \"acc_norm_stderr\": 0.03064360707167709\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.042295258468165044,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.042295258468165044\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7433962264150943,\n \"acc_stderr\": 0.026880647889051985,\n\
\ \"acc_norm\": 0.7433962264150943,\n \"acc_norm_stderr\": 0.026880647889051985\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.031164899666948617,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.031164899666948617\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \"acc_norm\": 0.61,\n\
\ \"acc_norm_stderr\": 0.04902071300001975\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.049236596391733084,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.049236596391733084\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.036146654241808254,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.036146654241808254\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7106382978723405,\n \"acc_stderr\": 0.02964400657700962,\n\
\ \"acc_norm\": 0.7106382978723405,\n \"acc_norm_stderr\": 0.02964400657700962\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4649122807017544,\n\
\ \"acc_stderr\": 0.046920083813689104,\n \"acc_norm\": 0.4649122807017544,\n\
\ \"acc_norm_stderr\": 0.046920083813689104\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6413793103448275,\n \"acc_stderr\": 0.03996629574876719,\n\
\ \"acc_norm\": 0.6413793103448275,\n \"acc_norm_stderr\": 0.03996629574876719\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4708994708994709,\n \"acc_stderr\": 0.025707658614154954,\n \"\
acc_norm\": 0.4708994708994709,\n \"acc_norm_stderr\": 0.025707658614154954\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04472135954999579,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04472135954999579\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8290322580645161,\n\
\ \"acc_stderr\": 0.02141724293632159,\n \"acc_norm\": 0.8290322580645161,\n\
\ \"acc_norm_stderr\": 0.02141724293632159\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5763546798029556,\n \"acc_stderr\": 0.03476725747649037,\n\
\ \"acc_norm\": 0.5763546798029556,\n \"acc_norm_stderr\": 0.03476725747649037\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\"\
: 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.02845038880528436,\n\
\ \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.02845038880528436\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8939393939393939,\n \"acc_stderr\": 0.02193804773885311,\n \"\
acc_norm\": 0.8939393939393939,\n \"acc_norm_stderr\": 0.02193804773885311\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.018088393839078894,\n\
\ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.018088393839078894\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7128205128205128,\n \"acc_stderr\": 0.022939925418530616,\n\
\ \"acc_norm\": 0.7128205128205128,\n \"acc_norm_stderr\": 0.022939925418530616\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253252,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253252\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7773109243697479,\n \"acc_stderr\": 0.02702543349888239,\n \
\ \"acc_norm\": 0.7773109243697479,\n \"acc_norm_stderr\": 0.02702543349888239\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.48344370860927155,\n \"acc_stderr\": 0.040802441856289715,\n \"\
acc_norm\": 0.48344370860927155,\n \"acc_norm_stderr\": 0.040802441856289715\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9045871559633027,\n \"acc_stderr\": 0.0125958992823358,\n \"acc_norm\"\
: 0.9045871559633027,\n \"acc_norm_stderr\": 0.0125958992823358\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.03350991604696044,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.03350991604696044\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.9117647058823529,\n \"acc_stderr\": 0.019907399791316942,\n\
\ \"acc_norm\": 0.9117647058823529,\n \"acc_norm_stderr\": 0.019907399791316942\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8818565400843882,\n \"acc_stderr\": 0.021011052659878467,\n \
\ \"acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.021011052659878467\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\
\ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\
\ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515368,\n\
\ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515368\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\
\ \"acc_stderr\": 0.035207039905179635,\n \"acc_norm\": 0.8425925925925926,\n\
\ \"acc_norm_stderr\": 0.035207039905179635\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8343558282208589,\n \"acc_stderr\": 0.029208296231259104,\n\
\ \"acc_norm\": 0.8343558282208589,\n \"acc_norm_stderr\": 0.029208296231259104\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5446428571428571,\n\
\ \"acc_stderr\": 0.04726835553719098,\n \"acc_norm\": 0.5446428571428571,\n\
\ \"acc_norm_stderr\": 0.04726835553719098\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\
\ \"acc_stderr\": 0.01831589168562585,\n \"acc_norm\": 0.9145299145299145,\n\
\ \"acc_norm_stderr\": 0.01831589168562585\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8646232439335888,\n\
\ \"acc_stderr\": 0.012234384586856478,\n \"acc_norm\": 0.8646232439335888,\n\
\ \"acc_norm_stderr\": 0.012234384586856478\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7774566473988439,\n \"acc_stderr\": 0.02239421566194282,\n\
\ \"acc_norm\": 0.7774566473988439,\n \"acc_norm_stderr\": 0.02239421566194282\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.646927374301676,\n\
\ \"acc_stderr\": 0.015984204545268575,\n \"acc_norm\": 0.646927374301676,\n\
\ \"acc_norm_stderr\": 0.015984204545268575\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7717041800643086,\n\
\ \"acc_stderr\": 0.0238393033113982,\n \"acc_norm\": 0.7717041800643086,\n\
\ \"acc_norm_stderr\": 0.0238393033113982\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8271604938271605,\n \"acc_stderr\": 0.021038517770157375,\n\
\ \"acc_norm\": 0.8271604938271605,\n \"acc_norm_stderr\": 0.021038517770157375\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6028368794326241,\n \"acc_stderr\": 0.029189805673587085,\n \
\ \"acc_norm\": 0.6028368794326241,\n \"acc_norm_stderr\": 0.029189805673587085\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5867014341590613,\n\
\ \"acc_stderr\": 0.012576779494860075,\n \"acc_norm\": 0.5867014341590613,\n\
\ \"acc_norm_stderr\": 0.012576779494860075\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7463235294117647,\n \"acc_stderr\": 0.026431329870789524,\n\
\ \"acc_norm\": 0.7463235294117647,\n \"acc_norm_stderr\": 0.026431329870789524\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7647058823529411,\n \"acc_stderr\": 0.017160587235046352,\n \
\ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.017160587235046352\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n\
\ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\
\ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8122448979591836,\n \"acc_stderr\": 0.02500025603954619,\n\
\ \"acc_norm\": 0.8122448979591836,\n \"acc_norm_stderr\": 0.02500025603954619\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8855721393034826,\n\
\ \"acc_stderr\": 0.022509345325101713,\n \"acc_norm\": 0.8855721393034826,\n\
\ \"acc_norm_stderr\": 0.022509345325101713\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.02567934272327692,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.02567934272327692\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4528763769889841,\n\
\ \"mc1_stderr\": 0.01742558984831402,\n \"mc2\": 0.6526486216773408,\n\
\ \"mc2_stderr\": 0.01469598165350219\n }\n}\n```"
repo_url: https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|arc:challenge|25_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hellaswag|10_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-00-15.258935.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-19T01-00-15.258935.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T01-00-15.258935.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-19T01-00-15.258935.parquet'
- config_name: results
data_files:
- split: 2023_09_19T01_00_15.258935
path:
- results_2023-09-19T01-00-15.258935.parquet
- split: latest
path:
- results_2023-09-19T01-00-15.258935.parquet
---
# Dataset Card for Evaluation run of ICBU-NPU/FashionGPT-70B-V1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [ICBU-NPU/FashionGPT-70B-V1.1](https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-19T01:00:15.258935](https://huggingface.co/datasets/open-llm-leaderboard/details_ICBU-NPU__FashionGPT-70B-V1.1/blob/main/results_2023-09-19T01-00-15.258935.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7092811864393619,
"acc_stderr": 0.03073791163351864,
"acc_norm": 0.7129018476555437,
"acc_norm_stderr": 0.030707851474891332,
"mc1": 0.4528763769889841,
"mc1_stderr": 0.01742558984831402,
"mc2": 0.6526486216773408,
"mc2_stderr": 0.01469598165350219
},
"harness|arc:challenge|25": {
"acc": 0.6843003412969283,
"acc_stderr": 0.013582571095815291,
"acc_norm": 0.7175767918088737,
"acc_norm_stderr": 0.013155456884097222
},
"harness|hellaswag|10": {
"acc": 0.7016530571599283,
"acc_stderr": 0.004565974937793703,
"acc_norm": 0.8819956184027086,
"acc_norm_stderr": 0.0032195397905004797
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6296296296296297,
"acc_stderr": 0.04171654161354543,
"acc_norm": 0.6296296296296297,
"acc_norm_stderr": 0.04171654161354543
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8289473684210527,
"acc_stderr": 0.03064360707167709,
"acc_norm": 0.8289473684210527,
"acc_norm_stderr": 0.03064360707167709
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.77,
"acc_stderr": 0.042295258468165044,
"acc_norm": 0.77,
"acc_norm_stderr": 0.042295258468165044
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7433962264150943,
"acc_stderr": 0.026880647889051985,
"acc_norm": 0.7433962264150943,
"acc_norm_stderr": 0.026880647889051985
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.031164899666948617,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.031164899666948617
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.4,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6589595375722543,
"acc_stderr": 0.036146654241808254,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.036146654241808254
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105654,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932263,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932263
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7106382978723405,
"acc_stderr": 0.02964400657700962,
"acc_norm": 0.7106382978723405,
"acc_norm_stderr": 0.02964400657700962
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4649122807017544,
"acc_stderr": 0.046920083813689104,
"acc_norm": 0.4649122807017544,
"acc_norm_stderr": 0.046920083813689104
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6413793103448275,
"acc_stderr": 0.03996629574876719,
"acc_norm": 0.6413793103448275,
"acc_norm_stderr": 0.03996629574876719
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4708994708994709,
"acc_stderr": 0.025707658614154954,
"acc_norm": 0.4708994708994709,
"acc_norm_stderr": 0.025707658614154954
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5,
"acc_stderr": 0.04472135954999579,
"acc_norm": 0.5,
"acc_norm_stderr": 0.04472135954999579
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8290322580645161,
"acc_stderr": 0.02141724293632159,
"acc_norm": 0.8290322580645161,
"acc_norm_stderr": 0.02141724293632159
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5763546798029556,
"acc_stderr": 0.03476725747649037,
"acc_norm": 0.5763546798029556,
"acc_norm_stderr": 0.03476725747649037
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932262,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932262
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8424242424242424,
"acc_stderr": 0.02845038880528436,
"acc_norm": 0.8424242424242424,
"acc_norm_stderr": 0.02845038880528436
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8939393939393939,
"acc_stderr": 0.02193804773885311,
"acc_norm": 0.8939393939393939,
"acc_norm_stderr": 0.02193804773885311
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9326424870466321,
"acc_stderr": 0.018088393839078894,
"acc_norm": 0.9326424870466321,
"acc_norm_stderr": 0.018088393839078894
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7128205128205128,
"acc_stderr": 0.022939925418530616,
"acc_norm": 0.7128205128205128,
"acc_norm_stderr": 0.022939925418530616
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253252,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.028820884666253252
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7773109243697479,
"acc_stderr": 0.02702543349888239,
"acc_norm": 0.7773109243697479,
"acc_norm_stderr": 0.02702543349888239
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.48344370860927155,
"acc_stderr": 0.040802441856289715,
"acc_norm": 0.48344370860927155,
"acc_norm_stderr": 0.040802441856289715
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9045871559633027,
"acc_stderr": 0.0125958992823358,
"acc_norm": 0.9045871559633027,
"acc_norm_stderr": 0.0125958992823358
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.03350991604696044,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.03350991604696044
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9117647058823529,
"acc_stderr": 0.019907399791316942,
"acc_norm": 0.9117647058823529,
"acc_norm_stderr": 0.019907399791316942
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8818565400843882,
"acc_stderr": 0.021011052659878467,
"acc_norm": 0.8818565400843882,
"acc_norm_stderr": 0.021011052659878467
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8026905829596412,
"acc_stderr": 0.02670985334496796,
"acc_norm": 0.8026905829596412,
"acc_norm_stderr": 0.02670985334496796
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8549618320610687,
"acc_stderr": 0.030884661089515368,
"acc_norm": 0.8549618320610687,
"acc_norm_stderr": 0.030884661089515368
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035202,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035202
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8425925925925926,
"acc_stderr": 0.035207039905179635,
"acc_norm": 0.8425925925925926,
"acc_norm_stderr": 0.035207039905179635
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8343558282208589,
"acc_stderr": 0.029208296231259104,
"acc_norm": 0.8343558282208589,
"acc_norm_stderr": 0.029208296231259104
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5446428571428571,
"acc_stderr": 0.04726835553719098,
"acc_norm": 0.5446428571428571,
"acc_norm_stderr": 0.04726835553719098
},
"harness|hendrycksTest-management|5": {
"acc": 0.8349514563106796,
"acc_stderr": 0.03675668832233188,
"acc_norm": 0.8349514563106796,
"acc_norm_stderr": 0.03675668832233188
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9145299145299145,
"acc_stderr": 0.01831589168562585,
"acc_norm": 0.9145299145299145,
"acc_norm_stderr": 0.01831589168562585
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.74,
"acc_stderr": 0.04408440022768078,
"acc_norm": 0.74,
"acc_norm_stderr": 0.04408440022768078
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8646232439335888,
"acc_stderr": 0.012234384586856478,
"acc_norm": 0.8646232439335888,
"acc_norm_stderr": 0.012234384586856478
},
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 64,897 | [
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Elriggs/openwebtext-100k | 2023-10-03T20:23:28.000Z | [
"region:us"
] | Elriggs | null | null | 1 | 338 | 2023-10-03T20:22:13 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 497257202
num_examples: 100000
download_size: 302558045
dataset_size: 497257202
---
# Dataset Card for "openwebtext-100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 366 | [
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hyperpartisan_news_detection | 2023-06-13T07:46:19.000Z | [
"task_categories:text-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"bias-classification",
"region:us"
] | null | Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
There are 2 parts:
- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com. | @inproceedings{kiesel-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
author = "Kiesel, Johannes and
Mestre, Maria and
Shukla, Rishabh and
Vincent, Emmanuel and
Adineh, Payam and
Corney, David and
Stein, Benno and
Potthast, Martin",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2145",
doi = "10.18653/v1/S19-2145",
pages = "829--839",
abstract = "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048.",
} | 8 | 337 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: HyperpartisanNewsDetection
tags:
- bias-classification
dataset_info:
- config_name: byarticle
features:
- name: text
dtype: string
- name: title
dtype: string
- name: hyperpartisan
dtype: bool
- name: url
dtype: string
- name: published_at
dtype: string
splits:
- name: train
num_bytes: 2803943
num_examples: 645
download_size: 1000352
dataset_size: 2803943
- config_name: bypublisher
features:
- name: text
dtype: string
- name: title
dtype: string
- name: hyperpartisan
dtype: bool
- name: url
dtype: string
- name: published_at
dtype: string
- name: bias
dtype:
class_label:
names:
'0': right
'1': right-center
'2': least
'3': left-center
'4': left
splits:
- name: train
num_bytes: 2805711609
num_examples: 600000
- name: validation
num_bytes: 960356598
num_examples: 150000
download_size: 1003195420
dataset_size: 5611423218
---
# Dataset Card for "hyperpartisan_news_detection"
## 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://pan.webis.de/semeval19/semeval19-web/](https://pan.webis.de/semeval19/semeval19-web/)
- **Repository:** https://github.com/pan-webis-de/pan-code/tree/master/semeval19
- **Paper:** https://aclanthology.org/S19-2145
- **Data:** https://doi.org/10.5281/zenodo.1489920
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.00 GB
- **Size of the generated dataset:** 5.61 GB
- **Total amount of disk used:** 6.62 GB
### Dataset Summary
Hyperpartisan News Detection was a dataset created for PAN @ SemEval 2019 Task 4.
Given a news article text, decide whether it follows a hyperpartisan argumentation, i.e., whether it exhibits blind, prejudiced, or unreasoning allegiance to one party, faction, cause, or person.
There are 2 parts:
- byarticle: Labeled through crowdsourcing on an article basis. The data contains only articles for which a consensus among the crowdsourcing workers existed.
- bypublisher: Labeled by the overall bias of the publisher as provided by BuzzFeed journalists or MediaBiasFactCheck.com.
### 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
#### byarticle
- **Size of downloaded dataset files:** 1.00 MB
- **Size of the generated dataset:** 2.80 MB
- **Total amount of disk used:** 3.81 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"hyperpartisan": true,
"published_at": "2020-01-01",
"text": "\"<p>This is a sample article which will contain lots of text</p>\\n \\n<p>Lorem ipsum dolor sit amet, consectetur adipiscing el...",
"title": "Example article 1",
"url": "http://www.example.com/example1"
}
```
#### bypublisher
- **Size of downloaded dataset files:** 1.00 GB
- **Size of the generated dataset:** 5.61 GB
- **Total amount of disk used:** 6.61 GB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"bias": 3,
"hyperpartisan": false,
"published_at": "2020-01-01",
"text": "\"<p>This is a sample article which will contain lots of text</p>\\n \\n<p>Phasellus bibendum porta nunc, id venenatis tortor fi...",
"title": "Example article 4",
"url": "https://example.com/example4"
}
```
### Data Fields
The data fields are the same among all splits.
#### byarticle
- `text`: a `string` feature.
- `title`: a `string` feature.
- `hyperpartisan`: a `bool` feature.
- `url`: a `string` feature.
- `published_at`: a `string` feature.
#### bypublisher
- `text`: a `string` feature.
- `title`: a `string` feature.
- `hyperpartisan`: a `bool` feature.
- `url`: a `string` feature.
- `published_at`: a `string` feature.
- `bias`: a classification label, with possible values including `right` (0), `right-center` (1), `least` (2), `left-center` (3), `left` (4).
### Data Splits
#### byarticle
| |train|
|---------|----:|
|byarticle| 645|
#### bypublisher
| |train |validation|
|-----------|-----:|---------:|
|bypublisher|600000| 150000|
## 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 collection (including labels) are licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@inproceedings{kiesel-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 4: Hyperpartisan News Detection",
author = "Kiesel, Johannes and
Mestre, Maria and
Shukla, Rishabh and
Vincent, Emmanuel and
Adineh, Payam and
Corney, David and
Stein, Benno and
Potthast, Martin",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2145",
doi = "10.18653/v1/S19-2145",
pages = "829--839",
abstract = "Hyperpartisan news is news that takes an extreme left-wing or right-wing standpoint. If one is able to reliably compute this meta information, news articles may be automatically tagged, this way encouraging or discouraging readers to consume the text. It is an open question how successfully hyperpartisan news detection can be automated, and the goal of this SemEval task was to shed light on the state of the art. We developed new resources for this purpose, including a manually labeled dataset with 1,273 articles, and a second dataset with 754,000 articles, labeled via distant supervision. The interest of the research community in our task exceeded all our expectations: The datasets were downloaded about 1,000 times, 322 teams registered, of which 184 configured a virtual machine on our shared task cloud service TIRA, of which in turn 42 teams submitted a valid run. The best team achieved an accuracy of 0.822 on a balanced sample (yes : no hyperpartisan) drawn from the manually tagged corpus; an ensemble of the submitted systems increased the accuracy by 0.048.",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset. | 9,740 | [
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ChristophSchuhmann/MS_COCO_2017_URL_TEXT | 2021-11-27T15:39:29.000Z | [
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jamescalam/youtube-transcriptions | 2022-10-22T01:20:07.000Z | [
"task_categories:conversational",
"task_categories:question-answering",
"task_categories:text-retrieval",
"task_categories:visual-question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"task_ids:document-retrieval",
"task_ids:visual-question-answering",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:afl-3.0",
"youtube",
"technical",
"speech to text",
"speech",
"video",
"video search",
"audio",
"audio search",
"region:us"
] | jamescalam | null | null | 18 | 336 | 2022-10-13T20:31:27 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- afl-3.0
multilinguality:
- monolingual
pretty_name: Youtube Transcriptions
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- youtube
- technical
- speech to text
- speech
- video
- video search
- audio
- audio search
task_categories:
- conversational
- question-answering
- text-retrieval
- visual-question-answering
task_ids:
- open-domain-qa
- extractive-qa
- document-retrieval
- visual-question-answering
---
The YouTube transcriptions dataset contains technical tutorials (currently from [James Briggs](https://www.youtube.com/c/jamesbriggs), [Daniel Bourke](https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ), and [AI Coffee Break](https://www.youtube.com/c/aicoffeebreak)) transcribed using [OpenAI's Whisper](https://huggingface.co/openai/whisper-large) (large). Each row represents roughly a sentence-length chunk of text alongside the video URL and timestamp.
Note that each item in the dataset contains just a short chunk of text. For most use cases you will likely need to merge multiple rows to create more substantial chunks of text, if you need to do that, this code snippet will help:
```python
from datasets import load_dataset
# first download the dataset
data = load_dataset(
'jamescalam/youtube-transcriptions',
split='train'
)
new_data = [] # this will store adjusted data
window = 6 # number of sentences to combine
stride = 3 # number of sentences to 'stride' over, used to create overlap
for i in range(0, len(data), stride):
i_end = min(len(data)-1, i+window)
if data[i]['title'] != data[i_end]['title']:
# in this case we skip this entry as we have start/end of two videos
continue
# create larger text chunk
text = ' '.join(data[i:i_end]['text'])
# add to adjusted data list
new_data.append({
'start': data[i]['start'],
'end': data[i_end]['end'],
'title': data[i]['title'],
'text': text,
'id': data[i]['id'],
'url': data[i]['url'],
'published': data[i]['published']
})
``` | 2,135 | [
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argilla/databricks-dolly-15k-curated-multilingual | 2023-06-14T07:47:54.000Z | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:es",
"language:de",
"language:fr",
"license:cc-by-sa-3.0",
"machine-translated",
"instruction-following",
"region:us"
] | argilla | null | null | 35 | 336 | 2023-04-13T12:18:17 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: context
dtype: string
- name: response
dtype: string
- name: category
dtype: string
- name: instruction_original_en
dtype: string
- name: context_original_en
dtype: string
- name: response_original_en
dtype: string
- name: id
dtype: int64
splits:
- name: de
num_bytes: 25985140
num_examples: 15015
- name: en
num_bytes: 24125109
num_examples: 15015
- name: es
num_bytes: 25902709
num_examples: 15015
- name: fr
num_bytes: 26704314
num_examples: 15015
download_size: 65586669
dataset_size: 102717272
license: cc-by-sa-3.0
task_categories:
- text-generation
- text2text-generation
language:
- es
- de
- fr
tags:
- machine-translated
- instruction-following
pretty_name: Databrick Dolly Instructions Multilingual
size_categories:
- 10K<n<100K
---
# Dataset Card for "databricks-dolly-15k-curated-multilingual"
A curated and multilingual version of the Databricks Dolly instructions dataset. It includes a programmatically and manually corrected version of the original `en` dataset. See below.
**STATUS**:
Currently, the original Dolly v2 English version has been curated combining automatic processing and collaborative human curation using Argilla (~400 records have been manually edited and fixed). The following graph shows a summary about the number of edited fields.
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage: https://huggingface.co/datasets/argilla/databricks-dolly-15k-multilingual/**
- **Repository: https://huggingface.co/datasets/argilla/databricks-dolly-15k-multilingual/**
- **Paper:**
- **Leaderboard:**
- **Point of Contact: contact@argilla.io, https://github.com/argilla-io/argilla**
### Dataset Summary
This dataset collection is a curated and machine-translated version of the `databricks-dolly-15k` [dataset](https://github.com/databrickslabs/dolly/tree/master/data) originally created by Databricks, Inc. in 2023.
The goal is to give practitioners a starting point for training open-source instruction-following models with better-quality English data and translated data beyond English. However, as the translation quality will not be perfect, we highly recommend dedicating time to curate and fix translation issues. Below we explain how to load the datasets into [Argilla for data curation and fixing](https://github.com/argilla-io/argilla). Additionally, we'll be improving the datasets made available here, with the help of different communities.
Currently, the original English version has been curated combining automatic processing and collaborative human curation using Argilla (~400 records have been manually edited and fixed). The following graph shows a summary of the number of edited fields.
The main issues (likely many issues still remaining) are the following:
1. Some labelers misunderstood the usage of the `context` field. This `context` field is used as part of the prompt for instruction-tuning and in other works it's called `input` (e.g., Alpaca). Likely, the name context, has led to some labelers using it to provide the full context of where they have extracted the response. This is problematic for some types of tasks (summarization, closed-qa or information-extraction) because sometimes the context is shorter than or unrelated to summaries, or the information cannot be extracted from the context (closed-qa, information-extraction).
2. Some labelers misunderstood the way to give instructions for summarization or closed-qa, for example, they ask: Who is Thomas Jefferson? then provide a very long context and a response equally long.
We programmatically identified records with these potential issues and ran a campaign to fix it and as a result more than 400 records have been adapted. See below for statistics:
As a result of this curation process the content of the fields has been reduced, counted in number of tokens, especially for the responses:
If you want to browse and curate your dataset with Argilla, you can:
1. [Duplicate this Space](https://huggingface.co/spaces/argilla/dolly-multilingual-curation/settings?duplicate=true). IMPORTANT: The Space's Visibility need to be Public, but you can setup your own password and API KEYS [following this guide](https://docs.argilla.io/en/latest/getting_started/installation/deployments/huggingface-spaces.html#setting-up-secret-environment-variables).
2. Setup two secrets: `HF_TOKEN` and `LANG` for indicating the language split
3. Login with `admin`/`12345678` and start browsing and labelling.
4. Start labeling. Every 5 min the validations will be stored on a Hub dataset in your personal HF space.
5. Please get in touch to contribute fixes and improvements to the source datasets.
There's one split per language:
```python
from datasets import load_dataset
# loads all splits
load_dataset("argilla/databricks-dolly-15k-curate-multilingual")
# loads Spanish splits
load_dataset("argilla/databricks-dolly-15k-curated-multilingual", split="es")
```
### Supported Tasks and Leaderboards
As described in the README of the original dataset, this dataset can be used for:
* Training LLMs
* Synthetic Data Generation
* Data Augmentation
### Languages
Currently: `es`, `fr`, `de`, `en`
Join Argilla [Slack community](https://join.slack.com/t/rubrixworkspace/shared_invite/zt-whigkyjn-a3IUJLD7gDbTZ0rKlvcJ5g) if you want to help us include other languages.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
There's one split per language:
```python
from datasets import load_dataset
# loads all splits
load_dataset("argilla/databricks-dolly-15k-multilingual")
# loads Spanish splits
load_dataset("argilla/databricks-dolly-15k-multilingual", split="es")
```
## Dataset Creation
These datasets have been translated using the DeepL API from the original English dataset between the 13th and 14th of April
### Curation Logbook
* 28/04/23: Removed references from Wikipedia copy pastes for 8113 rows. Applied to context and response fields with the following regex: `r'\[[\w]+\]'`
### Source Data
#### Initial Data Collection and Normalization
Refer to the [original dataset](https://github.com/databrickslabs/dolly/tree/master/data) for more information.
#### Who are the source language producers?
[More Information Needed]
### Annotations
Annotations are planned but not performed yet.
#### 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
This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Creative Commons Attribution-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-sa/3.0/legalcode).
**Original dataset Owner: Databricks, Inc.**
### Citation Information
[More Information Needed] | 8,482 | [
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SetFit/ade_corpus_v2_classification | 2022-09-05T14:14:53.000Z | [
"region:us"
] | SetFit | null | null | 0 | 335 | 2022-09-05T11:20:19 | # ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for classification if a sentence is ADE-related (True) or not (False).
**Train size: 17,637**
**Test size: 5,879**
[Source dataset](https://huggingface.co/datasets/ade_corpus_v2)
[Paper](https://www.sciencedirect.com/science/article/pii/S1532046412000615)
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open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1 | 2023-10-26T01:30:07.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 335 | 2023-09-27T15:31:20 | ---
pretty_name: Evaluation run of mistralai/Mistral-7B-v0.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-26T01:29:53.089924](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1/blob/main/results_2023-10-26T01-29-53.089924.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.001572986577181208,\n\
\ \"em_stderr\": 0.00040584511324177333,\n \"f1\": 0.06143666107382555,\n\
\ \"f1_stderr\": 0.0013713061256604275,\n \"acc\": 0.4828952478289202,\n\
\ \"acc_stderr\": 0.011049793459166254\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.001572986577181208,\n \"em_stderr\": 0.00040584511324177333,\n\
\ \"f1\": 0.06143666107382555,\n \"f1_stderr\": 0.0013713061256604275\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.17968157695223655,\n \
\ \"acc_stderr\": 0.010575119964242255\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7861089187056038,\n \"acc_stderr\": 0.011524466954090254\n\
\ }\n}\n```"
repo_url: https://huggingface.co/mistralai/Mistral-7B-v0.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|arc:challenge|25_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T23_48_21.884715
path:
- '**/details_harness|drop|3_2023-10-25T23-48-21.884715.parquet'
- split: 2023_10_26T01_29_53.089924
path:
- '**/details_harness|drop|3_2023-10-26T01-29-53.089924.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-26T01-29-53.089924.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T23_48_21.884715
path:
- '**/details_harness|gsm8k|5_2023-10-25T23-48-21.884715.parquet'
- split: 2023_10_26T01_29_53.089924
path:
- '**/details_harness|gsm8k|5_2023-10-26T01-29-53.089924.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-26T01-29-53.089924.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hellaswag|10_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-27T15-30-59.039834.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-27T15-30-59.039834.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T23_48_21.884715
path:
- '**/details_harness|winogrande|5_2023-10-25T23-48-21.884715.parquet'
- split: 2023_10_26T01_29_53.089924
path:
- '**/details_harness|winogrande|5_2023-10-26T01-29-53.089924.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-26T01-29-53.089924.parquet'
- config_name: results
data_files:
- split: 2023_09_27T15_30_59.039834
path:
- results_2023-09-27T15-30-59.039834.parquet
- split: 2023_10_25T23_48_21.884715
path:
- results_2023-10-25T23-48-21.884715.parquet
- split: 2023_10_26T01_29_53.089924
path:
- results_2023-10-26T01-29-53.089924.parquet
- split: latest
path:
- results_2023-10-26T01-29-53.089924.parquet
---
# Dataset Card for Evaluation run of mistralai/Mistral-7B-v0.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/mistralai/Mistral-7B-v0.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-26T01:29:53.089924](https://huggingface.co/datasets/open-llm-leaderboard/details_mistralai__Mistral-7B-v0.1/blob/main/results_2023-10-26T01-29-53.089924.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.001572986577181208,
"em_stderr": 0.00040584511324177333,
"f1": 0.06143666107382555,
"f1_stderr": 0.0013713061256604275,
"acc": 0.4828952478289202,
"acc_stderr": 0.011049793459166254
},
"harness|drop|3": {
"em": 0.001572986577181208,
"em_stderr": 0.00040584511324177333,
"f1": 0.06143666107382555,
"f1_stderr": 0.0013713061256604275
},
"harness|gsm8k|5": {
"acc": 0.17968157695223655,
"acc_stderr": 0.010575119964242255
},
"harness|winogrande|5": {
"acc": 0.7861089187056038,
"acc_stderr": 0.011524466954090254
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
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doc2dial | 2022-11-18T19:58:53.000Z | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-3.0",
"region:us"
] | null | Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. It includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets this dataset covers a variety of dialogue scenes in information-seeking conversations. | @inproceedings{feng-etal-2020-doc2dial,
title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
author = "Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
} | 2 | 334 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
paperswithcode_id: doc2dial
pretty_name: doc2dial
dataset_info:
- config_name: dialogue_domain
features:
- name: dial_id
dtype: string
- name: doc_id
dtype: string
- name: domain
dtype: string
- name: turns
list:
- name: turn_id
dtype: int32
- name: role
dtype: string
- name: da
dtype: string
- name: references
list:
- name: sp_id
dtype: string
- name: label
dtype: string
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 6924209
num_examples: 3474
- name: validation
num_bytes: 1315815
num_examples: 661
download_size: 5879543
dataset_size: 8240024
- config_name: document_domain
features:
- name: domain
dtype: string
- name: doc_id
dtype: string
- name: title
dtype: string
- name: doc_text
dtype: string
- name: spans
list:
- name: id_sp
dtype: string
- name: tag
dtype: string
- name: start_sp
dtype: int32
- name: end_sp
dtype: int32
- name: text_sp
dtype: string
- name: title
dtype: string
- name: parent_titles
dtype: string
- name: id_sec
dtype: string
- name: start_sec
dtype: int32
- name: text_sec
dtype: string
- name: end_sec
dtype: int32
- name: doc_html_ts
dtype: string
- name: doc_html_raw
dtype: string
splits:
- name: train
num_bytes: 204874908
num_examples: 3416
download_size: 5879543
dataset_size: 204874908
- config_name: doc2dial_rc
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: domain
dtype: string
splits:
- name: validation
num_bytes: 22705288
num_examples: 3972
- name: train
num_bytes: 114778994
num_examples: 20431
download_size: 5879543
dataset_size: 137484282
---
# Dataset Card for doc2dial
## 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://doc2dial.github.io
- **Repository:** [Needs More Information]
- **Paper:** https://www.aclweb.org/anthology/2020.emnlp-main.652.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Doc2dial is dataset of goal-oriented dialogues that are grounded in the associated documents. It includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets this dataset covers a variety of dialogue scenes in information-seeking conversations.
### Supported Tasks and Leaderboards
> Supported Task: [Shared Task](https://doc2dial.github.io/workshop2021/shared.html) hosted by DialDoc21 at ACL.
> Leaderboard: [LINK](https://eval.ai/web/challenges/challenge-page/793)
### Languages
English
## Dataset Structure
### Data Instances
Sample data instance for `dialogue_domain` :
```
{
"dial_id": "9f44c1539efe6f7e79b02eb1b413aa43",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0",
"domain": "dmv",
"turns": [
{
"da": "query_condition",
"references": [
{
"sp_id": "4",
"label": "precondition"
}
],
"role": "user",
"turn_id": 1,
"utterance": "Hello, I forgot o update my address, can you help me with that?"
},
{
"da": "response_solution",
"references": [
{
"sp_id": "6",
"label": "solution"
},
{
"sp_id": "7",
"label": "solution"
},
{
"sp_id": "4",
"label": "references"
}
],
"role": "agent",
"turn_id": 2,
"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles."
},
{
"da": "query_solution",
"references": [
{
"sp_id": "56",
"label": "solution"
},
{
"sp_id": "48",
"label": "references"
}
],
"role": "user",
"turn_id": 3,
"utterance": "Can I do my DMV transactions online?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "56",
"label": "solution"
},
{
"sp_id": "48",
"label": "references"
}
],
"role": "agent",
"turn_id": 4,
"utterance": "Yes, you can sign up for MyDMV for all the online transactions needed."
},
{
"da": "query_condition",
"references": [
{
"sp_id": "48",
"label": "precondition"
}
],
"role": "user",
"turn_id": 5,
"utterance": "Thanks, and in case I forget to bring all of the documentation needed to the DMV office, what can I do?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "49",
"label": "solution"
},
{
"sp_id": "50",
"label": "solution"
},
{
"sp_id": "52",
"label": "solution"
},
{
"sp_id": "48",
"label": "references"
}
],
"role": "agent",
"turn_id": 6,
"utterance": "This happens often with our customers so that's why our website and MyDMV are so useful for our customers. Just check if you can make your transaction online so you don't have to go to the DMV Office."
},
{
"da": "query_solution",
"references": [
{
"sp_id": "6",
"label": "solution"
},
{
"sp_id": "7",
"label": "solution"
},
{
"sp_id": "4",
"label": "references"
}
],
"role": "user",
"turn_id": 7,
"utterance": "Ok, and can you tell me again where should I report my new address?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "6",
"label": "solution"
},
{
"sp_id": "7",
"label": "solution"
},
{
"sp_id": "4",
"label": "references"
}
],
"role": "agent",
"turn_id": 8,
"utterance": "Sure. Any change of address must be reported to the DMV, that's for the address associated with your license and any of your vehicles."
},
{
"da": "query_condition",
"references": [
{
"sp_id": "40",
"label": "precondition"
}
],
"role": "user",
"turn_id": 9,
"utterance": "Can you tell me more about Traffic points and their cost?"
},
{
"da": "respond_solution",
"references": [
{
"sp_id": "41",
"label": "solution"
},
{
"sp_id": "43",
"label": "solution"
},
{
"sp_id": "40",
"label": "references"
}
],
"role": "agent",
"turn_id": 10,
"utterance": "Traffic points is the system used by DMV to track dangerous drivers. The cost of the traffic points is independent of the DRA, so you get a separate charge based on the total points you accumulate."
}
]
}
```
Sample data instance for `document_domain` :
```
{
"doc_id": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1_0",
"domain": "ssa",
"doc_html_raw": "<main class=\"content\" id=\"content\" role=\"main\">\n\n<section>\n\n<div>\n<h2>\nBenefits Planner: Retirement\n</h2>\n</div>\n</section>\n\n\n<section>\n\n<div>\n\n<div>\n\n\n</div>\n\n<article>\n<section>\n\n<h3>Online Calculator (WEP Version)</h3>\n<p>The calculator shown below allows you to estimate your Social Security benefit.\nHowever, for the most accurate estimates, <a>use the Detailed Calculator</a>.</p>\n<p>You need to enter all your past earnings\n, which are shown on your <a>online </a>.</p>\n\n<p>Please Note:</p>\n<ul class=\"browser-default\">\n<li>The Online Calculator is updated periodically<span>*</span> with new benefit increases and other benefit amounts. Therefore, it is likely that your benefit estimates in the future will differ from those calculated today.</li>\n<li>The Online Calculator works on PCs and Macs with Javascript enabled.</li>\n<li>Some browsers may not allow you to print the table below. </li>\n</ul>\n<p></p>\n\n<div>\nThe Online Calculator temporarily stores information on your local computer while your browser is open. To protect your personal information, you should close your browser after you have finished your estimate.\n</div>\n<p></p>\n\n<div>\n<p>Note: If your birthday is on January 1st, we figure your benefit as if your birthday was in the previous year.</p>\n<p>If you qualify for benefits as a Survivor, your <a>full retirement age for survivors benefits</a> may be different.</p></div>\n\n<div>\n</div></section></article></div></section></main>",
"doc_html_ts": "<main><section><div><h2 sent_id=\"1\" text_id=\"1\">Benefits Planner: Retirement</h2></div></section><section><div><article><section><h3 sent_id=\"2\" text_id=\"2\">Online Calculator (WEP Version)</h3><div tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\" text_id=\"3\">The calculator shown below allows you to estimate your Social Security benefit .</u></u><u sent_id=\"4\" tag_id=\"1\"><u sent_id=\"4\" tag_id=\"1\" text_id=\"4\">However ,</u><u sent_id=\"4\" tag_id=\"1\" text_id=\"5\">for the most accurate estimates ,</u><u sent_id=\"4\" tag_id=\"1\" text_id=\"6\">use the Detailed Calculator .</u></u></div><div tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\" text_id=\"7\">You need to enter all your past earnings , which are shown on your online .</u></u></div><div tag_id=\"3\"><u sent_id=\"6\" tag_id=\"3\"><u sent_id=\"6\" tag_id=\"3\" text_id=\"8\">Please Note:</u></u></div><ul class=\"browser-default\" tag_id=\"3\"><li tag_id=\"3\"><div tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\" text_id=\"9\">The Online Calculator is updated periodically * with new benefit increases and other benefit amounts .</u></u><u sent_id=\"10\" tag_id=\"3\"><u sent_id=\"10\" tag_id=\"3\" text_id=\"10\">Therefore ,</u><u sent_id=\"10\" tag_id=\"3\" text_id=\"11\">it is likely that your benefit estimates in the future will differ from those calculated today .</u></u></div></li><li tag_id=\"3\"><u sent_id=\"11\" tag_id=\"3\"><u sent_id=\"11\" tag_id=\"3\" text_id=\"12\">The Online Calculator works on PCs and Macs with Javascript enabled .</u></u></li><li tag_id=\"3\"><u sent_id=\"12\" tag_id=\"3\"><u sent_id=\"12\" tag_id=\"3\" text_id=\"13\">Some browsers may not allow you to print the table below .</u></u></li></ul><div>The Online Calculator temporarily stores information on your local computer while your browser is open. To protect your personal information, you should close your browser after you have finished your estimate.</div><div><div tag_id=\"4\"><u sent_id=\"13\" tag_id=\"4\"><u sent_id=\"13\" tag_id=\"4\" text_id=\"14\">Note:</u></u><u sent_id=\"14\" tag_id=\"4\"><u sent_id=\"14\" tag_id=\"4\" text_id=\"15\">If your birthday is on January 1st ,</u><u sent_id=\"14\" tag_id=\"4\" text_id=\"16\">we figure your benefit as if your birthday was in the previous year .</u></u></div><div tag_id=\"5\"><u sent_id=\"15\" tag_id=\"5\"><u sent_id=\"15\" tag_id=\"5\" text_id=\"17\">If you qualify for benefits as a Survivor ,</u><u sent_id=\"15\" tag_id=\"5\" text_id=\"18\">your full retirement age for survivors benefits may be different .</u></u></div></div></section></article></div></section></main>",
"doc_text": "\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \nThe calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. You need to enter all your past earnings, which are shown on your online. Please Note: The Online Calculator is updated periodically * with new benefit increases and other benefit amounts. Therefore , it is likely that your benefit estimates in the future will differ from those calculated today. The Online Calculator works on PCs and Macs with Javascript enabled. Some browsers may not allow you to print the table below. Note: If your birthday is on January 1st , we figure your benefit as if your birthday was in the previous year. If you qualify for benefits as a Survivor , your full retirement age for survivors benefits may be different. ",
"title": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1",
"spans": [
{
"end_sec": 32,
"end_sp": 32,
"id_sec": "t_0",
"id_sp": "1",
"parent_titles": "[]",
"start_sec": 0,
"start_sp": 0,
"tag": "h2",
"text_sec": "\n\nBenefits Planner: Retirement \n",
"text_sp": "\n\nBenefits Planner: Retirement \n",
"title": "Benefits Planner: Retirement"
},
{
"end_sec": 67,
"end_sp": 67,
"id_sec": "t_1",
"id_sp": "2",
"parent_titles": "[{'id_sp': '1', 'text': 'Benefits Planner: Retirement', 'level': 'h2'}]",
"start_sec": 32,
"start_sp": 32,
"tag": "h3",
"text_sec": "\n\nOnline Calculator (WEP Version) \n",
"text_sp": "\n\nOnline Calculator (WEP Version) \n",
"title": "Online Calculator (WEP Version)"
},
{
"end_sec": 220,
"end_sp": 147,
"id_sec": "1",
"id_sp": "3",
"parent_titles": "[]",
"start_sec": 67,
"start_sp": 67,
"tag": "u",
"text_sec": "The calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. ",
"text_sp": "The calculator shown below allows you to estimate your Social Security benefit. ",
"title": "Online Calculator (WEP Version)"
}
]
}
```
Sample data instance for `doc2dial_rc` :
```
{
"id": "78f72b08b43791a4a70363fe62b8de08_1",
"is_impossible": false,
"question": "Hello, I want to know about the retirement plan.",
"answers": {
"answer_start": [
0
],
"text": [
"\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \n"
]
},
"context": "\n\nBenefits Planner: Retirement \n\n\nOnline Calculator (WEP Version) \nThe calculator shown below allows you to estimate your Social Security benefit. However , for the most accurate estimates , use the Detailed Calculator. You need to enter all your past earnings, which are shown on your online. Please Note: The Online Calculator is updated periodically * with new benefit increases and other benefit amounts. Therefore , it is likely that your benefit estimates in the future will differ from those calculated today. The Online Calculator works on PCs and Macs with Javascript enabled. Some browsers may not allow you to print the table below. Note: If your birthday is on January 1st , we figure your benefit as if your birthday was in the previous year. If you qualify for benefits as a Survivor , your full retirement age for survivors benefits may be different. ",
"title": "Benefits Planner: Retirement | Online Calculator (WEP Version)#1_0",
"domain": "ssa"
}
```
### Data Fields
For `document_domain`,
- `doc_id`: the ID of a document;
- `title`: the title of the document;
- `domain`: the domain of the document;
- `doc_text`: the text content of the document (without HTML markups);
- `doc_html_ts`: the document content with HTML markups and the annotated spans that are indicated by `text_id` attribute, which corresponds to `id_sp`.
- `doc_html_raw`: the document content with HTML markups and without span annotations.
- `spans`: key-value pairs of all spans in the document, with `id_sp` as key. Each span includes the following,
- `id_sp`: the id of a span as noted by `text_id` in `doc_html_ts`;
- `start_sp`/ `end_sp`: the start/end position of the text span in `doc_text`;
- `text_sp`: the text content of the span.
- `id_sec`: the id of the (sub)section (e.g. `<p>`) or title (`<h2>`) that contains the span.
- `start_sec` / `end_sec`: the start/end position of the (sub)section in `doc_text`.
- `text_sec`: the text of the (sub)section.
- `title`: the title of the (sub)section.
- `parent_titles`: the parent titles of the `title`.
For `dialogue_domain`:
- `dial_id`: the ID of a dialogue;
- `doc_id`: the ID of the associated document;
- `domain`: domain of the document;
- `turns`: a list of dialogue turns. Each turn includes,
- `turn_id`: the time order of the turn;
- `role`: either "agent" or "user";
- `da`: dialogue act;
- `references`: the grounding span (`id_sp`) in the associated document. If a turn is an irrelevant turn, i.e., `da` ends with "ood", `reference` is empty. **Note** that spans with labels "*precondition*"/"*solution*" are the actual grounding spans. Spans with label "*reference*" are the related titles or contextual reference, which is used for the purpose of describing a dialogue scene better to crowd contributors.
- `utterance`: the human-generated utterance based on the dialogue scene.
For `doc2dial_rc`, this conforms to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) data format. For how to load Doc2Dial data for reading comprehension task, please refer [here](https://github.com/doc2dial/sharedtask-dialdoc2021).
- `id`: the ID of a QA instance;
- `question`: user query;
- `answers`: the answers that are grounded in the associated document;
- `answer_start`: the start position of the grounding span in the associated document (`context`);
- `text`: the text content of the grounding span;
- `title`: the title of the associated document;
- `domain`: the domain of the associated document;
- `context`: the text content of the associated document (without HTML markups).
### Data Splits
Training & dev split for dialogue domain
Training split only for document domain
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Song Feng, Hui Wan, Chulaka Gunasekara, Siva Sankalp Patel,Sachindra Joshi. Luis A. Lastras
### Licensing Information
Creative Commons Attribution 3.0 Unported
### Citation Information
@inproceedings{feng-etal-2020-doc2dial,
title = "doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset",
author = "Feng, Song and Wan, Hui and Gunasekara, Chulaka and Patel, Siva and Joshi, Sachindra and Lastras, Luis",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.652",
}
### Contributions
Thanks to [@songfeng](https://github.com/songfeng), [@KMFODA](https://github.com/KMFODA) for adding this dataset. | 22,650 | [
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web_of_science | 2023-04-05T13:42:58.000Z | [
"language:en",
"region:us"
] | null | The Web Of Science (WOS) dataset is a collection of data of published papers
available from the Web of Science. WOS has been released in three versions: WOS-46985, WOS-11967 and WOS-5736. WOS-46985 is the
full dataset. WOS-11967 and WOS-5736 are two subsets of WOS-46985. | @inproceedings{kowsari2017HDLTex,
title={HDLTex: Hierarchical Deep Learning for Text Classification},
author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},
booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},
year={2017},
organization={IEEE}
} | 2 | 334 | 2022-03-02T23:29:22 | ---
language:
- en
paperswithcode_id: web-of-science-dataset
pretty_name: Web of Science Dataset
dataset_info:
- config_name: WOS5736
features:
- name: input_data
dtype: string
- name: label
dtype: int32
- name: label_level_1
dtype: int32
- name: label_level_2
dtype: int32
splits:
- name: train
num_bytes: 8051533
num_examples: 5736
download_size: 60222421
dataset_size: 8051533
- config_name: WOS11967
features:
- name: input_data
dtype: string
- name: label
dtype: int32
- name: label_level_1
dtype: int32
- name: label_level_2
dtype: int32
splits:
- name: train
num_bytes: 16248391
num_examples: 11967
download_size: 60222421
dataset_size: 16248391
- config_name: WOS46985
features:
- name: input_data
dtype: string
- name: label
dtype: int32
- name: label_level_1
dtype: int32
- name: label_level_2
dtype: int32
splits:
- name: train
num_bytes: 65471726
num_examples: 46985
download_size: 60222421
dataset_size: 65471726
---
# Dataset Card for "web_of_science"
## 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://data.mendeley.com/datasets/9rw3vkcfy4/6](https://data.mendeley.com/datasets/9rw3vkcfy4/6)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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:** 180.67 MB
- **Size of the generated dataset:** 89.81 MB
- **Total amount of disk used:** 270.48 MB
### Dataset Summary
Copyright (c) 2017 Kamran Kowsari
Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal
in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset.
If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification
Description of Dataset:
Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736
Each folder contains:
-X.txt
-Y.txt
-YL1.txt
-YL2.txt
X is input data that include text sequences
Y is target value
YL1 is target value of level one (parent label)
YL2 is target value of level one (child label)
Web of Science Dataset WOS-5736
-This dataset contains 5,736 documents with 11 categories which include 3 parents categories.
### 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
#### WOS11967
- **Size of downloaded dataset files:** 60.22 MB
- **Size of the generated dataset:** 16.25 MB
- **Total amount of disk used:** 76.48 MB
An example of 'train' looks as follows.
```
```
#### WOS46985
- **Size of downloaded dataset files:** 60.22 MB
- **Size of the generated dataset:** 65.50 MB
- **Total amount of disk used:** 125.72 MB
An example of 'train' looks as follows.
```
```
#### WOS5736
- **Size of downloaded dataset files:** 60.22 MB
- **Size of the generated dataset:** 8.05 MB
- **Total amount of disk used:** 68.27 MB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### WOS11967
- `input_data`: a `string` feature.
- `label`: a `int32` feature.
- `label_level_1`: a `int32` feature.
- `label_level_2`: a `int32` feature.
#### WOS46985
- `input_data`: a `string` feature.
- `label`: a `int32` feature.
- `label_level_1`: a `int32` feature.
- `label_level_2`: a `int32` feature.
#### WOS5736
- `input_data`: a `string` feature.
- `label`: a `int32` feature.
- `label_level_1`: a `int32` feature.
- `label_level_2`: a `int32` feature.
### Data Splits
| name |train|
|--------|----:|
|WOS11967|11967|
|WOS46985|46985|
|WOS5736 | 5736|
## 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
```
@inproceedings{kowsari2017HDLTex,
title={HDLTex: Hierarchical Deep Learning for Text Classification},
author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E},
booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on},
year={2017},
organization={IEEE}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. | 8,273 | [
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Kyle1668/AG-Tweets | 2023-08-09T22:22:37.000Z | [
"region:us"
] | Kyle1668 | null | null | 0 | 334 | 2023-06-29T22:07:56 | ---
pretty_name: AG News Tweets
---
\subsection{Motivation}
AG News is a four-way topic classification task introduced in \cite{Zhang2015CharacterlevelCN}. In this setup, a task model must classify whether a given news article is about world events (\textbf{\textit{World}}), sports and athletics (\textbf{\textit{Sports}}), business and economics (\textbf{\textit{Business}}), and scientific developments (\textbf{\textit{Sci/Tech}}). The test set on HuggingFace (\url{huggingface.co/datasets/ag_news}) is composed of 7,600 examples equally balanced across the four classes.
News topic classification presents a promising opportunity for largely isolating the effect of writing style shifts. Existing deep learning methods also perform well on this dataset with accuracy reaching higher than 90\% (\url{paperswithcode.com/sota/text-classification-on-ag-news}).
Another motivation for this particular task is the common risk of data augmentation inadvertently flipping the label/semantics of the text \cite{Bayer2021ASO}. Unlike other tasks such as sentiment classification or subtle hate speech, the topic of a news article is unlikely to change during augmentation, thus preserving the original label.
\subsection{Creation}
We used GPT-3.5 Turbo \cite{brown2020language} (6/7/23 version) for style transfer. We did an initial pass through all 7,600 examples using a conservative "V1" prompt and greedy decoding. Calls were made using the OpenAI Python SDK with top\_p and temperature set to zero. The data was then lightly preprocessed to reduce the number of examples that began with \textbf{BREAKING NEWS} flanked my emojis.
512 of the initial model responses did not result in satisfactory generations. These were typical cases where the generated text was almost indiscernible from the original text or the generation was entirely emojis. We called GPT-3.5 Turbo again with an updated prompt and hyperparameters (temperature=0.7, top\_p=0.9, frequency\_penalty=0.5, presence\_penalty=0.5) for these examples. Whereas all the first-pass generations did not have any instructions to the model as to the sentiment/mood of the hypothetical post author, we purposefully instructed the model to "\textit{Add some flare with humor, anger, or sarcasm.}" in the generation.
It's important to note that we did not enforce Twitter's character limit. These sequences should be considered as more broadly inspired by social media posts rather than following the exact specifications of Twitter posts. We also did not manually review every sequence in the dataset to confirm that the original label was preserved. GPT 3.5 Turbo also hallucinates facts, such as adding the hashtag \#Olympics2021 even though the original dataset was created in 2015. | 2,750 | [
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GonzaloA/fake_news | 2022-07-04T18:09:58.000Z | [
"region:us"
] | GonzaloA | null | null | 6 | 333 | 2022-03-02T23:29:22 | TODO: Add YAML tags here. Copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 30k<n<50k
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
- intent-classification
pretty_name: GonzaloA / Fake News
---
# Dataset Card for [Fake_News_TFG]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [GonzaloA / fake_news]
- **Paper:** [Título del TFG]
- **Leaderboard:**
- **Point of Contact:** [Gonzalo Álvarez Hervás](mailto:g.alvarez.2018@alumnos.urjc.es)
### Dataset Summary
The GonzaloA / Fake_News_TFG Dataset repository is an English-language dataset containing just over 45k unique news articles. This articles are classified as true (1) or false (0). The current version is the first of the study the Fake News identification using Transformers models.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is code for English as generally spoken in the United States is en-US
## Dataset Structure
The structure of this dataSet is composed by 40587 fields about News. This fields are composed by three types of fields; title of the news, the text or content of the news, and finally, the value of the news, who said if the new are fake (0) or true (1).
### Data Instances
For each instance, there is a string for the title, a string for the article and a label to mark if it's true or false. See the [CNN / Daily Mail dataset viewer](https://huggingface.co/datasets/viewer/?dataset=fake_news&config=3.0.0) to explore more examples.
```
{'id': '1',
'title': Palestinians switch off Christmas lights in Bethlehem in anti-Trump protest'
'text': 'RAMALLAH, West Bank (Reuters) - Palestinians switched off Christmas lights at Jesus traditional birthplace in Bethlehem on Wednesday night in protest at U.S. President Donald Trump s decision to recognize Jerusalem as Israel s capital. A Christmas tree adorned with lights outside Bethlehem s Church of the Nativity, where Christians believe Jesus was born, and another in Ramallah, next to the burial site of former Palestinian leader Yasser Arafat, were plunged into darkness. The Christmas tree was switched off on the order of the mayor today in protest at Trump s decision, said Fady Ghattas, Bethlehem s municipal media officer. He said it was unclear whether the illuminations would be turned on again before the main Christmas festivities. In a speech in Washington, Trump said he had decided to recognize Jerusalem as Israel s capital and move the U.S. embassy to the city. Israeli Prime Minister Benjamin Netanyahu said Trump s move marked the beginning of a new approach to the Israeli-Palestinian conflict and said it was an historic landmark . Arabs and Muslims across the Middle East condemned the U.S. decision, calling it an incendiary move in a volatile region and the European Union and United Nations also voiced alarm at the possible repercussions for any chances of reviving Israeli-Palestinian peacemaking.'
'label': '1'}
```
### Data Fields
- `id`: an integer value to count the rows in the dataset
- `title`: a string that summarize the article
- `text`: a string that contains the article
- `label`: a boolean that mark the article true or false
### Data Splits
The GonzaloA/FakeNews dataset has 3 splits: train, evaluation, and test. Below are the statistics for the version 1.0 of the dataset:
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 24,353 |
| Validation | 8,117 |
| Test | 8,117 |
## Dataset Creation
This dataset was created with python, using pandas library as the main processing data. Also, this dataset are the mix of other datasets which are the same scope, the Fake News. All of the process is available in this repository: https://github.com/G0nz4lo-4lvarez-H3rv4s/FakeNewsDetection
### Source Data
The source data is a mix of multiple fake_news datasets in Kaggle, a platform for train your skills and learnings about Artificial Intelligence. The main datasets who are based this dataset are:
#### Initial Data Collection and Normalization
Version 1.0.0 aimed to support supervised neural methodologies for deep learning and study the new Transformers models in the Natural Language Processing with News of the United States.
### 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
This Dataset is compose for 3 types: Training phase, for training your model of NLP, validation phase, because we need to validate if the training was successful or our model has overfitting, and train phase, who count the probability and the mistakes in the model fine-tuning.
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | 6,731 | [
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rubrix/gutenberg_spacy-ner | 2022-02-24T21:48:13.000Z | [
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SajjadAyoubi/persian_qa | 2021-04-29T06:11:18.000Z | [
"region:us"
] | SajjadAyoubi | \\\\\\\Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia.
The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer". | \@misc{PersianQA,
author = {Sajjad Ayoubi, Mohammad Yasin Davoodeh},
title = {PersianQA: a dataset for Persian Question Answering},
year = 2021,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {url{https://github.com/SajjjadAyobi/PersianQA}},
} | 4 | 332 | 2022-03-02T23:29:22 | # PersianQA: a dataset for Persian Question Answering
Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. The crowd-sourced dataset consists of more than 9,000 entries. Each entry can be either an impossible to answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. Much like the SQuAD2.0 dataset, the impossible or unanswerable questions can be utilized to create a system which "knows that it doesn't know the answer".
On top of that, the dataset has 900 test data available. Moreover, the first models trained on the dataset, Transformers, are available.
All the crowd workers of the dataset are native Persian speakers. Also, it worth mentioning that the contexts are collected from all categories of the Wiki (Historical, Religious, Geography, Science, etc.)
At the moment, each context has 7 pairs of questions with one answer and 3 impossible questions.
## Dataset
### Access/Download
- You can find the data under the [`dataset/`](https://github.com/sajjjadayobi/PersianQA/tree/main/dataset) directory. and use it like this
```python
import read_qa # is avalible at src/read_ds.py
train_ds = read_qa('pqa_train.json')
test_ds = read_qa('pqa_test.json')
```
- Alternatively, you can also access the data through the HuggingFace🤗 datasets library
- First, you need to install datasets using this command in your terminal:
```sh
pip install -q datasets
```
- Then import `persian_qa` dataset using `load_dataset`:
```python
from datasets import load_dataset
dataset = load_dataset("SajjadAyoubi/persian_qa")
```
### Examples
| Title | Context | Question | Answer |
| :---: | :---------------------: | :--------: | :----: |
| خوب، بد، زشت | خوب، بد، زشت یک فیلم درژانر وسترن اسپاگتی حماسی است که توسط سرجو لئونه در سال ۱۹۶۶ در ایتالیا ساخته شد. زبانی که بازیگران این فیلم به آن تکلم میکنند مخلوطی از ایتالیایی و انگلیسی است. این فیلم سومین (و آخرین) فیلم از سهگانهٔ دلار (Dollars Trilogy) سرجو لئونه است. این فیلم در حال حاضر در فهرست ۲۵۰ فیلم برتر تاریخ سینما در وبگاه IMDB با امتیاز ۸٫۸ از ۱۰، رتبهٔ هشتم را به خود اختصاص دادهاست و به عنوان بهترین فیلم وسترن تاریخ سینمای جهان شناخته میشود. «خوب» (کلینت ایستوود، در فیلم، با نام «بلوندی») و «زشت» (ایلای والاک، در فیلم، با نام «توکو») با هم کار میکنند و با شگرد خاصی، به گول زدن کلانترهای مناطق مختلف و پول درآوردن از این راه میپردازند. «بد» (لی وان کلیف) آدمکشی حرفهای است که بهخاطر پول حاضر به انجام هر کاری است. «بد»، که در فیلم او را «اِنجل آیز (اِینجل آیز)» (به انگلیسی: Angel Eyes) صدا میکنند. بهدنبال گنجی است که در طی جنگهای داخلی آمریکا، به دست سربازی به نام «جکسون»، که بعدها به «کارسون» نامش را تغییر داده، مخفی شدهاست. | در فیلم خوب بد زشت شخصیت ها کجایی صحبت می کنند؟ | مخلوطی از ایتالیایی و انگلیسی |
| قرارداد کرسنت | قرارداد کرسنت قراردادی برای فروش روزانه معادل ۵۰۰ میلیون فوت مکعب، گاز ترش میدان سلمان است، که در سال ۱۳۸۱ و در زمان وزارت بیژن نامدار زنگنه در دولت هفتم مابین شرکت کرسنت پترولیوم و شرکت ملی نفت ایران منعقد گردید. مذاکرات اولیه این قرارداد از سال ۱۹۹۷ آغاز شد و در نهایت، سال ۲۰۰۱ (۱۳۸۱) به امضای این تفاهم نامه مشترک انجامید. بر اساس مفاد این قرارداد، مقرر شده بود که از سال ۲۰۰۵ با احداث خط لوله در خلیج فارس، گاز فرآورده نشده میدان سلمان (مخزن مشترک با ابوظبی)، به میزان روزانه ۵۰۰ میلیون فوت مکعب (به قول برخی منابع ۶۰۰ میلیون فوت مکعب) به امارات صادر شود. این قرارداد مطابق قوانین داخلی ایران بسته شده و تنها قرارداد نفتی ایران است که از طرف مقابل خود، تضمین گرفتهاست. اجرای این پروژه در سال ۱۳۸۴ با دلایل ارائه شده از سوی دیوان محاسبات ایران از جمله تغییر نیافتن بهای گاز صادراتی و ثابت ماندن آن در هفت سال اول اجرای قرارداد متوقف شد. این در حالی است که طبق تعریف حقوقی، دیوان محاسبات ایران، حق دخالت در قراردادها، پیش از آنکه قراردادها اجرایی و مالی شوند را ندارد. | طرفین قرار داد کرسنت کیا بودن؟ | کرسنت پترولیوم و شرکت ملی نفت ایران |
| چهارشنبهسوری | چهارشنبهسوری یکی از جشنهای ایرانی است که از غروب آخرین سهشنبه ی ماه اسفند، تا پس از نیمهشب تا آخرین چهارشنبه ی سال، برگزار میشود و برافروختن و پریدن از روی آتش مشخصهٔ اصلی آن است. این جشن، نخستین جشن از مجموعهٔ جشنها و مناسبتهای نوروزی است که با برافروختن آتش و برخی رفتارهای نمادین دیگر، بهصورت جمعی در فضای باز برگزار میشود. بهگفتهٔ ابراهیم پورداوود چهارشنبهسوری ریشه در گاهنبارِ هَمَسْپَتْمَدَم زرتشتیان و نیز جشن نزول فروهرها دارد که شش روز پیش از فرارسیدن نوروز برگزار میشد. احتمال دیگر این است که چهارشنبهسوری بازمانده و شکل تحولیافتهای از جشن سده باشد، که احتمال بعیدی است. علاوه برافروختن آتش، آیینهای مختلف دیگری نیز در بخشهای گوناگون ایران در زمان این جشن انجام میشوند. برای نمونه، در تبریز، مردم به چهارشنبهبازار میروند که با چراغ و شمع، بهطرز زیبایی چراغانی شدهاست. هر خانواده یک آینه، دانههای اسفند، و یک کوزه برای سال نو خریداری میکنند. همهساله شهروندانی از ایران در اثر انفجارهای ناخوشایند مربوط به این جشن، کشته یا مصدوم میشوند. | نام جشن اخرین شنبه ی سال چیست؟ | No Answer |
### Statistic
| Split | # of instances | # of unanswerables | avg. question length | avg. paragraph length | avg. answer length |
| :---: | :------------: | :----------------: | :------------------: | :-------------------: | :----------------: |
| Train | 9,000 | 2,700 | 8.39 | 224.58 | 9.61 |
| Test | 938 | 280 | 8.02 | 220.18 | 5.99 |
The lengths are on the token level.
- for more about data and more example see [here](https://github.com/sajjjadayobi/PersianQA/tree/main/dataset#readme)
## Models
Currently, two models (baseline) on [HuggingFace🤗](https://huggingface.co/SajjadAyoubi/) model hub are using the dataset.
## Citation
Yet, we didn't publish any papers on the work.
However, if you did, please cite us properly with an entry like one below.
```bibtex
@misc{PersianQA,
author = {Ayoubi, Sajjad \& Davoodeh, Mohammad Yasin},
title = {PersianQA: a dataset for Persian Question Answering},
year = 2021,
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/SajjjadAyobi/PersianQA}},
}
```
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sagnikrayc/mctest | 2022-10-25T00:16:37.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"explanations-in-question-answering",
"region:us"
] | sagnikrayc | MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension. | @inproceedings{richardson-etal-2013-mctest,
title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
author = "Richardson, Matthew and
Burges, Christopher J.C. and
Renshaw, Erin",
booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2013",
address = "Seattle, Washington, USA",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D13-1020",
pages = "193--203",
} | 2 | 332 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mctest
language_bcp47:
- en-US
tags:
- explanations-in-question-answering
---
# Dataset Card Creation Guide
## 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:** N/A
- **Repository:** [GitHub](https://github.com/mcobzarenco/mctest/)
- **Paper:** [MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text](https://www.aclweb.org/anthology/D13-1020.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### 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
[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
Microsoft Research License Agreement.
### Citation Information
[More Information Needed]
### Contributions
| 2,944 | [
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open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1 | 2023-09-22T18:17:15.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 332 | 2023-09-22T18:16:15 | ---
pretty_name: Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-22T18:15:51.325850](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1/blob/main/results_2023-09-22T18-15-51.325850.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7060854021313192,\n\
\ \"acc_stderr\": 0.030933452595170018,\n \"acc_norm\": 0.7100920024195952,\n\
\ \"acc_norm_stderr\": 0.030899690738616904,\n \"mc1\": 0.46878824969400246,\n\
\ \"mc1_stderr\": 0.017469364874577537,\n \"mc2\": 0.6440529032814905,\n\
\ \"mc2_stderr\": 0.014798378745240535\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6877133105802048,\n \"acc_stderr\": 0.013542598541688067,\n\
\ \"acc_norm\": 0.735494880546075,\n \"acc_norm_stderr\": 0.01288927294931337\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6876120294761999,\n\
\ \"acc_stderr\": 0.004625198756710245,\n \"acc_norm\": 0.8762198765186218,\n\
\ \"acc_norm_stderr\": 0.0032865748124511866\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8157894736842105,\n \"acc_stderr\": 0.031546980450822305,\n\
\ \"acc_norm\": 0.8157894736842105,\n \"acc_norm_stderr\": 0.031546980450822305\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7471698113207547,\n \"acc_stderr\": 0.026749899771241214,\n\
\ \"acc_norm\": 0.7471698113207547,\n \"acc_norm_stderr\": 0.026749899771241214\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.03216600808802267,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.03216600808802267\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \
\ \"acc_norm\": 0.45,\n \"acc_norm_stderr\": 0.04999999999999999\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n\
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7052023121387283,\n\
\ \"acc_stderr\": 0.03476599607516478,\n \"acc_norm\": 0.7052023121387283,\n\
\ \"acc_norm_stderr\": 0.03476599607516478\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3627450980392157,\n \"acc_stderr\": 0.04784060704105654,\n\
\ \"acc_norm\": 0.3627450980392157,\n \"acc_norm_stderr\": 0.04784060704105654\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.73,\n \"acc_stderr\": 0.04461960433384739,\n \"acc_norm\": 0.73,\n\
\ \"acc_norm_stderr\": 0.04461960433384739\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7106382978723405,\n \"acc_stderr\": 0.02964400657700962,\n\
\ \"acc_norm\": 0.7106382978723405,\n \"acc_norm_stderr\": 0.02964400657700962\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n\
\ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.46825396825396826,\n \"acc_stderr\": 0.0256993528321318,\n \"\
acc_norm\": 0.46825396825396826,\n \"acc_norm_stderr\": 0.0256993528321318\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.49206349206349204,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.49206349206349204,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8290322580645161,\n\
\ \"acc_stderr\": 0.021417242936321586,\n \"acc_norm\": 0.8290322580645161,\n\
\ \"acc_norm_stderr\": 0.021417242936321586\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5566502463054187,\n \"acc_stderr\": 0.03495334582162933,\n\
\ \"acc_norm\": 0.5566502463054187,\n \"acc_norm_stderr\": 0.03495334582162933\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.79,\n \"acc_stderr\": 0.04093601807403325,\n \"acc_norm\"\
: 0.79,\n \"acc_norm_stderr\": 0.04093601807403325\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8424242424242424,\n \"acc_stderr\": 0.028450388805284343,\n\
\ \"acc_norm\": 0.8424242424242424,\n \"acc_norm_stderr\": 0.028450388805284343\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8888888888888888,\n \"acc_stderr\": 0.02239078763821677,\n \"\
acc_norm\": 0.8888888888888888,\n \"acc_norm_stderr\": 0.02239078763821677\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9378238341968912,\n \"acc_stderr\": 0.017426974154240528,\n\
\ \"acc_norm\": 0.9378238341968912,\n \"acc_norm_stderr\": 0.017426974154240528\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7307692307692307,\n \"acc_stderr\": 0.022489389793654817,\n\
\ \"acc_norm\": 0.7307692307692307,\n \"acc_norm_stderr\": 0.022489389793654817\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3148148148148148,\n \"acc_stderr\": 0.02831753349606647,\n \
\ \"acc_norm\": 0.3148148148148148,\n \"acc_norm_stderr\": 0.02831753349606647\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7857142857142857,\n \"acc_stderr\": 0.02665353159671549,\n \
\ \"acc_norm\": 0.7857142857142857,\n \"acc_norm_stderr\": 0.02665353159671549\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"\
acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8972477064220183,\n \"acc_stderr\": 0.013018246509173768,\n \"\
acc_norm\": 0.8972477064220183,\n \"acc_norm_stderr\": 0.013018246509173768\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5925925925925926,\n \"acc_stderr\": 0.033509916046960436,\n \"\
acc_norm\": 0.5925925925925926,\n \"acc_norm_stderr\": 0.033509916046960436\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658928,\n \"\
acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658928\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8945147679324894,\n \"acc_stderr\": 0.01999556072375854,\n \
\ \"acc_norm\": 0.8945147679324894,\n \"acc_norm_stderr\": 0.01999556072375854\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.8026905829596412,\n\
\ \"acc_stderr\": 0.02670985334496796,\n \"acc_norm\": 0.8026905829596412,\n\
\ \"acc_norm_stderr\": 0.02670985334496796\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8549618320610687,\n \"acc_stderr\": 0.030884661089515368,\n\
\ \"acc_norm\": 0.8549618320610687,\n \"acc_norm_stderr\": 0.030884661089515368\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8677685950413223,\n \"acc_stderr\": 0.030922788320445784,\n \"\
acc_norm\": 0.8677685950413223,\n \"acc_norm_stderr\": 0.030922788320445784\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8333333333333334,\n\
\ \"acc_stderr\": 0.03602814176392645,\n \"acc_norm\": 0.8333333333333334,\n\
\ \"acc_norm_stderr\": 0.03602814176392645\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8343558282208589,\n \"acc_stderr\": 0.029208296231259104,\n\
\ \"acc_norm\": 0.8343558282208589,\n \"acc_norm_stderr\": 0.029208296231259104\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5892857142857143,\n\
\ \"acc_stderr\": 0.04669510663875191,\n \"acc_norm\": 0.5892857142857143,\n\
\ \"acc_norm_stderr\": 0.04669510663875191\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.03675668832233188,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.03675668832233188\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\
\ \"acc_stderr\": 0.019875655027867454,\n \"acc_norm\": 0.8974358974358975,\n\
\ \"acc_norm_stderr\": 0.019875655027867454\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542126,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.04512608598542126\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8684546615581098,\n\
\ \"acc_stderr\": 0.01208670521425043,\n \"acc_norm\": 0.8684546615581098,\n\
\ \"acc_norm_stderr\": 0.01208670521425043\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7687861271676301,\n \"acc_stderr\": 0.022698657167855713,\n\
\ \"acc_norm\": 0.7687861271676301,\n \"acc_norm_stderr\": 0.022698657167855713\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.6681564245810055,\n\
\ \"acc_stderr\": 0.015748421208187303,\n \"acc_norm\": 0.6681564245810055,\n\
\ \"acc_norm_stderr\": 0.015748421208187303\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7581699346405228,\n \"acc_stderr\": 0.024518195641879334,\n\
\ \"acc_norm\": 0.7581699346405228,\n \"acc_norm_stderr\": 0.024518195641879334\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7684887459807074,\n\
\ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\
\ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8179012345679012,\n \"acc_stderr\": 0.021473491834808334,\n\
\ \"acc_norm\": 0.8179012345679012,\n \"acc_norm_stderr\": 0.021473491834808334\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5638297872340425,\n \"acc_stderr\": 0.029583452036284076,\n \
\ \"acc_norm\": 0.5638297872340425,\n \"acc_norm_stderr\": 0.029583452036284076\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5788787483702738,\n\
\ \"acc_stderr\": 0.012610325733489903,\n \"acc_norm\": 0.5788787483702738,\n\
\ \"acc_norm_stderr\": 0.012610325733489903\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7279411764705882,\n \"acc_stderr\": 0.027033041151681456,\n\
\ \"acc_norm\": 0.7279411764705882,\n \"acc_norm_stderr\": 0.027033041151681456\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7663398692810458,\n \"acc_stderr\": 0.017119158496044506,\n \
\ \"acc_norm\": 0.7663398692810458,\n \"acc_norm_stderr\": 0.017119158496044506\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7363636363636363,\n\
\ \"acc_stderr\": 0.04220224692971987,\n \"acc_norm\": 0.7363636363636363,\n\
\ \"acc_norm_stderr\": 0.04220224692971987\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7836734693877551,\n \"acc_stderr\": 0.026358916334904035,\n\
\ \"acc_norm\": 0.7836734693877551,\n \"acc_norm_stderr\": 0.026358916334904035\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8706467661691543,\n\
\ \"acc_stderr\": 0.02372983088101853,\n \"acc_norm\": 0.8706467661691543,\n\
\ \"acc_norm_stderr\": 0.02372983088101853\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8654970760233918,\n \"acc_stderr\": 0.026168221344662297,\n\
\ \"acc_norm\": 0.8654970760233918,\n \"acc_norm_stderr\": 0.026168221344662297\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.46878824969400246,\n\
\ \"mc1_stderr\": 0.017469364874577537,\n \"mc2\": 0.6440529032814905,\n\
\ \"mc2_stderr\": 0.014798378745240535\n }\n}\n```"
repo_url: https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|arc:challenge|25_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hellaswag|10_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-15-51.325850.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T18-15-51.325850.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T18-15-51.325850.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T18-15-51.325850.parquet'
- config_name: results
data_files:
- split: 2023_09_22T18_15_51.325850
path:
- results_2023-09-22T18-15-51.325850.parquet
- split: latest
path:
- results_2023-09-22T18-15-51.325850.parquet
---
# Dataset Card for Evaluation run of AIDC-ai-business/Marcoroni-70B-v1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [AIDC-ai-business/Marcoroni-70B-v1](https://huggingface.co/AIDC-ai-business/Marcoroni-70B-v1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T18:15:51.325850](https://huggingface.co/datasets/open-llm-leaderboard/details_AIDC-ai-business__Marcoroni-70B-v1/blob/main/results_2023-09-22T18-15-51.325850.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7060854021313192,
"acc_stderr": 0.030933452595170018,
"acc_norm": 0.7100920024195952,
"acc_norm_stderr": 0.030899690738616904,
"mc1": 0.46878824969400246,
"mc1_stderr": 0.017469364874577537,
"mc2": 0.6440529032814905,
"mc2_stderr": 0.014798378745240535
},
"harness|arc:challenge|25": {
"acc": 0.6877133105802048,
"acc_stderr": 0.013542598541688067,
"acc_norm": 0.735494880546075,
"acc_norm_stderr": 0.01288927294931337
},
"harness|hellaswag|10": {
"acc": 0.6876120294761999,
"acc_stderr": 0.004625198756710245,
"acc_norm": 0.8762198765186218,
"acc_norm_stderr": 0.0032865748124511866
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8157894736842105,
"acc_stderr": 0.031546980450822305,
"acc_norm": 0.8157894736842105,
"acc_norm_stderr": 0.031546980450822305
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7471698113207547,
"acc_stderr": 0.026749899771241214,
"acc_norm": 0.7471698113207547,
"acc_norm_stderr": 0.026749899771241214
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.03216600808802267,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.03216600808802267
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7052023121387283,
"acc_stderr": 0.03476599607516478,
"acc_norm": 0.7052023121387283,
"acc_norm_stderr": 0.03476599607516478
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3627450980392157,
"acc_stderr": 0.04784060704105654,
"acc_norm": 0.3627450980392157,
"acc_norm_stderr": 0.04784060704105654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.73,
"acc_stderr": 0.04461960433384739,
"acc_norm": 0.73,
"acc_norm_stderr": 0.04461960433384739
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7106382978723405,
"acc_stderr": 0.02964400657700962,
"acc_norm": 0.7106382978723405,
"acc_norm_stderr": 0.02964400657700962
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6275862068965518,
"acc_stderr": 0.04028731532947558,
"acc_norm": 0.6275862068965518,
"acc_norm_stderr": 0.04028731532947558
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.46825396825396826,
"acc_stderr": 0.0256993528321318,
"acc_norm": 0.46825396825396826,
"acc_norm_stderr": 0.0256993528321318
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.49206349206349204,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.49206349206349204,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8290322580645161,
"acc_stderr": 0.021417242936321586,
"acc_norm": 0.8290322580645161,
"acc_norm_stderr": 0.021417242936321586
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5566502463054187,
"acc_stderr": 0.03495334582162933,
"acc_norm": 0.5566502463054187,
"acc_norm_stderr": 0.03495334582162933
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403325,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403325
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8424242424242424,
"acc_stderr": 0.028450388805284343,
"acc_norm": 0.8424242424242424,
"acc_norm_stderr": 0.028450388805284343
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8888888888888888,
"acc_stderr": 0.02239078763821677,
"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.02239078763821677
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9378238341968912,
"acc_stderr": 0.017426974154240528,
"acc_norm": 0.9378238341968912,
"acc_norm_stderr": 0.017426974154240528
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7307692307692307,
"acc_stderr": 0.022489389793654817,
"acc_norm": 0.7307692307692307,
"acc_norm_stderr": 0.022489389793654817
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3148148148148148,
"acc_stderr": 0.02831753349606647,
"acc_norm": 0.3148148148148148,
"acc_norm_stderr": 0.02831753349606647
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7857142857142857,
"acc_stderr": 0.02665353159671549,
"acc_norm": 0.7857142857142857,
"acc_norm_stderr": 0.02665353159671549
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4900662251655629,
"acc_stderr": 0.04081677107248436,
"acc_norm": 0.4900662251655629,
"acc_norm_stderr": 0.04081677107248436
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8972477064220183,
"acc_stderr": 0.013018246509173768,
"acc_norm": 0.8972477064220183,
"acc_norm_stderr": 0.013018246509173768
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.033509916046960436,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.033509916046960436
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9215686274509803,
"acc_stderr": 0.018869514646658928,
"acc_norm": 0.9215686274509803,
"acc_norm_stderr": 0.018869514646658928
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8945147679324894,
"acc_stderr": 0.01999556072375854,
"acc_norm": 0.8945147679324894,
"acc_norm_stderr": 0.01999556072375854
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.8026905829596412,
"acc_stderr": 0.02670985334496796,
"acc_norm": 0.8026905829596412,
"acc_norm_stderr": 0.02670985334496796
},
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```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 65,057 | [
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biosses | 2022-11-03T16:31:20.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:semantic-similarity-scoring",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"license:gpl-3.0",
"region:us"
] | null | BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset containing articles from the biomedical domain. The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). | @article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\\u{g}}anc{\\i}o{\\u{g}}lu, Gizem and {\\"O}zt{\\"u}rk, Hakime and {\\"O}zg{\\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
} | 4 | 331 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
- semantic-similarity-scoring
paperswithcode_id: biosses
pretty_name: BIOSSES
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float32
splits:
- name: train
num_bytes: 32783
num_examples: 100
download_size: 36324
dataset_size: 32783
---
# Dataset Card for BIOSSES
## 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://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html
- **Repository:** https://github.com/gizemsogancioglu/biosses
- **Paper:** [BIOSSES: a semantic sentence similarity estimation system for the biomedical domain](https://academic.oup.com/bioinformatics/article/33/14/i49/3953954)
- **Point of Contact:** [Gizem Soğancıoğlu](gizemsogancioglu@gmail.com) and [Arzucan Özgür](gizemsogancioglu@gmail.com)
### Dataset Summary
BIOSSES is a benchmark dataset for biomedical sentence similarity estimation. The dataset comprises 100 sentence pairs, in which each sentence was selected from the [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/) containing articles from the biomedical domain. The sentence pairs in BIOSSES were selected from citing sentences, i.e. sentences that have a citation to a reference article.
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). In the original paper the mean of the scores assigned by the five human annotators was taken as the gold standard. The Pearson correlation between the gold standard scores and the scores estimated by the models was used as the evaluation metric. The strength of correlation can be assessed by the general guideline proposed by Evans (1996) as follows:
- very strong: 0.80–1.00
- strong: 0.60–0.79
- moderate: 0.40–0.59
- weak: 0.20–0.39
- very weak: 0.00–0.19
### Supported Tasks and Leaderboards
Biomedical Semantic Similarity Scoring.
### Languages
English.
## Dataset Structure
### Data Instances
For each instance, there are two sentences (i.e. sentence 1 and 2), and its corresponding similarity score (the mean of the scores assigned by the five human annotators).
```
{'sentence 1': 'Here, looking for agents that could specifically kill KRAS mutant cells, they found that knockdown of GATA2 was synthetically lethal with KRAS mutation'
'sentence 2': 'Not surprisingly, GATA2 knockdown in KRAS mutant cells resulted in a striking reduction of active GTP-bound RHO proteins, including the downstream ROCK kinase'
'score': 2.2}
```
### Data Fields
- `sentence 1`: string
- `sentence 2`: string
- `score`: float ranging from 0 (no relation) to 4 (equivalent)
### Data Splits
No data splits provided.
## Dataset Creation
### Curation Rationale
### Source Data
The [TAC (Text Analysis Conference) Biomedical Summarization Track Training Dataset](https://tac.nist.gov/2014/BiomedSumm/).
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores ranging from 0 (no relation) to 4 (equivalent). The score range was described based on the guidelines of SemEval 2012 Task 6 on STS (Agirre et al., 2012). Besides the annotation instructions, example sentences from the biomedical literature were provided to the annotators for each of the similarity degrees.
The table below shows the Pearson correlation of the scores of each annotator with respect to the average scores of the remaining four annotators. It is observed that there is strong association among the scores of the annotators. The lowest correlations are 0.902, which can be considered as an upper bound for an algorithmic measure evaluated on this dataset.
| |Correlation r |
|----------:|--------------:|
|Annotator A| 0.952|
|Annotator B| 0.958|
|Annotator C| 0.917|
|Annotator D| 0.902|
|Annotator E| 0.941|
#### 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
- Gizem Soğancıoğlu, gizemsogancioglu@gmail.com
- Hakime Öztürk, hakime.ozturk@boun.edu.tr
- Arzucan Özgür, gizemsogancioglu@gmail.com
Bogazici University, Istanbul, Turkey
### Licensing Information
BIOSSES is made available under the terms of [The GNU Common Public License v.3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).
### Citation Information
@article{souganciouglu2017biosses,
title={BIOSSES: a semantic sentence similarity estimation system for the biomedical domain},
author={So{\u{g}}anc{\i}o{\u{g}}lu, Gizem and {\"O}zt{\"u}rk, Hakime and {\"O}zg{\"u}r, Arzucan},
journal={Bioinformatics},
volume={33},
number={14},
pages={i49--i58},
year={2017},
publisher={Oxford University Press}
}
### Contributions
Thanks to [@bwang482](https://github.com/bwang482) for adding this dataset. | 6,641 | [
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] |
totto | 2023-02-23T09:49:19.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"arxiv:2004.14373",
"region:us"
] | null | ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description. | @inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
} | 5 | 331 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: totto
pretty_name: ToTTo
dataset_info:
features:
- name: id
dtype: int32
- name: table_page_title
dtype: string
- name: table_webpage_url
dtype: string
- name: table_section_title
dtype: string
- name: table_section_text
dtype: string
- name: table
list:
list:
- name: column_span
dtype: int32
- name: is_header
dtype: bool
- name: row_span
dtype: int32
- name: value
dtype: string
- name: highlighted_cells
sequence:
sequence: int32
- name: example_id
dtype: string
- name: sentence_annotations
sequence:
- name: original_sentence
dtype: string
- name: sentence_after_deletion
dtype: string
- name: sentence_after_ambiguity
dtype: string
- name: final_sentence
dtype: string
- name: overlap_subset
dtype: string
splits:
- name: train
num_bytes: 652754806
num_examples: 120761
- name: validation
num_bytes: 47277039
num_examples: 7700
- name: test
num_bytes: 40883586
num_examples: 7700
download_size: 187724372
dataset_size: 740915431
---
# Dataset Card for ToTTo
## 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:** None
- **Repository:** https://github.com/google-research-datasets/ToTTo
- **Paper:** https://arxiv.org/abs/2004.14373
- **Leaderboard:** https://github.com/google-research-datasets/ToTTo#leaderboard
- **Point of Contact:** [totto@google.com](mailto:totto@google.com)
### Dataset Summary
ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled
generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A sample training set is provided below
```
{'example_id': '1762238357686640028',
'highlighted_cells': [[13, 2]],
'id': 0,
'overlap_subset': 'none',
'sentence_annotations': {'final_sentence': ['A Favorita is the telenovela aired in the 9 pm timeslot.'],
'original_sentence': ['It is also the first telenovela by the writer to air in the 9 pm timeslot.'],
'sentence_after_ambiguity': ['A Favorita is the telenovela aired in the 9 pm timeslot.'],
'sentence_after_deletion': ['It is the telenovela air in the 9 pm timeslot.']},
'table': [[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '#'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Run'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Title'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Chapters'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Author'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Director'},
{'column_span': 1,
'is_header': True,
'row_span': 1,
'value': 'Ibope Rating'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '59'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 5, 2000— February 2, 2001'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Laços de Família'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Ricardo Waddington'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '44.9'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '60'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'February 5, 2001— September 28, 2001'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Porto dos Milagres'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Aguinaldo Silva Ricardo Linhares'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Marcos Paulo Simões'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '44.6'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '61'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'October 1, 2001— June 14, 2002'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'O Clone'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Glória Perez'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '47.0'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '62'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 17, 2002— February 14, 2003'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Esperança'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Benedito Ruy Barbosa'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Luiz Fernando'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '37.7'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '63'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'February 17, 2003— October 10, 2003'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Mulheres Apaixonadas'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Ricardo Waddington'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.6'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '64'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'October 13, 2003— June 25, 2004'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Celebridade'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Gilberto Braga'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Dennis Carvalho'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.0'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '65'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 28, 2004— March 11, 2005'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Senhora do Destino'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Aguinaldo Silva'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Wolf Maya'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '50.4'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '66'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'March 14, 2005— November 4, 2005'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'América'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Glória Perez'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim Marcos Schechtman'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '49.4'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '67'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'November 7, 2005— July 7, 2006'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Belíssima'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Sílvio de Abreu'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Denise Saraceni'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '48.5'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '68'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'July 10, 2006— March 2, 2007'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Páginas da Vida'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.8'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '69'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'March 5, 2007— September 28, 2007'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Paraíso Tropical'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '179'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Gilberto Braga Ricardo Linhares'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Dennis Carvalho'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '42.8'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '70'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'October 1, 2007— May 31, 2008'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Duas Caras'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '210'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Aguinaldo Silva'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Wolf Maya'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '41.1'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '71'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 2, 2008— January 16, 2009'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'A Favorita'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '197'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'João Emanuel Carneiro'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Ricardo Waddington'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '39.5'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '72'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'January 19, 2009— September 11, 2009'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Caminho das Índias'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Glória Perez'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Marcos Schechtman'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '38.8'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '73'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'September 14, 2009— May 14, 2010'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Viver a Vida'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '35.6'}]],
'table_page_title': 'List of 8/9 PM telenovelas of Rede Globo',
'table_section_text': '',
'table_section_title': '2000s',
'table_webpage_url': 'http://en.wikipedia.org/wiki/List_of_8/9_PM_telenovelas_of_Rede_Globo'}
```
Please note that in test set sentence annotations are not available and thus values inside `sentence_annotations` can be safely ignored.
### Data Fields
- `table_webpage_url` (`str`): Table webpage URL.
- `table_page_title` (`str`): Table metadata with context about the table.
- `table_section_title` (`str`): Table metadata with context about the table.
- `table_section_text` (`str`): Table metadata with context about the table.
- `table` (`List[List[Dict]]`): The outer lists represents rows and the inner lists columns. Each Dict has the fields:
- `column_span` (`int`)
- `is_header` (`bool`)
- `row_span` (`int`)
- `value` (`str`)
- `highlighted_cells` (`List[[row_index, column_index]]`): Where each `[row_index, column_index]` pair indicates that `table[row_index][column_index]` is highlighted.
- `example_id` (`int`): A unique id for this example.
- `sentence_annotations`: Consists of the `original_sentence` and the sequence of revised sentences performed in order to produce the `final_sentence`.
### Data Splits
```
DatasetDict({
train: Dataset({
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
num_rows: 120761
})
validation: Dataset({
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
num_rows: 7700
})
test: Dataset({
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
num_rows: 7700
})
})
```
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### 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
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. | 17,009 | [
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HuggingFaceH4/instruction-dataset | 2023-02-28T22:30:11.000Z | [
"license:apache-2.0",
"region:us"
] | HuggingFaceH4 | null | null | 16 | 331 | 2023-02-28T21:26:43 | ---
license: apache-2.0
---
This is the blind eval dataset of high-quality, diverse, human-written instructions with demonstrations. We will be using this for step 3 evaluations in our RLHF pipeline. | 199 | [
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crystina-z/inlang-mrtydi-corpus | 2022-01-17T15:24:18.000Z | [
"region:us"
] | crystina-z | null | null | 0 | 330 | 2022-03-02T23:29:22 | Entry not found | 15 | [
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allenai/soda | 2023-01-04T09:24:32.000Z | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"source_datasets:extended|Atomic10x",
"language:en",
"license:cc-by-4.0",
"dialogue",
"narrative",
"commonsense",
"arxiv:2212.10465",
"region:us"
] | allenai | null | null | 99 | 330 | 2023-01-04T08:51:53 | ---
language:
- en
language_creators:
- machine-generated
annotation_creators:
- machine-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SODA
size_categories:
- 1M<n<10M
splits:
- name: train
num_examples: 1191582
- name: valid
num_examples: 146346
- name: test
num_examples: 148968
dataset_size: 1486896
source_datasets:
- original
- extended|Atomic10x
tags:
- dialogue
- narrative
- commonsense
task_categories:
- conversational
task_ids:
- dialogue-generation
---
# Dataset Card for 🥤SODA
## Dataset Description
- **Repository:** [Code](https://github.com/skywalker023/sodaverse)
- **Paper:** [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://arxiv.org/abs/2212.10465)
- **Point of Contact:** [Hyunwoo Kim](mailto:hyunwook@allenai.org)
## Dataset Summary
🥤SODA is the first publicly available, million-scale, high-quality dialogue dataset covering a wide range of social interactions. Dialogues are distilled from a PLM (InstructGPT; Ouyang et al., 2022) by contextualizing social commonsense knowledge from a knowledge graph (Atomic10x; West et al., 2022). Human evaluation shows that dialogues in SODA are more consistent, specific, and (surprisingly) natural than prior human-authored datasets – e.g., DailyDialog (Li et al., 2017), BlendedSkillTalk (Smith et al., 2020). Also, since social commonsense knowledge encompasses emotional reactions (i.e., the xReact `relation`), SODA includes 385K conversations labeled with 1.7K unique emotions along with information about the experiencer and the cause – i.e., `PersonX` and the `head` event in the symbolic commonsense knowledge triple.
## Languages
English
## Dataset Structure
field | type | description
--- | --- | ---
`head` | str | the head event in the symbolic commonsense knowledge triple
`relation` | str | the relationship between `head` and `tail` events
`tail` | str | the tail event in the symbolic commonsense knowledge triple
`literal` | str | the symbolic commonsense knowledge in sentence-form
`narrative` | str | narrative based on the `literal`
`dialogue` | list of str | dialogue grounded in the `narrative`
`speakers` | list of str | the speakers for each turn in the `dialogue`
`PersonX` | str | the assigned name for PersonX in the commonsense knowledge triple
`PersonY` | str\|null | the assigned name for PersonY in the commonsense knowledge triple
`PersonZ` | str\|null | the assigned name for PersonZ in the commonsense knowledge triple
`original_index` | int | the original index from Atomic10x
`split` | str | the split information: {train, valid, test}
`head_answer` | str | the answer for whether the `head` is included in the `narrative`: {Yes, Unknown}
`pmi_head_answer` | str | the answer for whether the `head` is included in the `narrative` with point-wise mutual information applied: {Yes, No, Unknown}
`relation_tail_answer` | str | the answer for whether the `relation`-`tail` is included in the `dialogue`: {Yes, No, Unknown}
`pmi_relation_tail_answer` | str | the answer for whether the `relation`-`tail` is included in the `dialogue` with point-wise mutual information applied: {Yes, No, Unknown}
## Dataset Creation
To create 🥤SODA, we distill dialogues from InstructGPT by contextualizing social commonsense knowledge – i.e., adding context information in multiple steps: (1) Retrieve social commonsense from the symbolic commonsense knowledge graph, (2) convert it into sentence form, (3) generate a narrative from the sentence, (4) infer the speakers from the narrative, and finally (5) derive contentful conversation grounded in the narrative and speakers. Anchoring the PLM in commonsense knowledge for deriving conversations offers two key advantages: (1) minimizing nonsensical conversations and (2) maximizing diversity. For more details, please refer to our [paper](https://arxiv.org/abs/2212.10465).
### Further Details, Social Impacts, and Limitations
Please refer to our [paper](https://arxiv.org/abs/2212.10465).
## Trained Model
Using 🥤SODA, we train 🧑🏻🚀COSMO: a generalizable conversation agent outperforming previous best-performing agents on both in- and out-of-domain datasets. COSMO-3B is available [here](https://huggingface.co/allenai/cosmo-xl)!
## Additional Information
For a brief summary of our paper, please see this [tweet](https://twitter.com/hyunw__kim/status/1605400305126248448).
### Citation
Please cite our work if you find the resources in this repository useful:
```
@article{kim2022soda,
title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
journal={ArXiv},
year={2022},
volume={abs/2212.10465}
}
``` | 4,886 | [
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ai4bharat/IndicSentiment | 2023-05-26T11:07:29.000Z | [
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open-llm-leaderboard/details_uni-tianyan__Uni-TianYan | 2023-09-18T02:40:22.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 330 | 2023-09-03T12:28:00 | ---
pretty_name: Evaluation run of uni-tianyan/Uni-TianYan
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uni-tianyan/Uni-TianYan](https://huggingface.co/uni-tianyan/Uni-TianYan) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_uni-tianyan__Uni-TianYan\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-18T02:40:09.826211](https://huggingface.co/datasets/open-llm-leaderboard/details_uni-tianyan__Uni-TianYan/blob/main/results_2023-09-18T02-40-09.826211.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.24486157718120805,\n\
\ \"em_stderr\": 0.004403654691385411,\n \"f1\": 0.39787751677852523,\n\
\ \"f1_stderr\": 0.004155160727794137,\n \"acc\": 0.5222921265482389,\n\
\ \"acc_stderr\": 0.01107896164608613\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.24486157718120805,\n \"em_stderr\": 0.004403654691385411,\n\
\ \"f1\": 0.39787751677852523,\n \"f1_stderr\": 0.004155160727794137\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.221379833206975,\n \
\ \"acc_stderr\": 0.011436000004253518\n },\n \"harness|winogrande|5\":\
\ {\n \"acc\": 0.8232044198895028,\n \"acc_stderr\": 0.010721923287918744\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uni-tianyan/Uni-TianYan
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|arc:challenge|25_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_18T02_40_09.826211
path:
- '**/details_harness|drop|3_2023-09-18T02-40-09.826211.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-18T02-40-09.826211.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_18T02_40_09.826211
path:
- '**/details_harness|gsm8k|5_2023-09-18T02-40-09.826211.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-18T02-40-09.826211.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hellaswag|10_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
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- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T12:27:36.436118.parquet'
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- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T12:27:36.436118.parquet'
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- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T12:27:36.436118.parquet'
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path:
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- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T12:27:36.436118.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T12:27:36.436118.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T12:27:36.436118.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_18T02_40_09.826211
path:
- '**/details_harness|winogrande|5_2023-09-18T02-40-09.826211.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-18T02-40-09.826211.parquet'
- config_name: results
data_files:
- split: 2023_09_03T12_27_36.436118
path:
- results_2023-09-03T12:27:36.436118.parquet
- split: 2023_09_18T02_40_09.826211
path:
- results_2023-09-18T02-40-09.826211.parquet
- split: latest
path:
- results_2023-09-18T02-40-09.826211.parquet
---
# Dataset Card for Evaluation run of uni-tianyan/Uni-TianYan
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/uni-tianyan/Uni-TianYan
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [uni-tianyan/Uni-TianYan](https://huggingface.co/uni-tianyan/Uni-TianYan) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_uni-tianyan__Uni-TianYan",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-18T02:40:09.826211](https://huggingface.co/datasets/open-llm-leaderboard/details_uni-tianyan__Uni-TianYan/blob/main/results_2023-09-18T02-40-09.826211.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.24486157718120805,
"em_stderr": 0.004403654691385411,
"f1": 0.39787751677852523,
"f1_stderr": 0.004155160727794137,
"acc": 0.5222921265482389,
"acc_stderr": 0.01107896164608613
},
"harness|drop|3": {
"em": 0.24486157718120805,
"em_stderr": 0.004403654691385411,
"f1": 0.39787751677852523,
"f1_stderr": 0.004155160727794137
},
"harness|gsm8k|5": {
"acc": 0.221379833206975,
"acc_stderr": 0.011436000004253518
},
"harness|winogrande|5": {
"acc": 0.8232044198895028,
"acc_stderr": 0.010721923287918744
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 38,583 | [
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result-kand2-sdxl-wuerst-karlo/023acaec | 2023-10-03T22:23:40.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 330 | 2023-10-03T22:23:39 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 233
num_examples: 10
download_size: 1392
dataset_size: 233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "023acaec"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
vincentmin/eli5_rlhf_explainlikeim5 | 2023-04-10T10:52:49.000Z | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] | vincentmin | null | null | 5 | 329 | 2023-04-07T19:22:14 | ---
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: Reddit Explain Like I'm 5 for Reinforcement Learning Human Feedback
size_categories:
- 100K<n<1M
---
# ELI5 paired
This is a processed version of the [`eli5`](https://huggingface.co/datasets/eli5) dataset.
Compared to ["eli5_rlhf"](https://huggingface.co/datasets/vincentmin/eli5_rlhf), this dataset contains only QA pairs from the train split of the eli5 dataset and only from the subreddit explainlikeimfive.
Furthermore, the function
```
def get_question(example):
title = example["title"]
selftext = example["selftext"]
if selftext:
if selftext[-1] not in [".", "?", "!"]:
seperator = ". "
else:
seperator = " "
question = title + seperator + selftext
else:
question = title
example["question"] = question
return example
```
was applied to get the "question" column and the "title" and "selftext" columns were removed.
The dataset was created following very closely the steps in the [`stack-exchange-paired`](https://huggingface.co/datasets/lvwerra/stack-exchange-paired) dataset.
The following steps were applied:
- The "question" field is a concatenation of "title" with "selftext".
- Create pairs `(response_j, response_k)` where j was rated better than k
- Sample at most 10 pairs per question
- Shuffle the dataset globally
This dataset is designed to be used for preference learning. The processing notebook is in the repository as well. | 1,519 | [
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open-llm-leaderboard/details_adonlee__LLaMA_2_70B_LoRA | 2023-09-22T21:37:15.000Z | [
"region:us"
] | open-llm-leaderboard | null | null | 0 | 329 | 2023-09-22T21:36:15 | ---
pretty_name: Evaluation run of adonlee/LLaMA_2_70B_LoRA
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [adonlee/LLaMA_2_70B_LoRA](https://huggingface.co/adonlee/LLaMA_2_70B_LoRA) on\
\ the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_adonlee__LLaMA_2_70B_LoRA\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-22T21:35:51.410251](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_70B_LoRA/blob/main/results_2023-09-22T21-35-51.410251.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"acc\": 0.7077096775676626,\n\
\ \"acc_stderr\": 0.030867670314758275,\n \"acc_norm\": 0.7114995822621553,\n\
\ \"acc_norm_stderr\": 0.030836833292351554,\n \"mc1\": 0.4663402692778458,\n\
\ \"mc1_stderr\": 0.017463793867168106,\n \"mc2\": 0.6451679386365279,\n\
\ \"mc2_stderr\": 0.014753028795637621\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6902730375426621,\n \"acc_stderr\": 0.013512058415238361,\n\
\ \"acc_norm\": 0.726962457337884,\n \"acc_norm_stderr\": 0.013019332762635743\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6886078470424218,\n\
\ \"acc_stderr\": 0.004621163476949205,\n \"acc_norm\": 0.8755228042222665,\n\
\ \"acc_norm_stderr\": 0.003294504807555228\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.041539484047424,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.041539484047424\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8223684210526315,\n \"acc_stderr\": 0.03110318238312338,\n\
\ \"acc_norm\": 0.8223684210526315,\n \"acc_norm_stderr\": 0.03110318238312338\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.76,\n\
\ \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\": 0.76,\n \
\ \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7358490566037735,\n \"acc_stderr\": 0.02713429162874171,\n\
\ \"acc_norm\": 0.7358490566037735,\n \"acc_norm_stderr\": 0.02713429162874171\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8263888888888888,\n\
\ \"acc_stderr\": 0.03167473383795718,\n \"acc_norm\": 0.8263888888888888,\n\
\ \"acc_norm_stderr\": 0.03167473383795718\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n\
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6936416184971098,\n\
\ \"acc_stderr\": 0.03514942551267439,\n \"acc_norm\": 0.6936416184971098,\n\
\ \"acc_norm_stderr\": 0.03514942551267439\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.37254901960784315,\n \"acc_stderr\": 0.048108401480826346,\n\
\ \"acc_norm\": 0.37254901960784315,\n \"acc_norm_stderr\": 0.048108401480826346\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932263,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932263\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7106382978723405,\n \"acc_stderr\": 0.02964400657700962,\n\
\ \"acc_norm\": 0.7106382978723405,\n \"acc_norm_stderr\": 0.02964400657700962\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.04685473041907789,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.04685473041907789\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6206896551724138,\n \"acc_stderr\": 0.04043461861916746,\n\
\ \"acc_norm\": 0.6206896551724138,\n \"acc_norm_stderr\": 0.04043461861916746\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.47619047619047616,\n \"acc_stderr\": 0.02572209706438853,\n \"\
acc_norm\": 0.47619047619047616,\n \"acc_norm_stderr\": 0.02572209706438853\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5079365079365079,\n\
\ \"acc_stderr\": 0.044715725362943486,\n \"acc_norm\": 0.5079365079365079,\n\
\ \"acc_norm_stderr\": 0.044715725362943486\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8096774193548387,\n \"acc_stderr\": 0.022331707611823078,\n \"\
acc_norm\": 0.8096774193548387,\n \"acc_norm_stderr\": 0.022331707611823078\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5714285714285714,\n \"acc_stderr\": 0.034819048444388045,\n \"\
acc_norm\": 0.5714285714285714,\n \"acc_norm_stderr\": 0.034819048444388045\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.78,\n \"acc_stderr\": 0.04163331998932262,\n \"acc_norm\"\
: 0.78,\n \"acc_norm_stderr\": 0.04163331998932262\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8545454545454545,\n \"acc_stderr\": 0.027530196355066584,\n\
\ \"acc_norm\": 0.8545454545454545,\n \"acc_norm_stderr\": 0.027530196355066584\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.898989898989899,\n \"acc_stderr\": 0.021469735576055343,\n \"\
acc_norm\": 0.898989898989899,\n \"acc_norm_stderr\": 0.021469735576055343\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9326424870466321,\n \"acc_stderr\": 0.0180883938390789,\n\
\ \"acc_norm\": 0.9326424870466321,\n \"acc_norm_stderr\": 0.0180883938390789\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7102564102564103,\n \"acc_stderr\": 0.023000628243687968,\n\
\ \"acc_norm\": 0.7102564102564103,\n \"acc_norm_stderr\": 0.023000628243687968\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.028820884666253252,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.028820884666253252\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7815126050420168,\n \"acc_stderr\": 0.02684151432295893,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.02684151432295893\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4900662251655629,\n \"acc_stderr\": 0.04081677107248436,\n \"\
acc_norm\": 0.4900662251655629,\n \"acc_norm_stderr\": 0.04081677107248436\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9009174311926605,\n \"acc_stderr\": 0.01280978008187893,\n \"\
acc_norm\": 0.9009174311926605,\n \"acc_norm_stderr\": 0.01280978008187893\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9019607843137255,\n \"acc_stderr\": 0.0208711184555521,\n \"acc_norm\"\
: 0.9019607843137255,\n \"acc_norm_stderr\": 0.0208711184555521\n },\n\
\ \"harness|hendrycksTest-high_school_world_history|5\": {\n \"acc\":\
\ 0.8818565400843882,\n \"acc_stderr\": 0.02101105265987847,\n \"\
acc_norm\": 0.8818565400843882,\n \"acc_norm_stderr\": 0.02101105265987847\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7847533632286996,\n\
\ \"acc_stderr\": 0.027584066602208274,\n \"acc_norm\": 0.7847533632286996,\n\
\ \"acc_norm_stderr\": 0.027584066602208274\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8473282442748091,\n \"acc_stderr\": 0.031545216720054725,\n\
\ \"acc_norm\": 0.8473282442748091,\n \"acc_norm_stderr\": 0.031545216720054725\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8760330578512396,\n \"acc_stderr\": 0.030083098716035202,\n \"\
acc_norm\": 0.8760330578512396,\n \"acc_norm_stderr\": 0.030083098716035202\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\
\ \"acc_stderr\": 0.035207039905179635,\n \"acc_norm\": 0.8425925925925926,\n\
\ \"acc_norm_stderr\": 0.035207039905179635\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8466257668711656,\n \"acc_stderr\": 0.0283116014414386,\n\
\ \"acc_norm\": 0.8466257668711656,\n \"acc_norm_stderr\": 0.0283116014414386\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5714285714285714,\n\
\ \"acc_stderr\": 0.04697113923010213,\n \"acc_norm\": 0.5714285714285714,\n\
\ \"acc_norm_stderr\": 0.04697113923010213\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9145299145299145,\n\
\ \"acc_stderr\": 0.01831589168562585,\n \"acc_norm\": 0.9145299145299145,\n\
\ \"acc_norm_stderr\": 0.01831589168562585\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8697318007662835,\n\
\ \"acc_stderr\": 0.012036729568216054,\n \"acc_norm\": 0.8697318007662835,\n\
\ \"acc_norm_stderr\": 0.012036729568216054\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7687861271676301,\n \"acc_stderr\": 0.022698657167855713,\n\
\ \"acc_norm\": 0.7687861271676301,\n \"acc_norm_stderr\": 0.022698657167855713\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.646927374301676,\n\
\ \"acc_stderr\": 0.01598420454526858,\n \"acc_norm\": 0.646927374301676,\n\
\ \"acc_norm_stderr\": 0.01598420454526858\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.024739981355113592,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.024739981355113592\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7684887459807074,\n\
\ \"acc_stderr\": 0.023956532766639133,\n \"acc_norm\": 0.7684887459807074,\n\
\ \"acc_norm_stderr\": 0.023956532766639133\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8271604938271605,\n \"acc_stderr\": 0.02103851777015737,\n\
\ \"acc_norm\": 0.8271604938271605,\n \"acc_norm_stderr\": 0.02103851777015737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.599290780141844,\n \"acc_stderr\": 0.029233465745573096,\n \
\ \"acc_norm\": 0.599290780141844,\n \"acc_norm_stderr\": 0.029233465745573096\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5814863102998696,\n\
\ \"acc_stderr\": 0.012599505608336482,\n \"acc_norm\": 0.5814863102998696,\n\
\ \"acc_norm_stderr\": 0.012599505608336482\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7316176470588235,\n \"acc_stderr\": 0.026917481224377204,\n\
\ \"acc_norm\": 0.7316176470588235,\n \"acc_norm_stderr\": 0.026917481224377204\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7679738562091504,\n \"acc_stderr\": 0.017077373377856933,\n \
\ \"acc_norm\": 0.7679738562091504,\n \"acc_norm_stderr\": 0.017077373377856933\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7454545454545455,\n\
\ \"acc_stderr\": 0.041723430387053825,\n \"acc_norm\": 0.7454545454545455,\n\
\ \"acc_norm_stderr\": 0.041723430387053825\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8081632653061225,\n \"acc_stderr\": 0.025206963154225395,\n\
\ \"acc_norm\": 0.8081632653061225,\n \"acc_norm_stderr\": 0.025206963154225395\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8756218905472637,\n\
\ \"acc_stderr\": 0.023335401790166323,\n \"acc_norm\": 0.8756218905472637,\n\
\ \"acc_norm_stderr\": 0.023335401790166323\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.86,\n \"acc_stderr\": 0.03487350880197769,\n \
\ \"acc_norm\": 0.86,\n \"acc_norm_stderr\": 0.03487350880197769\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8713450292397661,\n \"acc_stderr\": 0.02567934272327692,\n\
\ \"acc_norm\": 0.8713450292397661,\n \"acc_norm_stderr\": 0.02567934272327692\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4663402692778458,\n\
\ \"mc1_stderr\": 0.017463793867168106,\n \"mc2\": 0.6451679386365279,\n\
\ \"mc2_stderr\": 0.014753028795637621\n }\n}\n```"
repo_url: https://huggingface.co/adonlee/LLaMA_2_70B_LoRA
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|arc:challenge|25_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hellaswag|10_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T21-35-51.410251.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-22T21-35-51.410251.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T21-35-51.410251.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-22T21-35-51.410251.parquet'
- config_name: results
data_files:
- split: 2023_09_22T21_35_51.410251
path:
- results_2023-09-22T21-35-51.410251.parquet
- split: latest
path:
- results_2023-09-22T21-35-51.410251.parquet
---
# Dataset Card for Evaluation run of adonlee/LLaMA_2_70B_LoRA
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/adonlee/LLaMA_2_70B_LoRA
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [adonlee/LLaMA_2_70B_LoRA](https://huggingface.co/adonlee/LLaMA_2_70B_LoRA) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_adonlee__LLaMA_2_70B_LoRA",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-22T21:35:51.410251](https://huggingface.co/datasets/open-llm-leaderboard/details_adonlee__LLaMA_2_70B_LoRA/blob/main/results_2023-09-22T21-35-51.410251.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"acc": 0.7077096775676626,
"acc_stderr": 0.030867670314758275,
"acc_norm": 0.7114995822621553,
"acc_norm_stderr": 0.030836833292351554,
"mc1": 0.4663402692778458,
"mc1_stderr": 0.017463793867168106,
"mc2": 0.6451679386365279,
"mc2_stderr": 0.014753028795637621
},
"harness|arc:challenge|25": {
"acc": 0.6902730375426621,
"acc_stderr": 0.013512058415238361,
"acc_norm": 0.726962457337884,
"acc_norm_stderr": 0.013019332762635743
},
"harness|hellaswag|10": {
"acc": 0.6886078470424218,
"acc_stderr": 0.004621163476949205,
"acc_norm": 0.8755228042222665,
"acc_norm_stderr": 0.003294504807555228
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.041539484047424,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.041539484047424
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8223684210526315,
"acc_stderr": 0.03110318238312338,
"acc_norm": 0.8223684210526315,
"acc_norm_stderr": 0.03110318238312338
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7358490566037735,
"acc_stderr": 0.02713429162874171,
"acc_norm": 0.7358490566037735,
"acc_norm_stderr": 0.02713429162874171
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8263888888888888,
"acc_stderr": 0.03167473383795718,
"acc_norm": 0.8263888888888888,
"acc_norm_stderr": 0.03167473383795718
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.41,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.41,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6936416184971098,
"acc_stderr": 0.03514942551267439,
"acc_norm": 0.6936416184971098,
"acc_norm_stderr": 0.03514942551267439
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.37254901960784315,
"acc_stderr": 0.048108401480826346,
"acc_norm": 0.37254901960784315,
"acc_norm_stderr": 0.048108401480826346
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932263,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932263
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7106382978723405,
"acc_stderr": 0.02964400657700962,
"acc_norm": 0.7106382978723405,
"acc_norm_stderr": 0.02964400657700962
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.04685473041907789,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.04685473041907789
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6206896551724138,
"acc_stderr": 0.04043461861916746,
"acc_norm": 0.6206896551724138,
"acc_norm_stderr": 0.04043461861916746
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.02572209706438853,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.02572209706438853
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5079365079365079,
"acc_stderr": 0.044715725362943486,
"acc_norm": 0.5079365079365079,
"acc_norm_stderr": 0.044715725362943486
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8096774193548387,
"acc_stderr": 0.022331707611823078,
"acc_norm": 0.8096774193548387,
"acc_norm_stderr": 0.022331707611823078
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5714285714285714,
"acc_stderr": 0.034819048444388045,
"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.034819048444388045
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932262,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932262
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8545454545454545,
"acc_stderr": 0.027530196355066584,
"acc_norm": 0.8545454545454545,
"acc_norm_stderr": 0.027530196355066584
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.898989898989899,
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"acc_norm": 0.898989898989899,
"acc_norm_stderr": 0.021469735576055343
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9326424870466321,
"acc_stderr": 0.0180883938390789,
"acc_norm": 0.9326424870466321,
"acc_norm_stderr": 0.0180883938390789
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7102564102564103,
"acc_stderr": 0.023000628243687968,
"acc_norm": 0.7102564102564103,
"acc_norm_stderr": 0.023000628243687968
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.337037037037037,
"acc_stderr": 0.028820884666253252,
"acc_norm": 0.337037037037037,
"acc_norm_stderr": 0.028820884666253252
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7815126050420168,
"acc_stderr": 0.02684151432295893,
"acc_norm": 0.7815126050420168,
"acc_norm_stderr": 0.02684151432295893
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4900662251655629,
"acc_stderr": 0.04081677107248436,
"acc_norm": 0.4900662251655629,
"acc_norm_stderr": 0.04081677107248436
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9009174311926605,
"acc_stderr": 0.01280978008187893,
"acc_norm": 0.9009174311926605,
"acc_norm_stderr": 0.01280978008187893
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.033622774366080424,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.033622774366080424
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9019607843137255,
"acc_stderr": 0.0208711184555521,
"acc_norm": 0.9019607843137255,
"acc_norm_stderr": 0.0208711184555521
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8818565400843882,
"acc_stderr": 0.02101105265987847,
"acc_norm": 0.8818565400843882,
"acc_norm_stderr": 0.02101105265987847
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7847533632286996,
"acc_stderr": 0.027584066602208274,
"acc_norm": 0.7847533632286996,
"acc_norm_stderr": 0.027584066602208274
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8473282442748091,
"acc_stderr": 0.031545216720054725,
"acc_norm": 0.8473282442748091,
"acc_norm_stderr": 0.031545216720054725
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8760330578512396,
"acc_stderr": 0.030083098716035202,
"acc_norm": 0.8760330578512396,
"acc_norm_stderr": 0.030083098716035202
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8425925925925926,
"acc_stderr": 0.035207039905179635,
"acc_norm": 0.8425925925925926,
"acc_norm_stderr": 0.035207039905179635
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.8466257668711656,
"acc_stderr": 0.0283116014414386,
"acc_norm": 0.8466257668711656,
"acc_norm_stderr": 0.0283116014414386
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5714285714285714,
"acc_stderr": 0.04697113923010213,
"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.04697113923010213
},
"harness|hendrycksTest-management|5": {
"acc": 0.8252427184466019,
"acc_stderr": 0.03760178006026621,
"acc_norm": 0.8252427184466019,
"acc_norm_stderr": 0.03760178006026621
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9145299145299145,
"acc_stderr": 0.01831589168562585,
"acc_norm": 0.9145299145299145,
"acc_norm_stderr": 0.01831589168562585
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8697318007662835,
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"acc_norm": 0.8697318007662835,
"acc_norm_stderr": 0.012036729568216054
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7687861271676301,
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"acc_norm": 0.7687861271676301,
"acc_norm_stderr": 0.022698657167855713
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.646927374301676,
"acc_stderr": 0.01598420454526858,
"acc_norm": 0.646927374301676,
"acc_norm_stderr": 0.01598420454526858
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7516339869281046,
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"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.024739981355113592
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7684887459807074,
"acc_stderr": 0.023956532766639133,
"acc_norm": 0.7684887459807074,
"acc_norm_stderr": 0.023956532766639133
},
"harness|hendrycksTest-prehistory|5": {
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"acc_norm": 0.8271604938271605,
"acc_norm_stderr": 0.02103851777015737
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.599290780141844,
"acc_stderr": 0.029233465745573096,
"acc_norm": 0.599290780141844,
"acc_norm_stderr": 0.029233465745573096
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5814863102998696,
"acc_stderr": 0.012599505608336482,
"acc_norm": 0.5814863102998696,
"acc_norm_stderr": 0.012599505608336482
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7316176470588235,
"acc_stderr": 0.026917481224377204,
"acc_norm": 0.7316176470588235,
"acc_norm_stderr": 0.026917481224377204
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.7679738562091504,
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.6451679386365279,
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}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | 64,899 | [
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ostapeno/qa-platy_icl5_clen128_maxD-1_maxC10000_0.jsonl_length_matched | 2023-10-15T15:48:56.000Z | [
"region:us"
] | ostapeno | null | null | 0 | 329 | 2023-10-15T15:47:44 | Filtered ostapeno/qa-platy_icl5_clen128_maxD-1_maxC10000_0.jsonl to match per subject length from sordonia/qa-platy_icl0_clen128_maxD-1_maxC5000_0 | 146 | [
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] |
ComponentSoft/k8s-kubectl-10k | 2023-10-20T06:29:25.000Z | [
"region:us"
] | ComponentSoft | null | null | 0 | 329 | 2023-10-20T06:29:23 | ---
dataset_info:
features:
- name: objective
dtype: string
- name: command_name
dtype: string
- name: command
dtype: string
- name: description
dtype: string
- name: syntax
dtype: string
- name: flags
dtype: string
- name: question
dtype: string
- name: chain_of_thought
dtype: 'null'
splits:
- name: train
num_bytes: 40356885
num_examples: 35000
download_size: 2584976
dataset_size: 40356885
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "k8s-kubectl-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 719 | [
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corypaik/prost | 2022-10-25T09:07:34.000Z | [
"task_categories:question-answering",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en-US",
"license:apache-2.0",
"arxiv:2106.03634",
"region:us"
] | corypaik | *Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts: direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable. | @inproceedings{aroca-ouellette-etal-2021-prost,
title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time",
author = "Aroca-Ouellette, St{\'e}phane and
Paik, Cory and
Roncone, Alessandro and
Kann, Katharina",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.404",
pages = "4597--4608",
} | 1 | 328 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
extended:
- original
language_creators:
- expert-generated
language:
- en-US
license:
- apache-2.0
multilinguality:
- monolingual
paperswithcode_id: prost
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
- open-domain-qa
---
# PROST: Physical Reasoning about Objects Through Space and Time
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/nala-cub/prost
- **Paper:** https://arxiv.org/abs/2106.03634
- **Leaderboard:**
- **Point of Contact:** [Stéphane Aroca-Ouellette](mailto:stephane.aroca-ouellette@colorado.edu)
### Dataset Summary
*Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts: direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable.
### Supported Tasks and Leaderboards
The task is multiple choice question answering, but you can formulate it multiple ways. You can use `context` and `question` to form cloze style questions, or `context` and `ex_question` as multiple choice question answering. See the [GitHub](https://github.com/nala-cub/prost) repo for examples using GPT-1, GPT-2, BERT, RoBERTa, ALBERT, T5, and UnifiedQA.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en-US`.
## Dataset Structure
### Data Instances
An example looks like this:
```json
{
"A": "glass",
"B": "pillow",
"C": "coin",
"D": "ball",
"context": "A person drops a glass, a pillow, a coin, and a ball from a balcony.",
"ex_question": "Which object is the most likely to break?",
"group": "breaking",
"label": 0,
"name": "breaking_1",
"question": "The [MASK] is the most likely to break."
}
```
### Data Fields
- `A`: Option A (0)
- `B`: Option B (1)
- `C`: Option C (2)
- `D`: Option D (3)
- `context`: Context for the question
- `question`: A cloze style continuation of the context.
- `ex_question`: A multiple-choice style question.
- `group`: The question group, e.g. *bouncing*
- `label`: A ClassLabel indication the correct option
- `name':` The template identifier.
### Data Splits
The dataset contains 18,736 examples for testing.
## Dataset Creation
### Curation Rationale
PROST is designed to avoid models succeeding in unintended ways. First, PROST provides no training data, so as to probe models in a zero-shot fashion. This prevents models from succeeding through spurious correlations between testing and training, and encourages success through a true understanding of and reasoning about the concepts at hand. Second, we manually write templates for all questions in an effort to prevent models from having seen the exact same sentences in their training data. Finally, it focuses on a small set of well defined, objective concepts that only require a small vocabulary. This allows researchers to focus more on the quality of training data rather than on size of it.
### 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
PROST is licensed under the Apache 2.0 license.
### Citation Information
```
@inproceedings{aroca-ouellette-etal-2021-prost,
title = "{PROST}: {P}hysical Reasoning about Objects through Space and Time",
author = "Aroca-Ouellette, St{\'e}phane and
Paik, Cory and
Roncone, Alessandro and
Kann, Katharina",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.404",
pages = "4597--4608",
}
```
### Contributions
Thanks to [@corypaik](https://github.com/corypaik) for adding this dataset.
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yxchar/imdb-tlm | 2021-11-04T18:01:06.000Z | [
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Chris1/cityscapes | 2022-11-03T19:06:29.000Z | [
"region:us"
] | Chris1 | null | null | 1 | 327 | 2022-04-06T10:57:03 | Entry not found | 15 | [
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colbertv2/lotte_passages | 2023-08-23T01:55:55.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | colbertv2 | LoTTE Passages Dataset for ColBERTv2 | @inproceedings{santhanam-etal-2022-colbertv2,
title = "{C}ol{BERT}v2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Santhanam, Keshav and
Khattab, Omar and
Saad-Falcon, Jon and
Potts, Christopher and
Zaharia, Matei",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.272",
pages = "3715--3734",
abstract = "Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-vector representations, late interaction models produce multi-vector representations at the granularity of each token and decompose relevance modeling into scalable token-level computations. This decomposition has been shown to make late interaction more effective, but it inflates the space footprint of these models by an order of magnitude. In this work, we introduce Maize, a retriever that couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction. We evaluate Maize across a wide range of benchmarks, establishing state-of-the-art quality within and outside the training domain while reducing the space footprint of late interaction models by 6{--}10x.",
} | 0 | 327 | 2022-07-14T22:44:41 | ---
viewer: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte passages from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 1M<n<10M
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids:
- extractive-qa
dataset_info:
features:
- name: doc_id
dtype: int32
- name: author
dtype: string
- name: text
dtype: string
splits:
- name: dev_collection
num_bytes: 263355925
num_examples: 268880
- name: test_collection
num_bytes: 105718627
num_examples: 119458
download_size: 225568795
dataset_size: 369074552
---
Passages for the LoTTe dataset used for [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488) | 895 | [
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zishuod/pokemon-icons | 2022-09-24T15:35:39.000Z | [
"task_categories:image-classification",
"license:mit",
"pokemon",
"region:us"
] | zishuod | null | null | 2 | 327 | 2022-09-24T15:12:08 | ---
annotations_creators: []
language: []
language_creators: []
license:
- mit
multilinguality: []
pretty_name: pokemon-icons
size_categories: []
source_datasets: []
tags:
- pokemon
task_categories:
- image-classification
task_ids: []
---
# Dataset Card for pokemon-icons
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Pokemon Icons. Most of them are collected and cropped from screenshots captured in Pokémon Sword and Shield.
### Supported Tasks and Leaderboards
Image classification | 1,672 | [
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] |
dyngnosis/function_names_v2 | 2023-08-02T16:41:15.000Z | [
"region:us"
] | dyngnosis | null | null | 1 | 327 | 2023-08-02T13:42:13 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 81857312.0
num_examples: 60464
- name: test
num_bytes: 20464328.0
num_examples: 15116
download_size: 0
dataset_size: 102321640.0
---
# Dataset Card for "function_names_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 502 | [
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tucan-ai/summaries-de-v1 | 2023-10-18T14:33:42.000Z | [
"region:us"
] | tucan-ai | null | null | 0 | 327 | 2023-09-29T05:55:08 | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 93014092.0
num_examples: 8060
- name: test
num_bytes: 23253523.0
num_examples: 2015
download_size: 68440450
dataset_size: 116267615.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "summaries-de-v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 556 | [
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result-kand2-sdxl-wuerst-karlo/dbd855c1 | 2023-10-04T01:53:22.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | 0 | 327 | 2023-10-04T01:53:21 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 233
num_examples: 10
download_size: 1405
dataset_size: 233
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dbd855c1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 455 | [
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] |
humicroedit | 2023-06-01T14:59:51.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"funnier-headline-identification",
"funniness-score-prediction",
"region:us"
] | null | This new dataset is designed to assess the funniness of edited news headlines. | @article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
journal={arXiv preprint arXiv:1906.00274},
year={2019}
} | 2 | 326 | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- text-scoring
paperswithcode_id: humicroedit
pretty_name: Humicroedit
tags:
- funnier-headline-identification
- funniness-score-prediction
dataset_info:
- config_name: subtask-1
features:
- name: id
dtype: string
- name: original
dtype: string
- name: edit
dtype: string
- name: grades
dtype: string
- name: meanGrade
dtype: float32
splits:
- name: train
num_bytes: 1058589
num_examples: 9652
- name: test
num_bytes: 332113
num_examples: 3024
- name: validation
num_bytes: 269083
num_examples: 2419
- name: funlines
num_bytes: 942376
num_examples: 8248
download_size: 1621456
dataset_size: 2602161
- config_name: subtask-2
features:
- name: id
dtype: string
- name: original1
dtype: string
- name: edit1
dtype: string
- name: grades1
dtype: string
- name: meanGrade1
dtype: float32
- name: original2
dtype: string
- name: edit2
dtype: string
- name: grades2
dtype: string
- name: meanGrade2
dtype: float32
- name: label
dtype:
class_label:
names:
'0': equal
'1': sentence1
'2': sentence2
splits:
- name: train
num_bytes: 2102667
num_examples: 9381
- name: test
num_bytes: 665087
num_examples: 2960
- name: validation
num_bytes: 535044
num_examples: 2355
- name: funlines
num_bytes: 451416
num_examples: 1958
download_size: 1621456
dataset_size: 3754214
config_names:
- subtask-1
- subtask-2
---
# Dataset Card for [Dataset Name]
## 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:**[Humicroedit](https://www.cs.rochester.edu/u/nhossain/humicroedit.html)
- **Repository:**
- **Paper:**["President Vows to Cut Taxes Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines.](http://cs.rochester.edu/~nhossain/humicroedit-naacl-19.pdf)
- **Leaderboard:**
- **Point of Contact:**[nhossain@cs.rochester.edu]
### Dataset Summary
This is the task dataset for SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.
### Supported Tasks and Leaderboards
[Task Description Page](https://competitions.codalab.org/competitions/20970)
- Regression Task: In this task, given the original and the edited headline, the participant is required to predict the mean funniness of the edited headline. Success on this task is typically measured by achieving a *low* Mean Square Error.
- Predict the funnier of the two edited headlines: Given the original headline and two edited versions, the participant has to predict which edited version is the funnier of the two. Success on this task is typically measured by achieving a *high* accuracy.
### Languages
English
## Dataset Structure
### Data Instances
For subtask-1, i.e Given the original and the edited headline, predict the mean funniness of the edited headline.
```
{
'id': 1183,
'original': 'Kushner to visit <Mexico/> following latest trump tirades.',
'edit': 'therapist',
'grades': '33332',
'meanGrade': 2.8
}
```
For subtask-2, i.e Given the original headline and two edited versions, predict which edited version is the funnier of the two.
```
{
'id': 1183,
'original1': 'Gene Cernan , Last <Astronaut/> on the Moon , Dies at 82',
'edit1': 'Dancer',
'grades1': '1113',
'meanGrade1': 1.2,
'original2': 'Gene Cernan , Last Astronaut on the Moon , <Dies/> at 82',
'edit2': 'impregnated',
'grades2': '30001',
'meanGrade2': 0.8,
'label': 1
}
```
### Data Fields
For subtask-1
- `id`: Unique identifier of an edited headline.
- `original`: The headline with replaced word(s) identified with the </> tag.
- `edit`: The new word which replaces the word marked in </> tag in the original field.
- `grades`: 'grades' are the concatenation of all the grades by different annotators.
- `mean` is the mean of all the judges scores.
For subtask-2
- `id`: Unique identifier of an edited headline.
- `original1`: The original headline with replaced word(s) identified with </> tag.
- `edit1`: The new word which replaces the word marked in </> tag in the `original1` field.
- `grades1`: The concatenation of all the grades annotated by different annotators for sentence1.
- `meanGrade1` is the mean of all the judges scores for sentence1.
- `original2`: The original headline with replaced word(s) identified with </> tag.
- `edit2`: The new word which replaces the word marked in </> tag in the `original1` field.
- `grades2`: The concatenation of all the grades annotated by different annotators for the sentence2.
- `meanGrade2` is the mean of all the judges scores for sentence2.
- `label` is 1 if sentence1 is more humourous than sentence2,
2 if sentence 2 is more humorous than sentence1,
0 if both the sentences are equally humorous
### Data Splits
| Sub Task | Train | Dev | Test | Funlines|
| ----- | ------ | ---- | ---- |-----|
| Subtask-1:Regression | 9652 | 2419 | 3024| 8248 |
| Subtask-2: Funnier headline prediction| 9381 | 2355 | 2960| 1958 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Crowd-sourced the data by gamifying it as on the website funlines.co. Players rate the headlines on a scale of 0-4.
Players are scored based on their editing and rating, and they
are ranked on the game’s leaderboard page.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
journal={arXiv preprint arXiv:1906.00274},
year={2019}
}```
### Contributions
Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset. | 7,748 | [
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TurkuNLP/turku_paraphrase_corpus | 2022-07-01T15:25:27.000Z | [
"task_categories:text-classification",
"task_categories:sentence-similarity",
"task_categories:text2text-generation",
"task_categories:other",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:fi",
"license:cc-by-sa-4.0",
"region:us"
] | TurkuNLP | Turku Paraphrase Corpus is a dataset of 104,645 manually annotated Finnish paraphrases. The vast majority of the data is classified as a paraphrase either in the given context, or universally. | @inproceedings{kanerva-etal-2021-finnish,
title = {Finnish Paraphrase Corpus},
author = {Kanerva, Jenna and Ginter, Filip and Chang, Li-Hsin and Rastas, Iiro and Skantsi, Valtteri and Kilpeläinen, Jemina and Kupari, Hanna-Mari and Saarni, Jenna and Sevón, Maija and Tarkka, Otto},
booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa'21)},
year = {2021},
publisher = {Linköping University Electronic Press, Sweden},
url = {https://aclanthology.org/2021.nodalida-main.29},
pages = {288--298}
} | 2 | 326 | 2022-03-02T23:29:22 | ---
YAML tags:
annotations_creators:
- expert-generated
language_creators: []
language:
- fi
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: Turku Paraphrase Corpus
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
- sentence-similarity
- text2text-generation
- other
task_ids:
- semantic-similarity-classification
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://turkunlp.org/paraphrase.html
- **Repository:** https://github.com/TurkuNLP/Turku-paraphrase-corpus
- **Paper:** https://aclanthology.org/2021.nodalida-main.29
- **Leaderboard:** Not available
- **Point of Contact:** [Jenna Kanerva, Filip Ginter](mailto:jmnybl@utu.fi,filip.ginter@gmail.com)
### Dataset Summary
The project gathered a large dataset of Finnish paraphrase pairs (over 100,000). The paraphrases are selected and classified manually, so as to minimize lexical overlap, and provide examples that are maximally structurally and lexically different. The objective is to create a dataset which is challenging and better tests the capabilities of natural language understanding. An important feature of the data is that most paraphrase pairs are distributed in their document context. The primary application for the dataset is the development and evaluation of deep language models, and representation learning in general.
Usage:
```
from datasets import load_dataset
dataset = load_dataset('TurkuNLP/turku_paraphrase_corpus', name="plain")
```
where `name` is one of the supported loading options: `plain`, `plain-context`, `classification`, `classification-context`, or `generation`. See Data Fields for more information.
### Supported Tasks and Leaderboards
* Paraphrase classification
* Paraphrase generation
### Languages
Finnish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example includes two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata. The dataset includes three different configurations: `plain`, `classification`, and `generation`. The `plain` configuration loads the original data without any additional preprocessing or transformations, while the `classification` configuration directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` configuration, the examples are preprocessed to be directly suitable for the paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).
Each pair in the `plain` and `classification` configurations will include fields:
`id`:
Identifier of the paraphrase pair (string)
`gem_id`:
Identifier of the paraphrase pair in the GEM dataset (string)
`goeswith`:
Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data. All examples with the same `goeswith` value (other than `not available`) should be kept together in any train/dev/test split; most users won't need this (string)
`fold`:
0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold, most users won't need this (int)
`text1`:
First paraphrase passage (string)
`text2`:
Second paraphrase passage (string)
`label`:
Manually annotated labels (string)
`binary_label`:
Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)
`is_rewrite`:
Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)
Each pair in the `generation` config will include the same fields except `text1` and `text2` are renamed to `input` and `output` in order to indicate the generation direction. Thus the fields are: `id`, `gem_id`, `goeswith`, `fold`, `input`, `output`, `label`, `binary_label`, and `is_rewrite`
**Context**: Most (but not all) of the paraphrase pairs are identified in their document context. By default, these contexts are not included to conserve memory, but can be accessed using the configurations `plain-context` and `classification-context`. These are exactly like `plain` and `classification` with these additional fields:
`context1`:
a dictionary with the fields `doctext` (string), `begin` (int), `end` (int). These mean that the paraphrase in `text1` was extracted from `doctext[begin:end]`. In most cases, `doctext[begin:end]` and `text1` are the exact same string, but occassionally that is not the case when e.g. intervening punctuations or other unrelated texts were "cleaned" from `text1` during annotation. In case the context is not available, `doctext` is an empty string and `beg==end==0`
`context2`:
same as `context1` but for `text2`
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jmnybl](https://github.com/jmnybl) and [@fginter](https://github.com/fginter) for adding this dataset. | 7,717 | [
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SetFit/tweet_eval_stance_abortion | 2022-09-05T13:09:04.000Z | [
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] |
reciprocate/number-pairs | 2023-05-04T07:14:58.000Z | [
"region:us"
] | reciprocate | null | null | 0 | 326 | 2023-05-03T15:16:41 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 13830.3
num_examples: 900
- name: test
num_bytes: 1536.7
num_examples: 100
download_size: 3812
dataset_size: 15367.0
---
# Dataset Card for "autocrit-testing-numbers"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 497 | [
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] |
medarc/mednli | 2023-09-28T21:15:27.000Z | [
"region:us"
] | medarc | null | null | 0 | 326 | 2023-08-09T23:59:16 | ---
dataset_info:
features:
- name: id
dtype: string
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 2192185
num_examples: 11232
- name: test
num_bytes: 273023
num_examples: 1422
- name: validation
num_bytes: 280149
num_examples: 1395
download_size: 810004
dataset_size: 2745357
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
# Dataset Card for "mednli"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 755 | [
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] |
smangrul/chat-instruct-mixer | 2023-09-08T05:44:19.000Z | [
"region:us"
] | smangrul | null | null | 3 | 326 | 2023-09-08T03:03:01 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 169947792.7111158
num_examples: 73302
- name: test
num_bytes: 48395025.62775446
num_examples: 23318
download_size: 123606462
dataset_size: 218342818.33887026
---
# Chat-Instruct-Mixer Dataset
This dataset is focused on improving LLM logical reasoning skills and conversation skills. It is comprised of the following datasets:
| Dataset Name | Train Mixing Percentage/Samples | Test Mixing Percentage/Samples |
|--------------------------------------------------------------|--------------|------------------|
| [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) | 100% | 300 samples |
| [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) | 100% | 518 samples |
| [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) | 100% minus the samples set aside for test split | 2500 samples |
| [Open-Orca/OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) | 10000 samples from GPT-4 split | 5000 samples |
| [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) | 10000 samples from GPT-4 split | 5000 samples |
| [stingning/ultrachat](https://huggingface.co/datasets/stingning/ultrachat) | 10000 samples | 5000 samples |
| [jondurbin/airoboros-2.2](https://huggingface.co/datasets/jondurbin/airoboros-2.2) | 10000 Samples while filtering out samples with `skip_prompt_formatting==True` | 5000 samples |
Code for Creating this dataset: [ToDo]()
| 1,828 | [
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McGill-NLP/FaithDial | 2023-02-05T04:09:45.000Z | [
"task_categories:conversational",
"task_categories:text-generation",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100k",
"language:en",
"license:mit",
"faithful-dialogue-modeling",
"trustworthy-dialogue-modeling",
"arxiv:2204.10757",
"region:us"
] | McGill-NLP | FaithDial is a new benchmark for hallucination-free dialogues, created by manually editing hallucinated and uncooperative responses in Wizard of Wikipedia. | @article{dziri2022faithdial,
title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},
author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},
journal={arXiv preprint, arXiv:2204.10757},
year={2022},
url={https://arxiv.org/abs/2204.10757}
} | 11 | 325 | 2022-04-24T23:10:52 | ---
annotations_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100k
task_categories:
- conversational
- text-generation
task_ids:
- dialogue-modeling
pretty_name: A Faithful Benchmark for Information-Seeking Dialogue
tags:
- faithful-dialogue-modeling
- trustworthy-dialogue-modeling
---
## Dataset Summary
FaithDial is a faithful knowledge-grounded dialogue benchmark, composed of **50,761** turns spanning **5649** conversations. It was curated through Amazon Mechanical Turk by asking annotators to amend hallucinated utterances in [Wizard of Wikipedia](https://parl.ai/projects/wizard_of_wikipedia/) (WoW). In our dialogue setting, we simulate interactions between two speakers: **an information seeker** and **a bot wizard**. The seeker has a large degree of freedom as opposed to the wizard bot which is more restricted on what it can communicate. In fact, it must abide by the following rules:
- **First**, it should be truthful by providing information that is attributable to the source knowledge *K*.
- **Second**, it should provide information conversationally, i.e., use naturalistic phrasing of *K*, support follow-on discussion with questions, and prompt user's opinions.
- **Third**, it should acknowledge its ignorance of the answer in those cases where *K* does not include it while still moving the conversation forward using *K*.
## Dataset Description
- **Homepage:** [FaithDial](https://mcgill-nlp.github.io/FaithDial/)
- **Repository:** [GitHub](https://github.com/McGill-NLP/FaithDial)
- **Point of Contact:** [Nouha Dziri](mailto:dziri@ualberta.ca)
## Language
English
## Data Instance
An example of 'train' looks as follows:
```text
[
{
"utterances": [
... // prior utterances,
{
"history": [
"Have you ever been to a concert? They're so fun!",
"No I cannot as a bot. However, have you been to Madonna's? Her 10th concert was used to help her 13th album called \"Rebel Heart\".",
"Yeah I've heard of it but never went or what it was for. Can you tell me more about it?"
],
"speaker": "Wizard",
"knowledge": "It began on September 9, 2015, in Montreal, Canada, at the Bell Centre and concluded on March 20, 2016, in Sydney, Australia at Allphones Arena.",
"original_response": "It started in September of 2015 and ran all the way through March of 2016. Can you imagine being on the road that long?",
"response": "Sure. The concert started in September 9th of 2015 at Montreal, Canada. It continued till 20th of March of 2016, where it ended at Sydney, Australia.",
"BEGIN": [
"Hallucination",
"Entailment"
],
"VRM": [
"Disclosure",
"Question"
]
},
... // more utterances
]
},
... // more dialogues
]
```
If the `original_response` is empty, it means that the response is faithful to the source and we consider it as a FaithDial response. Faithful responses in WoW are also edited slightly if they are found to have some grammatical issues or typos.
## Data Fields
- `history`: `List[string]`. The dialogue history.
- `knowledge`: `string`. The source knowkedge on which the bot wizard should ground its response.
- `speaker`: `string`. The current speaker.
- `original response`: `string`. The WoW original response before editing it.
- `response`: `string`. The new Wizard response.
- `BEGIN`: `List[string]`. The BEGIN labels for the Wizard response.
- `VRM`: `List[string]`. The VRM labels for the wizard response.
## Data Splits
- `Train`: 36809 turns
- `Valid`: 6851 turns
- `Test`: 7101 turns
`Valid` includes both the `seen` and the `unseen` data splits from WoW. The same applies to `Test`. We also include those splits for FaithDial valid and test data.
## Annotations
Following the guidelines for ethical crowdsourcing outlined in [Sheehan. 2018](https://www.tandfonline.com/doi/abs/10.1080/03637751.2017.1342043),
we hire Amazon Mechanical Turk (AMT) workers to edit utterances in WoW dialogues that were found to exhibit unfaithful responses. To ensure clarity in the task definition, we provided detailed examples for our terminology. Moreover, we performed several staging rounds over the course of several months.
# Who are the annotators?
To be eligible for the task, workers have to be located in the United States and Canada and have to answer successfully 20 questions as part of a qualification test. Before launching the main annotation task, we perform a small pilot round (60 HITS) to check the performance of the workers. We email workers who commit errors, providing them with examples on how to fix their mistakes in future HITS.
## Personal and Sensitive Information
Seeker utterances in FaithDial may contain personal and sensitive information.
## Social Impact of Dataset
In recent years, the conversational AI market has seen
a proliferation of a variety of applications—which are powered by large pre-trained LMs—that span
across a broad range of domains, such as customer
support, education, e-commerce, health, entertainment, etc. Ensuring that
these systems are trustworthy is key to deploy systems safely at a large scale in real-world application, especially in high-stake domain. FaithDial holds promise to encourage faithfulness in information-seeking dialogue and make virtual assistants both safer and more reliable.
## Licensing Information
MIT
## Citation Information
```bibtex
@article{dziri2022faithdial,
title={FaithDial: A Faithful Benchmark for Information-Seeking Dialogue},
author={Dziri, Nouha and Kamalloo, Ehsan and Milton, Sivan and Zaiane, Osmar and Yu, Mo and Ponti, Edoardo and Reddy, Siva},
journal={arXiv preprint, arXiv:2204.10757},
year={2022},
url={https://arxiv.org/abs/2204.10757}
}
```
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cfilt/HiNER-original | 2023-03-07T16:42:05.000Z | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:hi",
"license:cc-by-sa-4.0",
"arxiv:2204.13743",
"region:us"
] | cfilt | This is the dataset repository for HiNER Dataset accepted to be published at LREC 2022.
The dataset can help build sequence labelling models for the task Named Entity Recognitin for the Hindi language. | 2 | 325 | 2022-04-25T13:55:19 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- hi
license: "cc-by-sa-4.0"
multilinguality:
- monolingual
paperswithcode_id: hiner-original-1
pretty_name: HiNER - Large Hindi Named Entity Recognition dataset
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
---
<p align="center"><img src="https://huggingface.co/datasets/cfilt/HiNER-collapsed/raw/main/cfilt-dark-vec.png" alt="Computation for Indian Language Technology Logo" width="150" height="150"/></p>
# Dataset Card for HiNER-original
[](https://twitter.com/cfiltnlp)
[](https://twitter.com/PeopleCentredAI)
## 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://github.com/cfiltnlp/HiNER
- **Repository:** https://github.com/cfiltnlp/HiNER
- **Paper:** https://arxiv.org/abs/2204.13743
- **Leaderboard:** https://paperswithcode.com/sota/named-entity-recognition-on-hiner-original
- **Point of Contact:** Rudra Murthy V
### Dataset Summary
This dataset was created for the fundamental NLP task of Named Entity Recognition for the Hindi language at CFILT Lab, IIT Bombay. We gathered the dataset from various government information webpages and manually annotated these sentences as a part of our data collection strategy.
**Note:** The dataset contains sentences from ILCI and other sources. ILCI dataset requires license from Indian Language Consortium due to which we do not distribute the ILCI portion of the data. Please send us a mail with proof of ILCI data acquisition to obtain the full dataset.
### Supported Tasks and Leaderboards
Named Entity Recognition
### Languages
Hindi
## Dataset Structure
### Data Instances
{'id': '0', 'tokens': ['प्राचीन', 'समय', 'में', 'उड़ीसा', 'को', 'कलिंग','के', 'नाम', 'से', 'जाना', 'जाता', 'था', '।'], 'ner_tags': [0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0]}
### Data Fields
- `id`: The ID value of the data point.
- `tokens`: Raw tokens in the dataset.
- `ner_tags`: the NER tags for this dataset.
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| original | 76025 | 10861 | 21722|
| collapsed | 76025 | 10861 | 21722|
## About
This repository contains the Hindi Named Entity Recognition dataset (HiNER) published at the Langauge Resources and Evaluation conference (LREC) in 2022. A pre-print via arXiv is available [here](https://arxiv.org/abs/2204.13743).
### Recent Updates
* Version 0.0.5: HiNER initial release
## Usage
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
```code
pip install datasets
```
To use the original dataset with all the tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-original')
```
To use the collapsed dataset with only PER, LOC, and ORG tags, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('cfilt/HiNER-collapsed')
```
However, the CoNLL format dataset files can also be found on this Git repository under the [data](data/) folder.
## Model(s)
Our best performing models are hosted on the HuggingFace models repository:
1. [HiNER-Collapsed-XLM-R](https://huggingface.co/cfilt/HiNER-Collapse-XLM-Roberta-Large)
2. [HiNER-Original-XLM-R](https://huggingface.co/cfilt/HiNER-Original-XLM-Roberta-Large)
## Dataset Creation
### Curation Rationale
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi. This dataset was built for the task of Named Entity Recognition. The dataset was introduced to introduce new resources to the Hindi language that was under-served for Natural Language Processing.
### Source Data
#### Initial Data Collection and Normalization
HiNER was built on data extracted from various government websites handled by the Government of India which provide information in Hindi
#### Who are the source language producers?
Various Government of India webpages
### Annotations
#### Annotation process
This dataset was manually annotated by a single annotator of a long span of time.
#### Who are the annotators?
Pallab Bhattacharjee
### Personal and Sensitive Information
We ensured that there was no sensitive information present in the dataset. All the data points are curated from publicly available information.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large Hindi Named Entity Recognition dataset. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
Any biases contained in the data released by the Indian government are bound to be present in our data.
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Pallab Bhattacharjee
### Licensing Information
CC-BY-SA 4.0
### Citation Information
```latex
@misc{https://doi.org/10.48550/arxiv.2204.13743,
doi = {10.48550/ARXIV.2204.13743},
url = {https://arxiv.org/abs/2204.13743},
author = {Murthy, Rudra and Bhattacharjee, Pallab and Sharnagat, Rahul and Khatri, Jyotsana and Kanojia, Diptesh and Bhattacharyya, Pushpak},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {HiNER: A Large Hindi Named Entity Recognition Dataset},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` | 6,969 | [
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nthngdy/bert_dataset_202203 | 2023-01-17T10:10:06.000Z | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:en",
"license:apache-2.0",
"language-modeling",
"masked-language-modeling",
"region:us"
] | nthngdy | null | null | 0 | 325 | 2023-01-16T14:40:52 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 24635440616
num_examples: 146707688
download_size: 14651841592
dataset_size: 24635440616
license: apache-2.0
task_categories:
- text-generation
- fill-mask
language:
- en
tags:
- language-modeling
- masked-language-modeling
pretty_name: BERT Dataset (BookCorpus + Wikipedia 03/2022)
---
# Dataset Card for "bert_dataset_202203"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 572 | [
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SetFit/go_emotions | 2022-09-08T15:41:33.000Z | [
"region:us"
] | SetFit | null | null | 4 | 324 | 2022-03-02T23:29:22 | # GoEmotions
This dataset is a port of the official [`go_emotions` dataset](https://huggingface.co/datasets/go_emotions) on the Hub. It only contains the `simplified` subset as these are the only fields we need for text classification. | 236 | [
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] |
midas/inspec | 2022-03-05T03:08:37.000Z | [
"arxiv:1910.08840",
"region:us"
] | midas | Benchmark dataset for automatic identification of keyphrases from text published with the work - Improved automatic keyword extraction given more linguistic knowledge. Anette Hulth. In Proceedings of EMNLP 2003. p. 216-223. | @inproceedings{hulth2003improved,
title={Improved automatic keyword extraction given more linguistic knowledge},
author={Hulth, Anette},
booktitle={Proceedings of the 2003 conference on Empirical methods in natural language processing},
pages={216--223},
year={2003}
} | 8 | 324 | 2022-03-02T23:29:22 | A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of English scientific papers. For more details about the dataset please refer the original paper - [https://dl.acm.org/doi/pdf/10.3115/1119355.1119383](https://dl.acm.org/doi/pdf/10.3115/1119355.1119383).
Data source - [https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec](https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec)
## Dataset Summary
The Inspec dataset was originally proposed by *Hulth* in the paper titled - [Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028.pdf) in the year 2003. The dataset consists of abstracts of 2,000 English scientific papers from the [Inspec database](https://clarivate.com/webofsciencegroup/solutions/webofscience-inspec/). The abstracts are from papers belonging to the scientific domains of *Computers and Control* and *Information Technology* published between 1998 to 2002. Each abstract has two sets of keyphrases annotated by professional indexers - *controlled* and *uncontrolled*. The *controlled* keyphrases are obtained from the Inspec thesaurus and therefore are often not present in the abstract's text. Only 18.1% of the *controlled* keyphrases are actually present in the abstract's text. The *uncontrolled* keyphrases are those selected by the indexers after reading the full-length scientific articles and 76.2% of them are present in the abstract's text. There is no information in the original paper about how these 2,000 scientific papers were selected. It is unknown whether the papers were randomly selected out of all the papers published between 1998-2002 in the *Computers and Control* and *Information Technology* domains or were there only 2,000 papers in this domain that were indexed by Inspec. The train, dev and test splits of the data were arbitrarily chosen.
One of the key aspect of this dataset which makes it unique is that it provides keyphrases assigned by professional indexers, which is uncommon in the keyphrase literature. Most of the datasets in this domain have author assigned keyphrases as the ground truth. The dataset shared over here does not explicitly presents the *controlled* and *uncontrolled* keyphrases instead it only categorizes the keyphrases into *extractive* and *abstractive*. **Extractive keyphrases** are those that could be found in the input text and the **abstractive keyphrases** are those that are not present in the input text. In order to get all the meta-data about the documents and keyphrases please refer to the [original source](https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec) from which the dataset was taken from. The main motivation behind making this dataset available in the form as presented over here is to make it easy for the researchers to programmatically download it and evaluate their models for the tasks of keyphrase extraction and generation. As keyphrase extraction by treating it as a sequence tagging task and using contextual language models has become popular - [Keyphrase extraction from scholarly articles as sequence labeling using contextualized embeddings](https://arxiv.org/pdf/1910.08840.pdf), we have also made the token tags available in the BIO tagging format.
## Dataset Structure
## Dataset Statistics
Table 1: Statistics on the length of the abstractive keyphrases for Train, Test, and Validation splits of Inspec dataset.
| | Train | Test | Validation |
|:---------------:|:-----------------------:|:----------------------:|:------------------------:|
| Single word | 9.0% | 9.5% | 10.1% |
| Two words | 50.4% | 48.2% | 45.7% |
| Three words | 27.6% | 28.6% | 29.8% |
| Four words | 9.3% | 10.3% | 10.3% |
| Five words | 2.4% | 2.0% | 3.2% |
| Six words | 0.9% | 1.2% | 0.7% |
| Seven words | 0.3% | 0.2% | 0.2% |
| Eight words | 0.1% | 0% | 0.1% |
| Nine words | 0% | 0.1% | 0% |
Table 2: Statistics on the length of the extractive keyphrases for Train, Test, and Validation splits of Inspec dataset.
| | Train | Test | Validation |
|:------------:|:-----------------------:|:----------------------:|:------------------------:|
| Single word | 16.2% | 15.4% | 17.0% |
| Two words | 52.4% | 54.8% | 51.6% |
| Three words | 24.3% | 22.99% | 24.3% |
| Four words | 5.6% | 4.96% | 5.8% |
| Five words | 1.2% | 1.3% | 1.1% |
| Six words | 0.2% | 0.36% | 0.2% |
| Seven words | 0.1% | 0.06% | 0.1% |
| Eight words | 0% | 0% | 0.03% |
Table 3: General statistics of the Inspec dataset.
| Type of Analysis | Train | Test | Validation |
|:----------------------------------------------:|:------------------------------:|:------------------------------:|:------------------------------:|
| Annotator Type | Professional Indexers | Professional Indexers | Professional Indexers |
| Document Type | Abstracts from Inspec Database | Abstracts from Inspec Database | Abstracts from Inspec Database |
| No. of Documents | 1000 | 500 | 500 |
| Avg. Document length (words) | 141.5 | 134.6 | 132.6 |
| Max Document length (words) | 557 | 384 | 330 |
| Max no. of abstractive keyphrases in a document | 17 | 20 | 14 |
| Min no. of abstractive keyphrases in a document | 0 | 0 | 0 |
| Avg. no. of abstractive keyphrases per document | 3.39 | 3.26 | 3.12 |
| Max no. of extractive keyphrases in a document | 24 | 27 | 22 |
| Min no. of extractive keyphrases in a document | 0 | 0 | 0 |
| Avg. no. of extractive keyphrases per document | 6.39 | 6.56 | 5.95 |
- Percentage of keyphrases that are named entities: 55.25% (named entities detected using scispacy - en-core-sci-lg model)
- Percentage of keyphrases that are noun phrases: 73.59% (noun phrases detected using spacy after removing determiners)
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| No. of datapoints |
|--|--|
| Train | 1,000 |
| Test | 500 |
| Validation | 500 |
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/inspec", "raw")
# sample from the train split
print("Sample from training dataset split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Document BIO Tags: ", train_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation dataset split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
Sample from training data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['A', 'conflict', 'between', 'language', 'and', 'atomistic', 'information', 'Fred', 'Dretske', 'and', 'Jerry', 'Fodor', 'are', 'responsible', 'for', 'popularizing', 'three', 'well-known', 'theses', 'in', 'contemporary', 'philosophy', 'of', 'mind', ':', 'the', 'thesis', 'of', 'Information-Based', 'Semantics', '-LRB-', 'IBS', '-RRB-', ',', 'the', 'thesis', 'of', 'Content', 'Atomism', '-LRB-', 'Atomism', '-RRB-', 'and', 'the', 'thesis', 'of', 'the', 'Language', 'of', 'Thought', '-LRB-', 'LOT', '-RRB-', '.', 'LOT', 'concerns', 'the', 'semantically', 'relevant', 'structure', 'of', 'representations', 'involved', 'in', 'cognitive', 'states', 'such', 'as', 'beliefs', 'and', 'desires', '.', 'It', 'maintains', 'that', 'all', 'such', 'representations', 'must', 'have', 'syntactic', 'structures', 'mirroring', 'the', 'structure', 'of', 'their', 'contents', '.', 'IBS', 'is', 'a', 'thesis', 'about', 'the', 'nature', 'of', 'the', 'relations', 'that', 'connect', 'cognitive', 'representations', 'and', 'their', 'parts', 'to', 'their', 'contents', '-LRB-', 'semantic', 'relations', '-RRB-', '.', 'It', 'holds', 'that', 'these', 'relations', 'supervene', 'solely', 'on', 'relations', 'of', 'the', 'kind', 'that', 'support', 'information', 'content', ',', 'perhaps', 'with', 'some', 'help', 'from', 'logical', 'principles', 'of', 'combination', '.', 'Atomism', 'is', 'a', 'thesis', 'about', 'the', 'nature', 'of', 'the', 'content', 'of', 'simple', 'symbols', '.', 'It', 'holds', 'that', 'each', 'substantive', 'simple', 'symbol', 'possesses', 'its', 'content', 'independently', 'of', 'all', 'other', 'symbols', 'in', 'the', 'representational', 'system', '.', 'I', 'argue', 'that', 'Dretske', "'s", 'and', 'Fodor', "'s", 'theories', 'are', 'false', 'and', 'that', 'their', 'falsehood', 'results', 'from', 'a', 'conflict', 'IBS', 'and', 'Atomism', ',', 'on', 'the', 'one', 'hand', ',', 'and', 'LOT', ',', 'on', 'the', 'other']
Document BIO Tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['philosophy of mind', 'content atomism', 'ibs', 'language of thought', 'lot', 'cognitive states', 'beliefs', 'desires']
Abstractive/absent Keyphrases: ['information-based semantics']
-----------
Sample from validation data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Impact', 'of', 'aviation', 'highway-in-the-sky', 'displays', 'on', 'pilot', 'situation', 'awareness', 'Thirty-six', 'pilots', '-LRB-', '31', 'men', ',', '5', 'women', '-RRB-', 'were', 'tested', 'in', 'a', 'flight', 'simulator', 'on', 'their', 'ability', 'to', 'intercept', 'a', 'pathway', 'depicted', 'on', 'a', 'highway-in-the-sky', '-LRB-', 'HITS', '-RRB-', 'display', '.', 'While', 'intercepting', 'and', 'flying', 'the', 'pathway', ',', 'pilots', 'were', 'required', 'to', 'watch', 'for', 'traffic', 'outside', 'the', 'cockpit', '.', 'Additionally', ',', 'pilots', 'were', 'tested', 'on', 'their', 'awareness', 'of', 'speed', ',', 'altitude', ',', 'and', 'heading', 'during', 'the', 'flight', '.', 'Results', 'indicated', 'that', 'the', 'presence', 'of', 'a', 'flight', 'guidance', 'cue', 'significantly', 'improved', 'flight', 'path', 'awareness', 'while', 'intercepting', 'the', 'pathway', ',', 'but', 'significant', 'practice', 'effects', 'suggest', 'that', 'a', 'guidance', 'cue', 'might', 'be', 'unnecessary', 'if', 'pilots', 'are', 'given', 'proper', 'training', '.', 'The', 'amount', 'of', 'time', 'spent', 'looking', 'outside', 'the', 'cockpit', 'while', 'using', 'the', 'HITS', 'display', 'was', 'significantly', 'less', 'than', 'when', 'using', 'conventional', 'aircraft', 'instruments', '.', 'Additionally', ',', 'awareness', 'of', 'flight', 'information', 'present', 'on', 'the', 'HITS', 'display', 'was', 'poor', '.', 'Actual', 'or', 'potential', 'applications', 'of', 'this', 'research', 'include', 'guidance', 'for', 'the', 'development', 'of', 'perspective', 'flight', 'display', 'standards', 'and', 'as', 'a', 'basis', 'for', 'flight', 'training', 'requirements']
Document BIO Tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['flight simulator', 'pilots', 'cockpit', 'flight guidance', 'situation awareness', 'flight path awareness']
Abstractive/absent Keyphrases: ['highway-in-the-sky display', 'human factors', 'aircraft display']
-----------
Sample from test data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['A', 'new', 'graphical', 'user', 'interface', 'for', 'fast', 'construction', 'of', 'computation', 'phantoms', 'and', 'MCNP', 'calculations', ':', 'application', 'to', 'calibration', 'of', 'in', 'vivo', 'measurement', 'systems', 'Reports', 'on', 'a', 'new', 'utility', 'for', 'development', 'of', 'computational', 'phantoms', 'for', 'Monte', 'Carlo', 'calculations', 'and', 'data', 'analysis', 'for', 'in', 'vivo', 'measurements', 'of', 'radionuclides', 'deposited', 'in', 'tissues', '.', 'The', 'individual', 'properties', 'of', 'each', 'worker', 'can', 'be', 'acquired', 'for', 'a', 'rather', 'precise', 'geometric', 'representation', 'of', 'his', '-LRB-', 'her', '-RRB-', 'anatomy', ',', 'which', 'is', 'particularly', 'important', 'for', 'low', 'energy', 'gamma', 'ray', 'emitting', 'sources', 'such', 'as', 'thorium', ',', 'uranium', ',', 'plutonium', 'and', 'other', 'actinides', '.', 'The', 'software', 'enables', 'automatic', 'creation', 'of', 'an', 'MCNP', 'input', 'data', 'file', 'based', 'on', 'scanning', 'data', '.', 'The', 'utility', 'includes', 'segmentation', 'of', 'images', 'obtained', 'with', 'either', 'computed', 'tomography', 'or', 'magnetic', 'resonance', 'imaging', 'by', 'distinguishing', 'tissues', 'according', 'to', 'their', 'signal', '-LRB-', 'brightness', '-RRB-', 'and', 'specification', 'of', 'the', 'source', 'and', 'detector', '.', 'In', 'addition', ',', 'a', 'coupling', 'of', 'individual', 'voxels', 'within', 'the', 'tissue', 'is', 'used', 'to', 'reduce', 'the', 'memory', 'demand', 'and', 'to', 'increase', 'the', 'calculational', 'speed', '.', 'The', 'utility', 'was', 'tested', 'for', 'low', 'energy', 'emitters', 'in', 'plastic', 'and', 'biological', 'tissues', 'as', 'well', 'as', 'for', 'computed', 'tomography', 'and', 'magnetic', 'resonance', 'imaging', 'scanning', 'information']
Document BIO Tags: ['O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'I', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'I', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'B', 'I', 'O', 'O', 'B', 'I', 'I', 'I', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'I', 'I', 'I', 'I']
Extractive/present Keyphrases: ['computational phantoms', 'monte carlo calculations', 'in vivo measurements', 'radionuclides', 'tissues', 'worker', 'precise geometric representation', 'mcnp input data file', 'scanning data', 'computed tomography', 'brightness', 'graphical user interface', 'computation phantoms', 'calibration', 'in vivo measurement systems', 'signal', 'detector', 'individual voxels', 'memory demand', 'calculational speed', 'plastic', 'magnetic resonance imaging scanning information', 'anatomy', 'low energy gamma ray emitting sources', 'actinides', 'software', 'automatic creation']
Abstractive/absent Keyphrases: ['th', 'u', 'pu', 'biological tissues']
-----------
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/inspec", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the train split
print("Sample from training data split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Document BIO Tags: ", train_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/inspec", "generation")
print("Samples for Keyphrase Generation")
# sample from the train split
print("Sample from training data split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
Please cite the works below if you use this dataset in your work.
```
@inproceedings{hulth2003improved,
title={Improved automatic keyword extraction given more linguistic knowledge},
author={Hulth, Anette},
booktitle={Proceedings of the 2003 conference on Empirical methods in natural language processing},
pages={216--223},
year={2003}
}
```
and
```
@InProceedings{10.1007/978-3-030-45442-5_41,
author="Sahrawat, Dhruva
and Mahata, Debanjan
and Zhang, Haimin
and Kulkarni, Mayank
and Sharma, Agniv
and Gosangi, Rakesh
and Stent, Amanda
and Kumar, Yaman
and Shah, Rajiv Ratn
and Zimmermann, Roger",
editor="Jose, Joemon M.
and Yilmaz, Emine
and Magalh{\~a}es, Jo{\~a}o
and Castells, Pablo
and Ferro, Nicola
and Silva, M{\'a}rio J.
and Martins, Fl{\'a}vio",
title="Keyphrase Extraction as Sequence Labeling Using Contextualized Embeddings",
booktitle="Advances in Information Retrieval",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="328--335",
abstract="In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architecture using both contextualized and fixed word embedding models on three different benchmark datasets, and compare with existing popular unsupervised and supervised techniques. Our results quantify the benefits of: (a) using contextualized embeddings over fixed word embeddings; (b) using a BiLSTM-CRF architecture with contextualized word embeddings over fine-tuning the contextualized embedding model directly; and (c) using domain-specific contextualized embeddings (SciBERT). Through error analysis, we also provide some insights into why particular models work better than the others. Lastly, we present a case study where we analyze different self-attention layers of the two best models (BERT and SciBERT) to better understand their predictions.",
isbn="978-3-030-45442-5"
}
```
and
```
@article{kulkarni2021learning,
title={Learning Rich Representation of Keyphrases from Text},
author={Kulkarni, Mayank and Mahata, Debanjan and Arora, Ravneet and Bhowmik, Rajarshi},
journal={arXiv preprint arXiv:2112.08547},
year={2021}
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset
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hf-internal-testing/fixtures_ocr | 2021-12-07T08:07:29.000Z | [
"region:us"
] | hf-internal-testing | \\n | \\n | 0 | 323 | 2022-03-02T23:29:22 | This dataset includes 2 images: one of the [IAM Handwriting Database](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database) and one of the [SRIOE](https://rrc.cvc.uab.es/?ch=13) dataset.
They are used for testing OCR models that are part of the HuggingFace Transformers library. See [here](https://github.com/huggingface/transformers/search?q=fixtures_ocr) for details.
More specifically, they are used inside `test_modeling_vision_encoder_decoder_model.py`, for testing the TrOCR models. | 499 | [
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] |
Bingsu/zeroth-korean | 2022-08-15T10:30:30.000Z | [
"task_categories:automatic-speech-recognition",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|kresnik/zeroth_korean",
"language:ko",
"license:cc-by-4.0",
"region:us"
] | Bingsu | null | null | 10 | 323 | 2022-08-14T08:50:33 | ---
language:
- ko
language_creators:
- crowdsourced
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: zeroth-korean
source_datasets:
- extended|kresnik/zeroth_korean
size_categories:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
---
# Zeroth-Korean
## Dataset Description
- **Homepage:** [OpenSLR](https://www.openslr.org/40/)
- **Repository:** [goodatlas/zeroth](https://github.com/goodatlas/zeroth)
- **Download Size** 2.68 GiB
- **Generated Size** 2.85 GiB
- **Total Size** 5.52 GiB
## Zeroth-Korean
The data set contains transcriebed audio data for Korean. There are 51.6 hours transcribed Korean audio for training data (22,263 utterances, 105 people, 3000 sentences) and 1.2 hours transcribed Korean audio for testing data (457 utterances, 10 people). This corpus also contains pre-trained/designed language model, lexicon and morpheme-based segmenter(morfessor).
Zeroth project introduces free Korean speech corpus and aims to make Korean speech recognition more broadly accessible to everyone.
This project was developed in collaboration between Lucas Jo(@Atlas Guide Inc.) and Wonkyum Lee(@Gridspace Inc.).
Contact: Lucas Jo(lucasjo@goodatlas.com), Wonkyum Lee(wonkyum@gridspace.com)
### License
CC BY 4.0
## Dataset Structure
### Data Instance
```pycon
>>> from datasets import load_dataset
>>> dataset = load_dataset("Bingsu/zeroth-korean")
>>> dataset
DatasetDict({
train: Dataset({
features: ['audio', 'text'],
num_rows: 22263
})
test: Dataset({
features: ['text', 'audio'],
num_rows: 457
})
})
```
### Data Size
download: 2.68 GiB<br>
generated: 2.85 GiB<br>
total: 5.52 GiB
### Data Fields
- audio: `audio`, sampling rate = 16000
- A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate.
- Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the "audio" column, i.e. `dataset[0]["audio"]` should always be preferred over `dataset["audio"][0]`.
- text: `string`
```pycon
>>> dataset["train"][0]
{'audio': {'path': None,
'array': array([-3.0517578e-05, 0.0000000e+00, -3.0517578e-05, ...,
0.0000000e+00, 0.0000000e+00, -6.1035156e-05], dtype=float32),
'sampling_rate': 16000},
'text': '인사를 결정하는 과정에서 당 지도부가 우 원내대표 및 원내지도부와 충분한 상의를 거치지 않은 채 일방적으로 인사를 했다는 불만도 원내지도부를 중심으로 흘러나왔다'}
```
### Data Splits
| | train | test |
| ---------- | -------- | ----- |
| # of data | 22263 | 457 |
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EleutherAI/the_pile_deduplicated | 2022-12-02T23:49:09.000Z | [
"region:us"
] | EleutherAI | null | null | 40 | 323 | 2022-12-02T20:03:38 | Entry not found | 15 | [
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marmal88/skin_cancer | 2023-01-25T02:21:28.000Z | [
"task_categories:image-classification",
"task_categories:image-segmentation",
"size_categories:1K<n<10K",
"language:en",
"skin_cancer",
"HAM10000",
"region:us"
] | marmal88 | null | null | 4 | 323 | 2023-01-24T13:53:28 | ---
dataset_info:
features:
- name: image
dtype: image
- name: image_id
dtype: string
- name: lesion_id
dtype: string
- name: dx
dtype: string
- name: dx_type
dtype: string
- name: age
dtype: float64
- name: sex
dtype: string
- name: localization
dtype: string
splits:
- name: train
num_bytes: 2490501038.358
num_examples: 9577
- name: test
num_bytes: 351507473.24
num_examples: 1285
- name: validation
num_bytes: 681758880.144
num_examples: 2492
download_size: 3693626934
dataset_size: 3523767391.7419996
task_categories:
- image-classification
- image-segmentation
language:
- en
tags:
- skin_cancer
- HAM10000
pretty_name: HAM10000
size_categories:
- 1K<n<10K
---
# The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
- Original Paper and Dataset [here](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T)
- Kaggle dataset [here](https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000?resource=download)
# Introduction to datasets
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).
More than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal).
The test set is not public, but the evaluation server remains running (see the challenge website). Any publications written using the HAM10000 data should be evaluated on the official test set hosted there, so that methods can be fairly compared.
- Test site can be accessed [here](https://challenge.isic-archive.com/landing/2018/)
# Disclaimer and additional information
This is a contribution to open sourced data in hugging face for image data. Images can be obtained from above links.
Train test split was done using a stratified splitting by cancer/diagnosis type. The code to stratify the dataset can be obtained on my github [here](https://github.com/marmal88/skin_cancer).
I do not own any rights to above images.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 3,236 | [
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thesistranslation/wmt14 | 2023-08-09T13:08:40.000Z | [
"region:us"
] | thesistranslation | null | @InProceedings{bojar-EtAl:2014:W14-33,
author = {Bojar, Ondrej and Buck, Christian and Federmann, Christian and Haddow, Barry and Koehn, Philipp and Leveling, Johannes and Monz, Christof and Pecina, Pavel and Post, Matt and Saint-Amand, Herve and Soricut, Radu and Specia, Lucia and Tamchyna, Ale\v{s}},
title = {Findings of the 2014 Workshop on Statistical Machine Translation},
booktitle = {Proceedings of the Ninth Workshop on Statistical Machine Translation},
month = {June},
year = {2014},
address = {Baltimore, Maryland, USA},
publisher = {Association for Computational Linguistics},
pages = {12--58},
url = {http://www.aclweb.org/anthology/W/W14/W14-3302}
} | 0 | 323 | 2023-07-31T09:21:04 | # Aim of this dataset
The code used to retrieve and create this dataset is almost identical to the one that you can find here [wmt14](https://huggingface.co/datasets/wmt14).
We only added the possibility to retrieve the "es-en" translation pairs from the wmt13. Keep in mind that for this language pair the validation and test sets are the newstest2012 and newstest2013 respectively.
**Pay attention**: some es-en pair sentences on the validation set contain the backslash followed by a double quote character (\\").
Thanks to the Huggingface team for all the work they have done! | 582 | [
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ziozzang/EverythingLM-data-V2-Ko | 2023-08-23T07:03:47.000Z | [
"language:ko",
"license:mit",
"region:us"
] | ziozzang | null | null | 8 | 323 | 2023-08-23T06:53:09 | ---
license: mit
language:
- ko
---
# Translated into Korean with DeepL
All Texts are translated with DeepL. (Machine Translated.)
- Issue: some data items are missing, cause of DeepL plan and processing method. I use very cheap plan and all datas are merged into single file and splitted by few code and hand.
- This is sample/test processing of data set creation with DeepL.
- Original Dataset: totally-not-an-llm/EverythingLM-data-V2
# EverythingLM V2 Dataset
**EverythingLM V2** is a diverse instruct dataset consisting of 1k of human-assistant conversations. These sets were generated using principles from both evol-instruct and Orca. The dataset encompasses a wide array of topics and interactions.
### Differences for V1:
- All data in V2 is generated by GPT4
- Higher quality dataset generation pipeline:
- More humalike seed prompts
- Fixed some bugs in the script
- More diverse creative writing
- More diverse seed prompts in general
- Attempt not to overfit the model on complex instructions by occasionally skipping evol
### Cost:
Reproducing this dataset would cost roughly $40.
### Instruction Categories:
- Reasoning
- Creative Writing
- General Knowledge
- Brainstorming
- Search Query
- Coding
- Basic Instruct
We also leverage various system prompts for evol-instruct and for responding to prompts.
This dataset has also been filtered to remove OpenAI alignment.
### How it stands out:
- Long, detailed outputs
- Humanlike creativity
- CoT reasoning
- Complex & challenging tasks
### Plans:
- Train Llama 7b & 13b models (13b model V1 trained)
- Train Llama 70b QLoRA
- Generate V2 of the dataset, with more categories and GPT-4 (DONE) ✓
Included in this repo is the script to generate the dataset. | 1,746 | [
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] |
autshumato | 2023-06-01T14:59:51.000Z | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"language:tn",
"language:ts",
"language:zu",
"license:cc-by-2.5",
"region:us"
] | null | Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine translation systems for three South African languages pairs. | @article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
} | 2 | 322 | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
- tn
- ts
- zu
license:
- cc-by-2.5
multilinguality:
- multilingual
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: autshumato
dataset_info:
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features:
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dtype:
translation:
languages:
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splits:
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num_bytes: 28826392
num_examples: 159000
download_size: 9458762
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features:
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dtype:
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languages:
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splits:
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num_bytes: 7188970
num_examples: 35489
download_size: 2068891
dataset_size: 7188970
- config_name: autshumato-en-ts
features:
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dtype:
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languages:
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splits:
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num_bytes: 50803849
num_examples: 450000
download_size: 15145915
dataset_size: 50803849
- config_name: autshumato-en-ts-manual
features:
- name: translation
dtype:
translation:
languages:
- en
- ts
splits:
- name: train
num_bytes: 10408757
num_examples: 92396
download_size: 2876924
dataset_size: 10408757
- config_name: autshumato-tn
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5132267
num_examples: 38206
download_size: 1599029
dataset_size: 5132267
- config_name: autshumato-ts
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3399674
num_examples: 58398
download_size: 974488
dataset_size: 3399674
config_names:
- autshumato-en-tn
- autshumato-en-ts
- autshumato-en-ts-manual
- autshumato-en-zu
- autshumato-tn
- autshumato-ts
---
# Dataset Card for autshumato
## 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://repo.sadilar.org/handle/20.500.12185/7/discover]()
- **Repository:** []()
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
Multilingual information access is stipulated in the South African constitution. In practise, this
is hampered by a lack of resources and capacity to perform the large volumes of translation
work required to realise multilingual information access. One of the aims of the Autshumato
project is to develop machine translation systems for three South African languages pairs.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### 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
[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
[More Information Needed]
### Dataset Curators
[More Information Needed]
### Licensing Information
### Citation Information
```
@article{groenewald2010processing,
title={Processing parallel text corpora for three South African language pairs in the Autshumato project},
author={Groenewald, Hendrik J and du Plooy, Liza},
journal={AfLaT 2010},
pages={27},
year={2010}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) for adding this dataset. | 5,068 | [
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ChaiML/20231007_chai_prize_model_feedback_all | 2023-10-08T00:36:27.000Z | [
"region:us"
] | ChaiML | null | null | 1 | 322 | 2023-10-08T00:34:03 | ---
dataset_info:
features:
- name: conversation_id
dtype: string
- name: bot_id
dtype: string
- name: user_id
dtype: string
- name: conversation
dtype: string
- name: thumbs_up
dtype: bool
- name: feedback
dtype: string
- name: model_name
dtype: string
- name: season
dtype: string
splits:
- name: train
num_bytes: 242533107
num_examples: 124233
download_size: 132332865
dataset_size: 242533107
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "20231007_chai_prize_model_feedback_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | 744 | [
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] |
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