datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
sequence | task_categories
sequence | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
sequence |
|---|---|---|---|---|---|---|---|---|---|
slava-medvedev/zelensky-speeches | slava-medvedev | 2023-12-17T23:24:45Z | 88 | 2 | [
"task_categories:summarization",
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:uk",
"language:en",
"license:cc-by-4.0",
"zelensky",
"ukraine",
"politics",
"region:us"
] | [
"summarization",
"text-classification"
] | 2023-11-14T18:43:21Z | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- uk
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
task_categories:
- summarization
- text-classification
pretty_name: 'Speeches given by the president of Ukraine Volodymyr Zelensky
Language: Ukrainian
Source: https://www.president.gov.ua/news/speeches'
dataset_info:
features:
- name: date
dtype: int64
- name: link
dtype: string
- name: topic
dtype: string
- name: full_text
dtype: string
- name: lang
dtype: string
splits:
- name: train
num_bytes: 14119362
num_examples: 1928
download_size: 7090045
dataset_size: 14119362
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- zelensky
- ukraine
- politics
---
# Dataset Card for "zelenskiy-speeches"
Speeches given by the president of Ukraine Volodymyr Zelensky
Languages: Ukrainian, English
Source: [president.gov.ua](https://www.president.gov.ua/news/speeches)
Auto-updated daily by Github Actions of [zelensky-speech-fetcher](https://github.com/medvedev/zelensky-speech-fetcher)
License: [CC BY-NC-ND 4.0 Deed](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) | [
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] |
IconicAI/janet-textclassification-10k | IconicAI | 2023-12-12T13:27:51Z | 86 | 0 | [
"task_categories:text-classification",
"region:us"
] | [
"text-classification"
] | 2023-11-17T15:12:52Z | ---
task_categories:
- text-classification
---
---
tags:
- gpt-4
- janet
---
# Description
10k user questions for a single class classification task.
All questions are related to a hypothetical game in whichn the user is a sniper
and has to answer questions from a commander. The questions in the dataset
are the ones that the user is expected to answer.
# Schema
```
{
"question": Ds.Value("string"),
"topic": Ds.ClassLabel(names=[
"about_to_shoot",
"ballistics",
"civilians",
"countersniping",
"dark_hair",
"description_age",
"description_body",
"description_crime",
"description_face",
"description_height",
"description_no_hair",
"description_pose",
"description_race",
"description_request",
"description_request_armed",
"description_request_behavior",
"description_request_clothing",
"description_request_companions",
"description_request_location",
"description_request_tattoo",
"description_request_transport",
"description_right_handed",
"description_sex",
"description_skin_color",
"description_weight",
"easter_egg_go_on_date",
"extraction",
"goodbye",
"hello",
"how_are_you",
"light_hair",
"permission_to_fire",
"request_change_location",
"returning_to_base",
"say_that_again",
"searching",
"secondary_targets",
"target_down",
"target_down_negative",
"target_identified",
"target_identified_maybe",
"target_identified_negative",
"target_name",
"thanks",
"thanks_and_goodbye",
"time_constraint",
"wearing_eyewear",
"wearing_eyewear_negative",
"what_to_do",
],),
}
```
# Citation
```
@misc{JanetTextClassification10k,
title = {JanetTextClassification10k: A Dataset of user questions for a single class classification task.},
author = {Kieran Donaldson and Piotr Trochim},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\\url{https://huggingface.co/datasets/IconicAI/janet-textclassification-10k}},
}
``` | [
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umarbutler/open-australian-legal-qa | umarbutler | 2023-12-14T06:58:56Z | 84 | 2 | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_ids:closed-domain-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"size_categories:1K<n<10K",
"source_datasets:umarbutler/open-australian-legal-corpus",
"language:en",
"license:other",
"law",
"legal",
"australia",
"question-answering",
"qa",
"question-answer",
"text-generation",
"llm",
"chatbot",
"conversational-ai",
"generative-ai",
"natural-language-understanding",
"fine-tuning",
"doi:10.57967/hf/1479",
"region:us"
] | [
"question-answering",
"text-generation",
"text2text-generation"
] | 2023-11-18T10:35:19Z | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license: other
size_categories:
- 1K<n<10K
source_datasets:
- umarbutler/open-australian-legal-corpus
task_categories:
- question-answering
- text-generation
- text2text-generation
task_ids:
- closed-domain-qa
pretty_name: Open Australian Legal QA
license_name: open-australian-legal-corpus
license_link: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md
tags:
- law
- legal
- australia
- question-answering
- qa
- question-answer
- text-generation
- llm
- chatbot
- conversational-ai
- generative-ai
- natural-language-understanding
- fine-tuning
language_details: en-AU, en-GB
viewer: true
dataset_info:
config_name: train
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: prompt
dtype: string
- name: source
struct:
- name: version_id
dtype: string
- name: type
dtype: string
- name: jurisdiction
dtype: string
- name: source
dtype: string
- name: citation
dtype: string
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 13243775
num_examples: 2124
download_size: 13538191
dataset_size: 13243775
---
<!-- To update the above `dataset_info` section, please run the following command: `datasets-cli test open_australian_legal_qa.py --save_info --all_configs`. -->
# **Open Australian Legal QA ⚖️**
<a href="https://huggingface.co/datasets/umarbutler/open-australian-legal-qa" alt="Release"><img src="https://img.shields.io/badge/release-v2.0.0-green"></a>
Open Australian Legal QA is the first open dataset of Australian legal questions and answers.
Comprised of 2,124 questions and answers synthesised by `gpt-4` from the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), the largest open database of Australian law, the dataset is intended to facilitate the development of legal AI assistants in Australia.
To ensure its accessibility to as wide an audience as possible, the dataset is distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Usage 👩💻
The below code snippet illustrates how the dataset may be loaded with the [Hugging Face Datasets](https://huggingface.co/docs/datasets/index) Python library:
```python
from datasets import load_dataset
corpus = load_dataset('umarbutler/open_australian_legal_qa', split='train')
```
To speed up the loading of the dataset, you may wish to install [`orjson`](https://github.com/ijl/orjson).
## Structure 🗂️
The dataset is stored in [qa.jsonl](https://huggingface.co/datasets/umarbutler/open-australian-legal-qa/blob/main/qa.jsonl), a json lines file where each line represents a question-answer pair consisting of four keys:
| Key | Description |
| --- | --- |
| question | The text of the question. |
| answer | The text of the answer to the question. |
| text | The text of the question and answer in the format `Question: {question}\nAnswer: {answer}`. |
| prompt | The text of the prompt used to generate the question-answer pair. |
| source | A dictionary representing the document from which the question-answer pair was synthesised, sharing the same keys as documents in the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus), with the `text` field constituting the text of the chunk used to generate the pair. |
## Methodology 🧪
2,124 documents from the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus) were randomly sampled, barring bills and documents consisting entirely of whitespace. These documents were then split into semantically meaningful chunks up to 384-tokens-long (as determined by [`tiktoken`](https://github.com/openai/tiktoken)'s tokeniser for `gpt-4`) with the [`semchunk`](https://github.com/umarbutler/semchunk) Python library.
Chunks that consisted entirely of whitespace, contained 6 or more consecutive periods, ignoring whitespace (indicating that they contained a table of contents) or that were less than 96-tokens-long were discarded. A single chunk was randomly selected from each document (for those documents with a chunk to select) and subsequently cleaned of consecutive newlines, consecutive whitespace and lines consisting entirely of whitespace.
These chunks were then embedded into the following prompt, with the names of jurisdictions and types being capitalised and stripped of hyphens:
```xml
# Snippet
The snippet from an Australian legal document from which you must synthesise a question and answer is provided below.
<document_metadata>
<document_title><!-- insert citation here --></document_title>
<document_jurisdiction><!-- insert jurisdiction here --></document_jurisdiction>
<document_type><!-- insert type here --></document_type>
</document_metadata>
<snippet>
<!-- insert text here -->
</snippet>
# Format
You must format your response as follows:
<format>
# Question
{A question related to the snippet, or a topic discussed therein.}
# Answer
{The answer to the question, extracted from the snippet.}
</format>
# Instructions
You must act as a question-and-answer synthesiser that takes a snippet from an Australian legal document and synthesises a question related to the snippet, or a topic discussed therein, and an answer to that question, extracted from the snippet.
Your question must be decontextualised and standalone from the snippet. If the question pertains to a particular jurisdiction or document, it must state that explicitly (eg, 'In Victoria, is it lawful for ...?', 'What did the Court decide in Mabo v Queensland (No 2) [1992] HCA 23?', etc...).
Your answer must also be decontextualised and standalone from the snippet. It must reference the document from which it came (eg, 'Under the Crimes Act 1958 (Vic), ...', 'In Mabo v Queensland (No 2) [1992] HCA 23, the Court decided ...', etc...), not the snippet itself. It must be capable of being understood on its own and without reference to the snippet or its source document.
When referring to a document (eg, the Crimes Act) or a part thereof (eg, Paragraph 1), or to a person (eg, the Minister), organisation (eg, the Department) or concept (eg, the rule of law), you must refer to it by its full name (eg, the Crimes Act 1958 (Vic) instead of the Crimes Act, Paragraph 1 of ABC v XYZ instead of Paragraph 1, the Commonwealth Minister for Finance instead of the Minister).
If it is not possible to synthesise a question and answer from the snippet, you must respond with `<!no_qa!>`. Otherwise, your response must conform to the provided format.
```
The resulting prompts were then sent to `gpt-4` with the following hyperparameters:
| Hyperparameter | Value |
| --- | --- |
| `temperature` | 0 |
| `top_p` | 1 |
| `frequency_penalty` | 0 |
| `presence_penalty` | 0 |
| `max_tokens` | 768 |
`gpt-4`'s responses were parsed with the regex pattern `#\s?Question:?\s+((?:\n|.)+)#\s?Answer:?\s+((?:\n|.)+)`, yielding the question-answer pairs. Any malformed responses were discarded.
## Changelog 🔄
All notable changes to the dataset are documented in its [Changelog 🔄](https://huggingface.co/datasets/umarbutler/open-australian-legal-qa/blob/main/CHANGELOG.md).
This project adheres to [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) and [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## Licence 📜
The dataset is distributed under the same licence as the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/blob/main/LICENCE.md).
## Citation 🔖
If you've relied on the dataset for your work, please cite:
```latex
@misc{butler-2023-open-australian-legal-dataset,
author = {Butler, Umar},
year = {2023},
title = {Open Australian Legal QA},
publisher = {Hugging Face},
version = {2.0.0},
doi = {10.57967/hf/1479},
url = {https://huggingface.co/datasets/umarbutler/open-australian-legal-qa}
}
```
## Acknowledgements 🙏
In the spirit of reconciliation, the author acknowledges the Traditional Custodians of Country throughout Australia and their connections to land, sea and community. He pays his respect to their Elders past and present and extends that respect to all Aboriginal and Torres Strait Islander peoples today.
The author thanks Matthew Altenberg, who gave him the idea of using `gpt-4` to synthesise questions and answers from the [Open Australian Legal Corpus](https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus).
The author also acknowledges the creators of the many Python libraries relied upon in the creation of the dataset.
Finally, the author is eternally grateful for the endless support of his wife and her willingness to put up with many a late night spent writing code and quashing bugs. | [
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habanoz/airoboros-3.1-no-mathjson-max-1k-chat-format | habanoz | 2023-12-16T15:13:46Z | 84 | 0 | [
"region:us"
] | null | 2023-12-14T11:24:07Z | ---
dataset_info:
features:
- name: category
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 11937413
num_examples: 20180
download_size: 5699534
dataset_size: 11937413
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Copy of [habanoz/airoboros-3.1-no-mathjson-max-1k](https://huggingface.co/datasets/habanoz/airoboros-3.1-no-mathjson-max-1k) transformed to work with huggingface chat templates e.g. role(user|assistant), content. | [
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davanstrien/notebooks_by_repo_type | davanstrien | 2023-12-17T14:26:20Z | 82 | 0 | [
"region:us"
] | null | 2023-03-27T09:16:12Z | ---
dataset_info:
features:
- name: repo_type
dtype: large_string
- name: repo_notebook_count
dtype: int64
- name: date
dtype: date32
splits:
- name: train
num_bytes: 11500
num_examples: 460
download_size: 5365
dataset_size: 11500
---
# Dataset Card for "notebooks_by_repo_type"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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davanstrien/notebooks_on_the_hub_raw | davanstrien | 2023-12-17T14:26:15Z | 82 | 0 | [
"region:us"
] | null | 2023-04-03T12:41:41Z | ---
dataset_info:
features:
- name: date
dtype: date32
- name: repo_type
dtype: large_string
- name: user
dtype: large_string
- name: repo_id
dtype: large_string
- name: repo_notebook_count
dtype: int64
splits:
- name: train
num_bytes: 71202222
num_examples: 1081757
download_size: 27492507
dataset_size: 71202222
---
# Dataset Card for "notebooks_on_the_hub_raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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librarian-bots/model_cards_with_metadata | librarian-bots | 2023-12-17T02:18:22Z | 81 | 4 | [
"task_categories:text-retrieval",
"size_categories:100K<n<1M",
"ethics",
"region:us"
] | [
"text-retrieval"
] | 2023-10-12T21:50:53Z | ---
size_categories:
- 100K<n<1M
task_categories:
- text-retrieval
pretty_name: Hugging Face Hub Model Cards
dataset_info:
features:
- name: modelId
dtype: string
- name: author
dtype: string
- name: last_modified
dtype: timestamp[us, tz=UTC]
- name: downloads
dtype: int64
- name: likes
dtype: int64
- name: library_name
dtype: string
- name: tags
sequence: string
- name: pipeline_tag
dtype: string
- name: createdAt
dtype: timestamp[us, tz=UTC]
- name: card
dtype: string
splits:
- name: train
num_bytes: 722843550
num_examples: 435189
download_size: 236426037
dataset_size: 722843550
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- ethics
---
# Dataset Card for Hugging Face Hub Model Cards
This datasets consists of [model cards](https://huggingface.co/docs/hub/model-cards) for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more.
This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new).
## Dataset Details
### Dataset Description
- **Curated by:** Daniel van Strien
- **Language(s) (NLP):** Model cards on the Hugging Face Hub are predominantly in English but may include other languages.
## Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
### Out-of-Scope Use
[More Information Needed]
## Dataset Structure
This dataset has a single split.
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
### Source Data
The source data is `README.md` files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
The data is downloaded using a CRON job on a daily basis.
#### Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
### Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
#### Annotation process
N/A
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
N/A
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards.
Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
## Dataset Card Authors
[@davanstrien](https://huggingface.co/davanstrien)
## Dataset Card Contact
[@davanstrien](https://huggingface.co/davanstrien) | [
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jovianzm/Pexels-400k | jovianzm | 2023-12-17T05:29:16Z | 81 | 4 | [
"task_categories:image-to-text",
"task_categories:text-to-image",
"task_categories:text-to-video",
"task_categories:image-to-video",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] | [
"image-to-text",
"text-to-image",
"text-to-video",
"image-to-video"
] | 2023-12-05T03:35:57Z | ---
license: mit
task_categories:
- image-to-text
- text-to-image
- text-to-video
- image-to-video
language:
- en
pretty_name: Pexels-400k
size_categories:
- 100K<n<1M
---
# Pexels 400k
Dataset of 400,476 videos, their thumbnails, viewcounts, <s>explicit classification,</s> and caption.
Note: The Pexels-320k dataset in the repo is this dataset, with videos <10s removed.
# Why is NSFW crossed out?
You realized you didn't see a NFAA banner before seeing this dataset, and that being crossed out because there are no NSFW images/videos. | [
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noanabeshima/TinyStoriesV2 | noanabeshima | 2023-12-16T00:52:54Z | 80 | 0 | [
"license:cdla-sharing-1.0",
"region:us"
] | null | 2023-12-15T23:48:34Z | ---
license: cdla-sharing-1.0
configs:
- config_name: default
data_files:
- split: train
path: "TinyStoriesV2-GPT4-train.jsonl"
- split: validation
path: "TinyStoriesV2-GPT4-valid.jsonl"
---
Data is from https://huggingface.co/datasets/roneneldan/TinyStories/:
> TinyStoriesV2-GPT4-train.txt - Is a new version of the dataset that is based on generations by GPT-4 only (the original dataset also has generations by GPT-3.5 which are of lesser quality). It contains all the examples in TinyStories.txt which were GPT-4 generated as a subset (but is significantly larger).
TinyStoriesV2's train split has ~2.7M stories as opposed to TinyStories' ~2.1M.
Data is preprocessed with
```
from unidecode import unidecode
import json
def clean_story(story):
# Convert from unicode to ascii to make tokenization better; don't split up quotation marks into multiple tokens e.g.
story = unidecode(story)
# lots of spaces at beginning of documents
story = story.strip()
# lots of trailing spaces at the end of lines
story_lines = story.split('\n')
story_lines = [line.strip() for line in story_lines]
story = '\n'.join(story_lines)
# Double newline is rare in train data while single newline is common.
while '\n\n' in story:
story = story.replace('\n\n', '\n')
# Double spaces are rare
while ' ' in story:
story = story.replace(' ', ' ')
return story
for split in ['train', 'valid']:
with open(f'TinyStoriesV2-GPT4-{split}.txt', 'r') as f:
text = f.read()
# first and last split snippets seem to be broken-up stories; I drop them
stories = text.split('<|endoftext|>')[1:-1]
# artifact of encoding error, pretty rare so just drop
stories = [story for story in stories if ('â' not in story) and ('€' not in story) and ('»' not in story) and ('«' not in story)]
stories = [clean_story(story) for story in stories]
with open(f'TinyStoriesV2-GPT4-{split}.jsonl', 'w') as f:
f.write('\n'.join([json.dumps({'text': story}) for story in stories]))
```
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Hasan-Mesbaul-420/Tamil_speech | Hasan-Mesbaul-420 | 2023-12-12T01:48:23Z | 77 | 0 | [
"region:us"
] | null | 2023-11-29T07:58:13Z | ---
dataset_info:
features:
- name: path
dtype: string
- name: array
sequence: float64
- name: sampling_rate
dtype: int64
- name: Text File Path
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 2280662818
num_examples: 908
- name: test
num_bytes: 675141227
num_examples: 351
download_size: 3191937492
dataset_size: 2955804045
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
Notebook: https://www.kaggle.com/code/hasanmesbaulalitaher/tamil-voice-dataset-preparation/notebook | [
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Cnam-LMSSC/vibravox | Cnam-LMSSC | 2023-12-14T18:41:56Z | 76 | 0 | [
"task_categories:audio-to-audio",
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"task_categories:text-to-speech",
"task_ids:speaker-identification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"language:fr",
"license:cc-by-4.0",
"region:us"
] | [
"audio-to-audio",
"automatic-speech-recognition",
"audio-classification",
"text-to-speech"
] | 2023-10-18T19:15:20Z | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- fr
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- audio-to-audio
- automatic-speech-recognition
- audio-classification
- text-to-speech
task_ids:
- speaker-identification
pretty_name: VibraVox
dataset_info:
- config_name: ASR_Forehead_miniature_accelerometer
features:
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dtype: audio
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dtype: float64
- name: transcription
dtype: string
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- name: sensor_id
sequence: string
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splits:
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num_examples: 8098
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num_examples: 1233
- name: test
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num_examples: 969
download_size: 845220020
dataset_size: 8862041402.472
- config_name: ASR_In-ear_Comply_Foam_microphone
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num_examples: 1389
download_size: 8723039938
dataset_size: 12282429843.576
- config_name: ASR_In-ear_rigid_earpiece_microphone
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num_examples: 1196
download_size: 2470840983
dataset_size: 12235457854.311998
- config_name: ASR_Mouth_headworn_reference_microphone
default: true
features:
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dtype: audio
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dtype: float64
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dataset_size: 9181233887.876
- config_name: ASR_Temple_contact_microphone
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- name: audio
dtype:
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mono: false
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dtype: float64
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dtype: string
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dtype: int64
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dtype: string
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dtype: int64
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sequence: string
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dtype: int64
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download_size: 3945292131
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configs:
- config_name: ASR_Forehead_miniature_accelerometer
data_files:
- split: train
path: ASR_Forehead_miniature_accelerometer/train-*
- split: validation
path: ASR_Forehead_miniature_accelerometer/validation-*
- split: test
path: ASR_Forehead_miniature_accelerometer/test-*
- config_name: ASR_In-ear_Comply_Foam_microphone
data_files:
- split: train
path: ASR_In-ear_Comply_Foam_microphone/train-*
- split: validation
path: ASR_In-ear_Comply_Foam_microphone/validation-*
- split: test
path: ASR_In-ear_Comply_Foam_microphone/test-*
- config_name: ASR_In-ear_rigid_earpiece_microphone
data_files:
- split: train
path: ASR_In-ear_rigid_earpiece_microphone/train-*
- split: validation
path: ASR_In-ear_rigid_earpiece_microphone/validation-*
- split: test
path: ASR_In-ear_rigid_earpiece_microphone/test-*
- config_name: ASR_Mouth_headworn_reference_microphone
data_files:
- split: train
path: ASR_Mouth_headworn_reference_microphone/train-*
- split: validation
path: ASR_Mouth_headworn_reference_microphone/validation-*
- split: test
path: ASR_Mouth_headworn_reference_microphone/test-*
- config_name: ASR_Temple_contact_microphone
data_files:
- split: train
path: ASR_Temple_contact_microphone/train-*
- split: validation
path: ASR_Temple_contact_microphone/validation-*
- split: test
path: ASR_Temple_contact_microphone/test-*
- config_name: ASR_Throat_Piezoelectric_sensor
data_files:
- split: train
path: ASR_Throat_Piezoelectric_sensor/train-*
- split: validation
path: ASR_Throat_Piezoelectric_sensor/validation-*
- split: test
path: ASR_Throat_Piezoelectric_sensor/test-*
- config_name: BWE_Forehead_miniature_accelerometer
data_files:
- split: train
path: BWE_Forehead_miniature_accelerometer/train-*
- split: validation
path: BWE_Forehead_miniature_accelerometer/validation-*
- split: test
path: BWE_Forehead_miniature_accelerometer/test-*
- config_name: BWE_In-ear_Comply_Foam_microphone
data_files:
- split: train
path: BWE_In-ear_Comply_Foam_microphone/train-*
- split: validation
path: BWE_In-ear_Comply_Foam_microphone/validation-*
- split: test
path: BWE_In-ear_Comply_Foam_microphone/test-*
- config_name: BWE_In-ear_rigid_earpiece_microphone
data_files:
- split: train
path: BWE_In-ear_rigid_earpiece_microphone/train-*
- split: validation
path: BWE_In-ear_rigid_earpiece_microphone/validation-*
- split: test
path: BWE_In-ear_rigid_earpiece_microphone/test-*
- config_name: BWE_Temple_contact_microphone
data_files:
- split: train
path: BWE_Temple_contact_microphone/train-*
- split: validation
path: BWE_Temple_contact_microphone/validation-*
- split: test
path: BWE_Temple_contact_microphone/test-*
- config_name: BWE_Throat_Piezoelectric_sensor
data_files:
- split: train
path: BWE_Throat_Piezoelectric_sensor/train-*
- split: validation
path: BWE_Throat_Piezoelectric_sensor/validation-*
- split: test
path: BWE_Throat_Piezoelectric_sensor/test-*
- config_name: BWE_all_sensors
data_files:
- split: train
path: BWE_all_sensors/train-*
- split: validation
path: BWE_all_sensors/validation-*
- split: test
path: BWE_all_sensors/test-*
---
# Dataset Card for VibraVox
### Dataset Summary
The [VibraVox dataset](https://vibravox.cnam.fr) is a general purpose dataset of french speech captured with body-conduction transducers.
This dataset can be used for various audio machine learning tasks : **Automatic Speech Recognition (ASR)** (Speech-to-Text , Speech-to-Phoneme), **Audio Bandwidth Extension**, **Speaker identification** / recognition , **Voice cloning** , etc ...
<!--This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).-->
## Dataset Details
### Dataset Description
VibraVox ([vibʁavɔks]) is a GDPR-compliant dataset scheduled for release in early 2024. It includes speech recorded simultaneously using multiple body-conducted microphones :
- two in-ear microphones
- a bone conduction microphones
- a throat piezoelectric transducer
- a forehead miniature accelerometer
- a headset reference microphone located in front of the mouth.
Those sensors are described and documented in [the dataset creation](#dataset-creation) section.
The VibraVox speech corpus has been recorded with 200 participants under various acoustic conditions imposed by a 5th order ambisonics spatialization sphere.
VibraVox aims at serving as a valuable resource for advancing the field of body-conducted speech analysis and facilitating the development of robust communication systems for real-world applications.
Unlike traditional microphones, which rely on airborne sound waves, body-conduction microphones capture speech signals directly from the body, offering advantages in noisy environments by eliminating the capture of ambient noise. Although body-conduction microphones have been available for decades, their limited bandwidth has restricted their widespread usage. However, thanks to two tracks of improvements, this may be the awakening of this technology to a wide public for speech capture and communication in noisy environments.
### Links and details :
- **Homepage:** [https://vibravox.cnam.fr](https://vibravox.cnam.fr)
- **Point of Contact:** [Eric Bavu](https://acoustique.cnam.fr/contacts/bavu/en/#contact)
- **Curated by:** AVA Team of the [LMSSC Research Laboratory](https://lmssc.cnam.fr)
- **Funded by:** French [Agence Nationale Pour la Recherche / AHEAD Project](https://anr.fr/en/funded-projects-and-impact/funded-projects/project/funded/project/b2d9d3668f92a3b9fbbf7866072501ef-5aac4914c7/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=fa352121b44b60bf6a5917180d5205e6)
- **Language:** French
- **License:** Creative Commons Attributions 4.0
<!--- **Repository:** [Needs More Information]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
- **Leaderboard:** [Needs More Information] -->
<!---- **Shared by [optional]:** [More Information Needed] -->
### Example usage
VibraVox contains labelled data for 11 configurations tailored for specific tasks, mainly oriented towards **Automatic Speech Recognition** and **Bandwidth Extension**.
#### ASR Datasets
The ASR dataset configurations contain mono audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 6 different kinds of microphones.
Recording was carried out simultaneously on all 6 sensors. The audio files were sampled at 48 kHz and encoded as .wav PCM32 files.
To load a specific configuration simply use the following command :
```python
from datasets import load_dataset
config_name = "ASR_Mouth_headworn_reference_microphone"
vibravox_asr = load_dataset("Cnam-LMSSC/vibravox", config_name)
```
```config_name``` can be any of the following :
``` "ASR_Mouth_headworn_reference_microphone" ``` (full bandwidth microphone),
``` "ASR_In-ear_Comply_Foam_microphone" ``` (body conduction) ,
``` "ASR_In-ear_rigid_earpiece_microphone" ``` (body conduction) ,
``` "ASR_Forehead_miniature_accelerometer" ``` (body-conduction, high frequency response),
``` "ASR_Temple_contact_microphone" ``` (body-conduction, low SNR) ,
``` "ASR_Throat_Piezoelectric_sensor" ``` (body-conduction, high distorsion rate).
To load all the sensors in a single dataset, use "ASR_all" config name **(Not available at this time)** :
```python
vibravox_asr_all = load_dataset("Cnam-LMSSC/vibravox", "ASR_all")
```
#### BWE Datasets
The BWE dataset configurations contain stereo audio along with corresponding text transcriptions (cased and with punctuation) for french speech, using 5 different kinds of body conduction microphones (first channel), and a standard reference microphone (second channel).
Recording was carried out simultaneously on all 6 sensors, therefore the 5 BWE configurations only differ on the band-limited sensor used, and the reference microphone channel is the same as the data found in the ``` "ASR_Mouth_headworn_reference_microphone" ``` configuration.
The audio files are sampled at 48 kHz and encoded as .wav PCM32 files.
To load a specific configuration simply use the following command :
```python
from datasets import load_dataset
config_name = "BWE_In-ear_Comply_Foam_microphone"
vibravox_bwe = load_dataset("Cnam-LMSSC/vibravox", config_name)
```
```config_name``` can be any of the following :
``` "BWE_In-ear_Comply_Foam_microphone" ``` (body conduction) ,
``` "BWE_In-ear_rigid_earpiece_microphone" ``` (body conduction) ,
``` "BWE_Forehead_miniature_accelerometer" ``` (body-conduction, high frequency response),
``` "BWE_Temple_contact_microphone" ``` (body-conduction, low SNR) ,
``` "BWE_Throat_Piezoelectric_sensor" ``` (body-conduction, high distorsion rate).
To load all the sensors in a single dataset (intersection of validated audio for each sensor), use "BWE_all" config name, which contains 7-channel audios **(Not available at this time)** :
```python
vibravox_bwe_all = load_dataset("Cnam-LMSSC/vibravox", "BWE_all")
```
### Supported Tasks and Leaderboards
* automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).
* bandwidth-extension : **TODO : explain**
### Data statistics
VibraVox contains labelled (transcribed) data for 6 sensors :
| Sensor | Code | Transcribed Hours | Transcribed Speakers | Transcribed Tokens |
|:---:|:---:|:---:|:---:|:---:|
<!--| English | En | 543 | 1313 | 4.8M |
| German | De | 282 | 531 | 2.3M |
| French | Fr | 211 | 534 | 2.1M |
| Spanish | Es | 166 | 305 | 1.6M |
| Polish | Pl | 111 | 282 | 802K |
| Italian | It | 91 | 306 | 757K |
| Romanian | Ro | 89 | 164 | 739K |
| Hungarian | Hu | 63 | 143 | 431K |
| Czech | Cs | 62 | 138 | 461K |
| Dutch | Nl | 53 | 221 | 488K |
| Finnish | Fi | 27 | 84 | 160K |
| Croatian | Hr | 43 | 83 | 337K |
| Slovak | Sk | 35 | 96 | 270K |
| Slovene | Sl | 10 | 45 | 76K |
| Estonian | Et | 3 | 29 | 18K |
| Lithuanian | Lt | 2 | 21 | 10K |
| Total | | 1791 | 4295 | 15M | -->
Noise data :
| Sensor | Code | Recorded Hours | Individuals |
|:---:|:---:|:---:|:---:|
<!--| Dutch | en_nl | 3.52 | 45 |
| German | en_de | 3.52 | 84 |
| Czech | en_cs | 3.30 | 26 |
| Polish | en_pl | 3.23 | 33 |
| French | en_fr | 2.56 | 27 |
| Hungarian | en_hu | 2.33 | 23 |
| Finnish | en_fi | 2.18 | 20 |
| Romanian | en_ro | 1.85 | 27 |
| Slovak | en_sk | 1.46 | 17 |
| Spanish | en_es | 1.42 | 18 |
| Italian | en_it | 1.11 | 15 |
| Estonian | en_et | 1.08 | 6 |
| Lithuanian | en_lt | 0.65 | 7 |
| Croatian | en_hr | 0.42 | 9 |
| Slovene | en_sl | 0.25 | 7 | -->
## Dataset Structure
### Data Instances
```python
{
'audio': {
'path': '/home/polina/.cache/huggingface/datasets/downloads/extracted/44aedc80bb053f67f957a5f68e23509e9b181cc9e30c8030f110daaedf9c510e/SiS_00005683_inearde.wav',
'array': array([-3.89814377e-05, 7.23600388e-05, 5.14984131e-05, ..., 8.02278519e-05, 5.85317612e-05, 1.22070312e-04], dtype=float32),
'sampling_rate': 48000
},
'audio_length': 3.86,
'transcription': 'Le bureau international du scoutisme est créé à Londres.',
'sentence_id': '00005683',
'num_channels': 1,
'sensor_id': ['In-ear rigid earpiece microphone'],
'speaker_id': '053',
'gender': 'Female',
'type': 'clean',
'split': 'train'
}
```
### Data Fields
* `audio` (datasets.Audio) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
* `audio_length` (float32) - the audio length in seconds.
* `transcription` (string) - audio segment text (cased and with punctuation preserved)
* `sentence_id` (string) - id of the pronounced sentence
* `num_channels` (int) - the number of audio channels in audio
* `sensor_id` (string) - a list of sensors used in this audio, ordered by channel number
* `speaker_id` (string) - id of speaker
* `gender` (string) - gender of speaker
* `type` (string) - type of excertp, for example "clean", if no background noise, else "other". **CHANGE THIS FOR VERSION 1.2.0**
* `split`(string) - split (can be "train", "val", or "test")
### Data Splits
Almost all configs contain data in three splits: train, validation and test.
Noise-only configs `QiS` and `QiN` configurations (intended for except for data augmentation) contain only a train split.
Speech in noise config `SiN` configuration (intended for extreme tests) contains only a test split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The text data is collected from the french Wikipedia **(CITE SOURCE FILE IN COMMONVOICE)**
#### Initial Data Collection and Normalization
<!--The VoxPopuli transcribed set comes from aligning the full-event source speech audio with the transcripts for plenary sessions. Official timestamps
are available for locating speeches by speaker in the full session, but they are frequently inaccurate, resulting in truncation of the speech or mixture
of fragments from the preceding or the succeeding speeches. To calibrate the original timestamps,
we perform speaker diarization (SD) on the full-session audio using pyannote.audio (Bredin et al.2020) and adopt the nearest SD timestamps (by L1 distance to the original ones) instead for segmentation.
Full-session audios are segmented into speech paragraphs by speaker, each of which has a transcript available.
The speech paragraphs have an average duration of 197 seconds, which leads to significant. We hence further segment these paragraphs into utterances with a
maximum duration of 20 seconds. We leverage speech recognition (ASR) systems to force-align speech paragraphs to the given transcripts.
The ASR systems are TDS models (Hannun et al., 2019) trained with ASG criterion (Collobert et al., 2016) on audio tracks from in-house deidentified video data.
The resulting utterance segments may have incorrect transcriptions due to incomplete raw transcripts or inaccurate ASR force-alignment.
We use the predictions from the same ASR systems as references and filter the candidate segments by a maximum threshold of 20% character error rate(CER).-->
#### Who are the source language producers?
Speakers are **TODO : explain**
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
| **Sensor** | **Image** | **Transducer** | **Online documentation** |
|:---------------------------|:---------------------|:-------------|:----------------------------------------------------------------------------------------------------------------------|
| Reference microphone (headset) |  | Shure WH20 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/airborne/index.html) |
| In-ear microphone Type 1 (Comply Foam) |  | STMicroelectronics MP34DT01 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/soft_inear/index.html) |
| In-ear microphone Type 2 (rigid earpiece) |  | Knowles SPH1642HT5H | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/rigid_inear/index.html) |
| Forehead (miniature accelerometer) | | Knowles BU23173-000 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/forehead/index.html) |
| Temple contact microphone |  | AKG C411 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/temple/index.html) |
| Larynx piezoelectric sensor |  | iXRadio XVTM822D-D35 | [See documentation on vibravox.cnam.fr](https://vibravox.cnam.fr/documentation/hardware/microphones/throat/index.html) |
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
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ArmelRandy/kde4 | ArmelRandy | 2023-12-11T08:49:25Z | 76 | 0 | [
"region:us"
] | null | 2023-12-11T08:49:23Z | ---
dataset_info:
features:
- name: en
dtype: string
- name: fr
dtype: string
splits:
- name: train
num_bytes: 10583004
num_examples: 20058
download_size: 5304712
dataset_size: 10583004
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "kde4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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StarkWizard/cairo-instruct | StarkWizard | 2023-12-14T16:15:32Z | 76 | 0 | [
"region:us"
] | null | 2023-12-14T16:15:28Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1045332
num_examples: 5035
- name: eval
num_bytes: 821
num_examples: 5
download_size: 419636
dataset_size: 1046153
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
---
# Dataset Card for "cairo-instruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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csebuetnlp/CrossSum | csebuetnlp | 2023-12-16T13:27:47Z | 74 | 7 | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:found",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:am",
"language:ar",
"language:az",
"language:bn",
"language:my",
"language:zh",
"language:en",
"language:fr",
"language:gu",
"language:ha",
"language:hi",
"language:ig",
"language:id",
"language:ja",
"language:rn",
"language:ko",
"language:ky",
"language:mr",
"language:ne",
"language:om",
"language:ps",
"language:fa",
"language:pcm",
"language:pt",
"language:pa",
"language:ru",
"language:gd",
"language:sr",
"language:si",
"language:so",
"language:es",
"language:sw",
"language:ta",
"language:te",
"language:th",
"language:ti",
"language:tr",
"language:uk",
"language:ur",
"language:uz",
"language:vi",
"language:cy",
"language:yo",
"license:cc-by-nc-sa-4.0",
"arxiv:2112.08804",
"region:us"
] | [
"summarization"
] | 2022-04-20T08:27:10Z | ---
task_categories:
- summarization
task_ids:
- news-articles-summarization
language:
- am
- ar
- az
- bn
- my
- zh
- en
- fr
- gu
- ha
- hi
- ig
- id
- ja
- rn
- ko
- ky
- mr
- ne
- om
- ps
- fa
- pcm
- pt
- pa
- ru
- gd
- sr
- si
- so
- es
- sw
- ta
- te
- th
- ti
- tr
- uk
- ur
- uz
- vi
- cy
- yo
size_categories:
- 1M<n<10M
license:
- cc-by-nc-sa-4.0
multilinguality:
- multilingual
source_datasets:
- original
annotations_creators:
- found
language_creators:
- found
pretty_name: CrossSum
---
# Dataset Card for "CrossSum"
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [https://github.com/csebuetnlp/CrossSum](https://github.com/csebuetnlp/CrossSum)
- **Paper:** [CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs](https://arxiv.org/abs/2112.08804)
- **Point of Contact:** [Tahmid Hasan](mailto:[email protected])
### Dataset Summary
We present CrossSum, a large-scale dataset
comprising 1.70 million cross-lingual article summary samples in 1500+ language-pairs
constituting 45 languages. We use the multilingual XL-Sum dataset and align identical
articles written in different languages via crosslingual retrieval using a language-agnostic
representation model.
### Supported Tasks and Leaderboards
[More information needed](https://github.com/csebuetnlp/CrossSum)
### Languages
- `amharic`
- `arabic`
- `azerbaijani`
- `bengali`
- `burmese`
- `chinese_simplified`
- `chinese_traditional`
- `english`
- `french`
- `gujarati`
- `hausa`
- `hindi`
- `igbo`
- `indonesian`
- `japanese`
- `kirundi`
- `korean`
- `kyrgyz`
- `marathi`
- `nepali`
- `oromo`
- `pashto`
- `persian`
- `pidgin`
- `portuguese`
- `punjabi`
- `russian`
- `scottish_gaelic`
- `serbian_cyrillic`
- `serbian_latin`
- `sinhala`
- `somali`
- `spanish`
- `swahili`
- `tamil`
- `telugu`
- `thai`
- `tigrinya`
- `turkish`
- `ukrainian`
- `urdu`
- `uzbek`
- `vietnamese`
- `welsh`
- `yoruba`
## Loading the dataset
```python
from datasets import load_dataset
# for available language names, see above
src_lang = "english"
tgt_lang = "bengali"
ds = load_dataset(f"csebuetnlp/CrossSum", "{}-{}".format(src_lang, tgt_lang))
```
## Dataset Structure
### Data Instances
One example from the `English` dataset is given below in JSON format.
```
{
"source_url": "https://www.bbc.com/japanese/53074000",
"target_url": "https://www.bbc.com/bengali/news-53064712",
"summary": "বিজ্ঞানীরা বলছেন ডেক্সামেথাসোন নামে সস্তা ও সহজলভ্য একটি ওষুধ করোনাভাইরাসে গুরুতর অসুস্থ রোগীদের জীবন রক্ষা করতে সাহায্য করবে।",
"text": "ミシェル･ロバーツ、BBCニュースオンライン健康担当編集長 英オックスフォード大学の研究チームによると、低用量のデキサメタゾンは新型ウイルスとの戦いで画期的な突破口になる。 新型コロナウイルスに対し、様々な既存の治療法の効果を試す世界的規模の臨床試験の一貫として、デキサメタゾンが試された。 その結果、人工呼吸器を必要とする重症患者の致死率が3割下がり、酸素供給を必要とする患者の場合は2割下がった。 新型ウイルスのパンデミック（世界的流行）の初期からイギリスでデキサメタゾンを治療に使用していた場合、最大5000人の命が救えたはずだと研究者たちは言う。 さらに、新型コロナウイルスによる感染症「COVID-19」の患者が多く出ている貧しい国にとっても、安価なデキサメタゾンを使う治療は大いに役立つと期待される。 重症者の致死率が大幅に下がる イギリス政府は20万人分の投与量を備蓄しており、国民医療制度の国民保健サービス（NHS）で患者への使用を開始する方針を示した。 ボリス･ジョンソン英首相は「イギリス科学界の素晴らしい成果」を歓迎し、「たとえ感染の第2波が来ても備蓄が足りるよう、数を確保するための措置をとった」と述べた。 イングランド首席医務官クリス・ウィッティー教授は、「COVID-19にとってこれまでで一番重要な臨床試験結果だ。手に入りやすく安全でなじみのある薬によって、酸素供給や人工呼吸器が必要な人の致死率が大幅に下がった。（中略）この発見が世界中で人命を救う」と評価した。 ＜関連記事＞ 新型コロナウイルスに20人が感染した場合、19人は入院しないまま回復する。入院する人もほとんどは回復するものの、重症化して酸素供給や人工呼吸器を必要とする人もいる。 デキサメタゾンはこうした重症患者の治療に効果があるもよう。 新型ウイルスに感染した患者の体内では、ウイルスと戦う免疫系が暴走することがある。その免疫系の過剰反応による体の損傷を、デキサメタゾンが緩和するものとみられる。 「サイトカイン・ストーム」と呼ばれる免疫系の過剰反応が、患者の命を奪うこともある。 デキサメタゾンはすでに抗炎症剤として、ぜんそくや皮膚炎など様々な症状の治療に使われている。 初めて致死率を下げる薬 オックスフォード大学が主導する臨床試験は、約2000人の入院患者にデキサメタゾンを投与。それ以外の4000人以上の患者と容体を比較した。 人工呼吸器を使用する患者については、死亡リスクが40％から28％に下がった。 酸素供給する患者は、死亡リスクが25％から20％に下がった。 研究チームのピーター・ホービー教授は、「今のところ、致死率を実際に下げる結果が出たのは、この薬だけだ。しかも、致死率をかなり下げる。画期的な突破口だ」と話した。 研究を主導するマーティン・ランドレイ教授によると、人工呼吸器を使う患者の8人に1人、ならびに酸素供給治療を受ける患者の20-25人に1人が、デキサメタゾンで救えることが分かったという。 「これはきわめて明確なメリットだ」と教授は言う。 「最大10日間、デキサメタゾンを投与するという治療法で、費用は患者1人あたり1日約5ポンド（約670円）。つまり、35ポンド（約4700円）で人ひとりの命が救える」 「しかもこれは、世界中で手に入る薬だ」 状況が許す限り、新型コロナウイルスで入院中の患者にはただちに投与を開始すべきだと、ランドレイ教授は促した。 ただし、自宅で自己治療するために薬局に買いに行くべきではないと言う。 デキサメタゾンは、呼吸補助を必要としない軽症の患者には効果がないもよう。 3月に始動した新型コロナウイルス治療薬の無作為化臨床試験「リカバリー・トライアル」は、抗マラリア薬「ヒドロキシクロロキン」も調べたものの、心臓疾患や致死率の悪化につながるという懸念から、ヒドロキシクロロキンについては試験を中止した。 一方で、感染者の回復にかかる時間を短縮するとみられるレムデシビルは、すでにNHSの保険対象になり治療現場で使われている。 ＜解説＞ ファーガス・ウォルシュBBC健康担当編集委員 COVID-19の死者を減らすと初めて立証された薬は、高価な新しい薬ではなく、古くからずっと使われてきた、きわめて安いステロイド剤だった。 世界中の患者が直ちにその恩恵を受けることになるので、これは歓迎すべき発見だ。 この臨床試験の最新成果がこれほど急いで発表されたのは、そのためだ。とてつもない影響を世界中にもたらすので。 デキサメタゾンは1960年代初めから、関節リウマチやぜんそくなど、幅広い症状の治療に使われてきた。 これまでは、人工呼吸器を必要とするCOVID-19患者の半数が亡くなってきた。その致死率を3割減らすというのは、絶大な効果だ。 集中治療室では点滴で投与する。もう少し軽症な患者には、錠剤で与える。 これまでのところ、COVID-19患者に効果があると証明された薬は、エボラ治療薬のレムデシビルだけだった。 レムデシビルは症状の回復期間を15日から11日に短縮する。しかし、致死率を下げると言えるだけの証拠は出ていなかった。 デキサメタゾンと異なり、レムデシビルは数の少ない新薬で、薬価もまだ公表されていない。"
}
```
### Data Fields
- 'source_url': A string representing the source article URL.
- 'target_url': A string representing the target article URL.
- 'summary': A string containing the article summary.
- 'text' : A string containing the article text.
### Data Splits
No. of total examples for each language pair are as follows:
Language (ISO 639-1-Code) | am | ar | az | bn | my | zh-CN | zh-TW | en | fr | gu | ha | hi | ig | id | ja | rn | ko | ky | mr | np | om | ps | fa | pcm | pt | pa | ru | gd | sr | sr | si | so | es | sw | ta | te | th | ti | tr | uk | ur | uz | vi | cy | yo
----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | -----
am | -- | 667 | 100 | 272 | 95 | 179 | 167 | 1456 | 358 | 173 | 221 | 377 | 26 | 494 | 264 | 423 | 244 | 92 | 221 | 301 | 21 | 192 | 431 | 209 | 307 | 189 | 347 | 0 | 357 | 365 | 62 | 309 | 351 | 378 | 390 | 329 | 124 | 131 | 435 | 345 | 409 | 41 | 285 | 1 | 67
ar | 667 | -- | 787 | 804 | 652 | 2968 | 2843 | 9653 | 989 | 475 | 747 | 3665 | 86 | 6084 | 1188 | 876 | 707 | 299 | 559 | 854 | 9 | 2161 | 4186 | 436 | 2539 | 547 | 5564 | 1 | 1109 | 1145 | 315 | 1049 | 3654 | 1186 | 1311 | 877 | 367 | 27 | 4147 | 3457 | 4935 | 388 | 2666 | 38 | 141
az | 100 | 787 | -- | 277 | 84 | 371 | 334 | 1317 | 208 | 192 | 126 | 748 | 28 | 1111 | 231 | 188 | 155 | 221 | 194 | 242 | 1 | 252 | 817 | 91 | 678 | 190 | 2238 | 4 | 289 | 283 | 124 | 367 | 704 | 539 | 515 | 245 | 140 | 2 | 1495 | 1383 | 966 | 199 | 725 | 30 | 42
bn | 272 | 804 | 277 | -- | 139 | 318 | 284 | 1549 | 317 | 559 | 231 | 1396 | 35 | 1076 | 342 | 298 | 352 | 154 | 586 | 668 | 2 | 300 | 790 | 135 | 764 | 580 | 838 | 0 | 562 | 564 | 151 | 412 | 701 | 471 | 919 | 793 | 245 | 6 | 860 | 688 | 1382 | 98 | 527 | 37 | 61
my | 95 | 652 | 84 | 139 | -- | 356 | 314 | 685 | 90 | 96 | 74 | 528 | 12 | 761 | 144 | 100 | 112 | 58 | 89 | 152 | 1 | 234 | 426 | 39 | 230 | 86 | 535 | 0 | 115 | 123 | 87 | 79 | 431 | 86 | 185 | 147 | 71 | 4 | 449 | 350 | 591 | 62 | 447 | 4 | 12
zh-CN | 179 | 2968 | 371 | 318 | 356 | -- | 47101 | 4975 | 348 | 201 | 159 | 1379 | 38 | 2851 | 1017 | 240 | 412 | 139 | 240 | 275 | 14 | 559 | 1111 | 149 | 1371 | 250 | 2572 | 2 | 504 | 530 | 166 | 323 | 2002 | 412 | 511 | 353 | 269 | 11 | 1511 | 1619 | 1651 | 176 | 1858 | 33 | 39
zh-TW | 167 | 2843 | 334 | 284 | 314 | 47101 | -- | 4884 | 331 | 174 | 150 | 1213 | 35 | 2588 | 953 | 209 | 382 | 131 | 213 | 252 | 16 | 501 | 967 | 141 | 1271 | 226 | 2286 | 1 | 453 | 494 | 150 | 302 | 1873 | 383 | 465 | 335 | 250 | 12 | 1294 | 1464 | 1444 | 158 | 1663 | 31 | 38
en | 1456 | 9653 | 1317 | 1549 | 685 | 4975 | 4884 | -- | 1889 | 978 | 913 | 4728 | 144 | 10040 | 3040 | 1878 | 1673 | 490 | 1181 | 1614 | 38 | 1522 | 4680 | 1074 | 4744 | 1330 | 9080 | 128 | 3760 | 3809 | 532 | 2141 | 6910 | 2701 | 3156 | 2121 | 1020 | 58 | 5676 | 6562 | 6320 | 450 | 4574 | 2655 | 229
fr | 358 | 989 | 208 | 317 | 90 | 348 | 331 | 1889 | -- | 242 | 477 | 616 | 106 | 1018 | 274 | 735 | 264 | 124 | 241 | 323 | 4 | 196 | 602 | 439 | 921 | 247 | 849 | 2 | 555 | 569 | 98 | 502 | 990 | 872 | 425 | 380 | 185 | 10 | 829 | 721 | 766 | 76 | 438 | 40 | 159
gu | 173 | 475 | 192 | 559 | 96 | 201 | 174 | 978 | 242 | -- | 147 | 5170 | 34 | 710 | 228 | 183 | 268 | 106 | 2091 | 561 | 1 | 246 | 522 | 101 | 529 | 2210 | 582 | 0 | 331 | 345 | 125 | 261 | 540 | 300 | 1762 | 2066 | 164 | 5 | 631 | 508 | 1619 | 80 | 450 | 21 | 54
ha | 221 | 747 | 126 | 231 | 74 | 159 | 150 | 913 | 477 | 147 | -- | 460 | 202 | 901 | 157 | 485 | 135 | 61 | 159 | 239 | 5 | 229 | 487 | 529 | 375 | 157 | 525 | 1 | 258 | 258 | 49 | 391 | 463 | 568 | 299 | 260 | 87 | 9 | 519 | 400 | 526 | 59 | 352 | 30 | 362
hi | 377 | 3665 | 748 | 1396 | 528 | 1379 | 1213 | 4728 | 616 | 5170 | 460 | -- | 65 | 5627 | 623 | 489 | 520 | 234 | 3831 | 1357 | 4 | 1519 | 5351 | 192 | 6563 | 4052 | 4622 | 1 | 809 | 807 | 449 | 747 | 2931 | 893 | 3711 | 3762 | 378 | 7 | 3694 | 3935 | 15666 | 352 | 3738 | 77 | 79
ig | 26 | 86 | 28 | 35 | 12 | 38 | 35 | 144 | 106 | 34 | 202 | 65 | -- | 113 | 24 | 107 | 32 | 16 | 51 | 36 | 3 | 11 | 49 | 255 | 61 | 39 | 79 | 0 | 51 | 51 | 13 | 77 | 91 | 151 | 52 | 54 | 18 | 5 | 91 | 83 | 61 | 15 | 65 | 6 | 296
id | 494 | 6084 | 1111 | 1076 | 761 | 2851 | 2588 | 10040 | 1018 | 710 | 901 | 5627 | 113 | -- | 1274 | 994 | 774 | 347 | 745 | 1104 | 8 | 1430 | 3892 | 367 | 4409 | 725 | 7588 | 7 | 1387 | 1379 | 470 | 1312 | 4547 | 1873 | 1886 | 1131 | 599 | 9 | 5663 | 4829 | 6476 | 432 | 4810 | 145 | 174
ja | 264 | 1188 | 231 | 342 | 144 | 1017 | 953 | 3040 | 274 | 228 | 157 | 623 | 24 | 1274 | -- | 372 | 654 | 140 | 302 | 424 | 2 | 266 | 1014 | 152 | 706 | 269 | 1517 | 2 | 550 | 571 | 109 | 387 | 950 | 425 | 641 | 425 | 305 | 5 | 1242 | 1013 | 797 | 49 | 908 | 25 | 33
rn | 423 | 876 | 188 | 298 | 100 | 240 | 209 | 1878 | 735 | 183 | 485 | 489 | 107 | 994 | 372 | -- | 283 | 106 | 242 | 369 | 18 | 228 | 684 | 398 | 526 | 206 | 711 | 0 | 443 | 450 | 77 | 584 | 607 | 1186 | 521 | 363 | 149 | 13 | 724 | 610 | 617 | 59 | 631 | 20 | 180
ko | 244 | 707 | 155 | 352 | 112 | 412 | 382 | 1673 | 264 | 268 | 135 | 520 | 32 | 774 | 654 | 283 | -- | 99 | 319 | 445 | 1 | 150 | 596 | 130 | 587 | 264 | 649 | 0 | 522 | 543 | 81 | 234 | 613 | 324 | 541 | 452 | 197 | 5 | 680 | 616 | 532 | 54 | 530 | 12 | 45
ky | 92 | 299 | 221 | 154 | 58 | 139 | 131 | 490 | 124 | 106 | 61 | 234 | 16 | 347 | 140 | 106 | 99 | -- | 107 | 167 | 4 | 102 | 252 | 59 | 251 | 118 | 1013 | 1 | 206 | 211 | 45 | 145 | 279 | 150 | 206 | 174 | 109 | 3 | 346 | 508 | 270 | 113 | 201 | 12 | 23
mr | 221 | 559 | 194 | 586 | 89 | 240 | 213 | 1181 | 241 | 2091 | 159 | 3831 | 51 | 745 | 302 | 242 | 319 | 107 | -- | 630 | 1 | 232 | 608 | 138 | 524 | 1797 | 675 | 0 | 419 | 436 | 129 | 270 | 603 | 332 | 1776 | 1886 | 196 | 11 | 706 | 596 | 1395 | 79 | 473 | 16 | 48
np | 301 | 854 | 242 | 668 | 152 | 275 | 252 | 1614 | 323 | 561 | 239 | 1357 | 36 | 1104 | 424 | 369 | 445 | 167 | 630 | -- | 1 | 303 | 916 | 134 | 706 | 545 | 849 | 2 | 553 | 538 | 164 | 420 | 687 | 513 | 994 | 741 | 217 | 7 | 930 | 741 | 1156 | 84 | 719 | 39 | 65
om | 21 | 9 | 1 | 2 | 1 | 14 | 16 | 38 | 4 | 1 | 5 | 4 | 3 | 8 | 2 | 18 | 1 | 4 | 1 | 1 | -- | 2 | 3 | 11 | 4 | 6 | 8 | 0 | 2 | 3 | 0 | 6 | 7 | 5 | 2 | 2 | 1 | 103 | 5 | 10 | 1 | 4 | 2 | 0 | 7
ps | 192 | 2161 | 252 | 300 | 234 | 559 | 501 | 1522 | 196 | 246 | 229 | 1519 | 11 | 1430 | 266 | 228 | 150 | 102 | 232 | 303 | 2 | -- | 2815 | 94 | 594 | 249 | 1246 | 0 | 235 | 242 | 156 | 304 | 766 | 314 | 441 | 314 | 92 | 8 | 1049 | 818 | 2833 | 156 | 657 | 7 | 32
fa | 431 | 4186 | 817 | 790 | 426 | 1111 | 967 | 4680 | 602 | 522 | 487 | 5351 | 49 | 3892 | 1014 | 684 | 596 | 252 | 608 | 916 | 3 | 2815 | -- | 186 | 5512 | 541 | 4328 | 0 | 1028 | 1023 | 276 | 812 | 2512 | 1002 | 1250 | 797 | 364 | 8 | 3695 | 3567 | 6752 | 313 | 3190 | 66 | 74
pcm | 209 | 436 | 91 | 135 | 39 | 149 | 141 | 1074 | 439 | 101 | 529 | 192 | 255 | 367 | 152 | 398 | 130 | 59 | 138 | 134 | 11 | 94 | 186 | -- | 227 | 112 | 322 | 0 | 234 | 246 | 28 | 219 | 314 | 436 | 232 | 162 | 85 | 28 | 287 | 280 | 232 | 18 | 170 | 9 | 462
pt | 307 | 2539 | 678 | 764 | 230 | 1371 | 1271 | 4744 | 921 | 529 | 375 | 6563 | 61 | 4409 | 706 | 526 | 587 | 251 | 524 | 706 | 4 | 594 | 5512 | 227 | -- | 579 | 4452 | 7 | 1371 | 1341 | 231 | 602 | 7112 | 983 | 1042 | 820 | 468 | 3 | 3483 | 4421 | 6759 | 186 | 3754 | 110 | 97
pa | 189 | 547 | 190 | 580 | 86 | 250 | 226 | 1330 | 247 | 2210 | 157 | 4052 | 39 | 725 | 269 | 206 | 264 | 118 | 1797 | 545 | 6 | 249 | 541 | 112 | 579 | -- | 629 | 0 | 410 | 404 | 128 | 283 | 585 | 357 | 1726 | 1892 | 200 | 10 | 643 | 570 | 1515 | 73 | 431 | 16 | 44
ru | 347 | 5564 | 2238 | 838 | 535 | 2572 | 2286 | 9080 | 849 | 582 | 525 | 4622 | 79 | 7588 | 1517 | 711 | 649 | 1013 | 675 | 849 | 8 | 1246 | 4328 | 322 | 4452 | 629 | -- | 5 | 1495 | 1460 | 373 | 1166 | 4864 | 1672 | 1628 | 892 | 595 | 7 | 6223 | 22241 | 5309 | 809 | 3963 | 134 | 125
gd | 0 | 1 | 4 | 0 | 0 | 2 | 1 | 128 | 2 | 0 | 1 | 1 | 0 | 7 | 2 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 7 | 0 | 5 | -- | 2 | 3 | 2 | 1 | 3 | 1 | 0 | 0 | 1 | 0 | 6 | 5 | 2 | 1 | 3 | 36 | 2
sr | 357 | 1109 | 289 | 562 | 115 | 504 | 453 | 3760 | 555 | 331 | 258 | 809 | 51 | 1387 | 550 | 443 | 522 | 206 | 419 | 553 | 2 | 235 | 1028 | 234 | 1371 | 410 | 1495 | 2 | -- | 9041 | 127 | 377 | 1235 | 574 | 761 | 691 | 340 | 6 | 1247 | 1512 | 1021 | 109 | 685 | 42 | 69
sr | 365 | 1145 | 283 | 564 | 123 | 530 | 494 | 3809 | 569 | 345 | 258 | 807 | 51 | 1379 | 571 | 450 | 543 | 211 | 436 | 538 | 3 | 242 | 1023 | 246 | 1341 | 404 | 1460 | 3 | 9041 | -- | 137 | 382 | 1260 | 568 | 775 | 699 | 347 | 10 | 1229 | 1498 | 1009 | 112 | 639 | 45 | 79
si | 62 | 315 | 124 | 151 | 87 | 166 | 150 | 532 | 98 | 125 | 49 | 449 | 13 | 470 | 109 | 77 | 81 | 45 | 129 | 164 | 0 | 156 | 276 | 28 | 231 | 128 | 373 | 2 | 127 | 137 | -- | 137 | 260 | 189 | 348 | 173 | 69 | 7 | 301 | 306 | 510 | 38 | 216 | 5 | 15
so | 309 | 1049 | 367 | 412 | 79 | 323 | 302 | 2141 | 502 | 261 | 391 | 747 | 77 | 1312 | 387 | 584 | 234 | 145 | 270 | 420 | 6 | 304 | 812 | 219 | 602 | 283 | 1166 | 1 | 377 | 382 | 137 | -- | 689 | 1020 | 723 | 384 | 178 | 19 | 968 | 875 | 1000 | 75 | 724 | 20 | 116
es | 351 | 3654 | 704 | 701 | 431 | 2002 | 1873 | 6910 | 990 | 540 | 463 | 2931 | 91 | 4547 | 950 | 607 | 613 | 279 | 603 | 687 | 7 | 766 | 2512 | 314 | 7112 | 585 | 4864 | 3 | 1235 | 1260 | 260 | 689 | -- | 1047 | 1073 | 827 | 469 | 10 | 3645 | 3130 | 3060 | 290 | 2330 | 59 | 133
sw | 378 | 1186 | 539 | 471 | 86 | 412 | 383 | 2701 | 872 | 300 | 568 | 893 | 151 | 1873 | 425 | 1186 | 324 | 150 | 332 | 513 | 5 | 314 | 1002 | 436 | 983 | 357 | 1672 | 1 | 574 | 568 | 189 | 1020 | 1047 | -- | 929 | 492 | 261 | 10 | 1348 | 1309 | 1253 | 90 | 936 | 37 | 219
ta | 390 | 1311 | 515 | 919 | 185 | 511 | 465 | 3156 | 425 | 1762 | 299 | 3711 | 52 | 1886 | 641 | 521 | 541 | 206 | 1776 | 994 | 2 | 441 | 1250 | 232 | 1042 | 1726 | 1628 | 0 | 761 | 775 | 348 | 723 | 1073 | 929 | -- | 2278 | 400 | 14 | 1486 | 1423 | 2404 | 134 | 1092 | 32 | 68
te | 329 | 877 | 245 | 793 | 147 | 353 | 335 | 2121 | 380 | 2066 | 260 | 3762 | 54 | 1131 | 425 | 363 | 452 | 174 | 1886 | 741 | 2 | 314 | 797 | 162 | 820 | 1892 | 892 | 0 | 691 | 699 | 173 | 384 | 827 | 492 | 2278 | -- | 306 | 11 | 893 | 832 | 1748 | 107 | 644 | 21 | 61
th | 124 | 367 | 140 | 245 | 71 | 269 | 250 | 1020 | 185 | 164 | 87 | 378 | 18 | 599 | 305 | 149 | 197 | 109 | 196 | 217 | 1 | 92 | 364 | 85 | 468 | 200 | 595 | 1 | 340 | 347 | 69 | 178 | 469 | 261 | 400 | 306 | -- | 5 | 477 | 480 | 414 | 37 | 357 | 10 | 26
ti | 131 | 27 | 2 | 6 | 4 | 11 | 12 | 58 | 10 | 5 | 9 | 7 | 5 | 9 | 5 | 13 | 5 | 3 | 11 | 7 | 103 | 8 | 8 | 28 | 3 | 10 | 7 | 0 | 6 | 10 | 7 | 19 | 10 | 10 | 14 | 11 | 5 | -- | 8 | 8 | 4 | 2 | 5 | 0 | 6
tr | 435 | 4147 | 1495 | 860 | 449 | 1511 | 1294 | 5676 | 829 | 631 | 519 | 3694 | 91 | 5663 | 1242 | 724 | 680 | 346 | 706 | 930 | 5 | 1049 | 3695 | 287 | 3483 | 643 | 6223 | 6 | 1247 | 1229 | 301 | 968 | 3645 | 1348 | 1486 | 893 | 477 | 8 | -- | 4108 | 4340 | 370 | 2981 | 126 | 130
uk | 345 | 3457 | 1383 | 688 | 350 | 1619 | 1464 | 6562 | 721 | 508 | 400 | 3935 | 83 | 4829 | 1013 | 610 | 616 | 508 | 596 | 741 | 10 | 818 | 3567 | 280 | 4421 | 570 | 22241 | 5 | 1512 | 1498 | 306 | 875 | 3130 | 1309 | 1423 | 832 | 480 | 8 | 4108 | -- | 4290 | 442 | 3017 | 108 | 89
ur | 409 | 4935 | 966 | 1382 | 591 | 1651 | 1444 | 6320 | 766 | 1619 | 526 | 15666 | 61 | 6476 | 797 | 617 | 532 | 270 | 1395 | 1156 | 1 | 2833 | 6752 | 232 | 6759 | 1515 | 5309 | 2 | 1021 | 1009 | 510 | 1000 | 3060 | 1253 | 2404 | 1748 | 414 | 4 | 4340 | 4290 | -- | 389 | 3723 | 72 | 88
uz | 41 | 388 | 199 | 98 | 62 | 176 | 158 | 450 | 76 | 80 | 59 | 352 | 15 | 432 | 49 | 59 | 54 | 113 | 79 | 84 | 4 | 156 | 313 | 18 | 186 | 73 | 809 | 1 | 109 | 112 | 38 | 75 | 290 | 90 | 134 | 107 | 37 | 2 | 370 | 442 | 389 | -- | 257 | 10 | 15
vi | 285 | 2666 | 726 | 527 | 447 | 1858 | 1663 | 4575 | 438 | 450 | 352 | 3738 | 65 | 4810 | 908 | 631 | 530 | 201 | 473 | 719 | 2 | 657 | 3190 | 170 | 3755 | 431 | 3963 | 3 | 685 | 639 | 216 | 724 | 2330 | 936 | 1092 | 644 | 357 | 5 | 2982 | 3017 | 3723 | 257 | -- | 106 | 76
cy | 1 | 38 | 30 | 37 | 4 | 33 | 31 | 2655 | 40 | 21 | 30 | 77 | 6 | 145 | 25 | 20 | 12 | 12 | 16 | 39 | 0 | 7 | 66 | 9 | 110 | 16 | 134 | 36 | 42 | 45 | 5 | 20 | 59 | 37 | 32 | 21 | 10 | 0 | 126 | 108 | 72 | 10 | 106 | -- | 8
yo | 67 | 141 | 42 | 61 | 12 | 39 | 38 | 229 | 159 | 54 | 362 | 79 | 296 | 174 | 33 | 180 | 45 | 23 | 48 | 65 | 7 | 32 | 74 | 462 | 97 | 44 | 125 | 2 | 69 | 79 | 15 | 116 | 133 | 219 | 68 | 61 | 26 | 6 | 130 | 89 | 88 | 15 | 76 | 8 | --
## Dataset Creation
### Curation Rationale
[More information needed](https://github.com/csebuetnlp/CrossSum)
### Source Data
[BBC News](https://www.bbc.co.uk/ws/languages)
#### Initial Data Collection and Normalization
[Detailed in the paper](https://arxiv.org/abs/2112.08804/)
#### Who are the source language producers?
[Detailed in the paper](https://arxiv.org/abs/2112.08804/)
### Annotations
[Detailed in the paper](https://arxiv.org/abs/2112.08804/)
#### Annotation process
[Detailed in the paper](https://arxiv.org/abs/2112.08804/)
#### Who are the annotators?
[Detailed in the paper](https://arxiv.org/abs/2112.08804/)
### Personal and Sensitive Information
[More information needed](https://github.com/csebuetnlp/CrossSum)
## Considerations for Using the Data
### Social Impact of Dataset
[More information needed](https://github.com/csebuetnlp/CrossSum)
### Discussion of Biases
[More information needed](https://github.com/csebuetnlp/CrossSum)
### Other Known Limitations
[More information needed](https://github.com/csebuetnlp/CrossSum)
## Additional Information
### Dataset Curators
[More information needed](https://github.com/csebuetnlp/CrossSum)
### Licensing Information
Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to the original copyright holders.
### Citation Information
If you use any of the datasets, models or code modules, please cite the following paper:
```
@article{hasan2021crosssum,
author = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar},
title = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs},
journal = {CoRR},
volume = {abs/2112.08804},
year = {2021},
url = {https://arxiv.org/abs/2112.08804},
eprinttype = {arXiv},
eprint = {2112.08804}
}
```
### Contributions
Thanks to [@abhik1505040](https://github.com/abhik1505040) and [@Tahmid](https://github.com/Tahmid04) for adding this dataset. | [
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davidfant/rapidapi-example-responses-summaries | davidfant | 2023-12-11T10:06:49Z | 73 | 0 | [
"region:us"
] | null | 2023-12-10T11:24:49Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: summary
dtype: string
- name: usage
struct:
- name: completion_tokens
dtype: int64
- name: prompt_tokens
dtype: int64
- name: total_tokens
dtype: int64
splits:
- name: train
num_bytes: 667897
num_examples: 1000
download_size: 276970
dataset_size: 667897
---
# Dataset Card for "rapidapi-example-responses-summaries"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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ccmusic-database/music_genre | ccmusic-database | 2023-12-12T04:52:49Z | 71 | 8 | [
"task_categories:audio-classification",
"size_categories:10K<n<100K",
"language:zh",
"language:en",
"license:mit",
"music",
"art",
"region:us"
] | [
"audio-classification"
] | 2023-05-25T14:10:47Z | ---
license: mit
task_categories:
- audio-classification
language:
- zh
- en
tags:
- music
- art
pretty_name: Music Genre Dataset
size_categories:
- 10K<n<100K
viewer: false
---
# Dataset Card for Music Genre
## Dataset Description
- **Homepage:** <https://ccmusic-database.github.io>
- **Repository:** <https://huggingface.co/datasets/ccmusic-database/music_genre>
- **Paper:** <https://doi.org/10.5281/zenodo.5676893>
- **Leaderboard:** <https://ccmusic-database.github.io/team.html>
- **Point of Contact:** N/A
### Dataset Summary
This database contains about 1700 musical pieces (.mp3 format) with lengths of 270-300s that are divided into 17 genres in total.
### Supported Tasks and Leaderboards
Audio classification
### Languages
Multilingual
## Usage
When doing classification task, only one colum of fst_level_label, sec_level_label and thr_level_label can be used, not for mixing.
```
from datasets import load_dataset
dataset = load_dataset("ccmusic-database/music_genre", split="test")
for item in dataset:
print(item)
```
## Dataset Structure
<style>
#genres td {
vertical-align: middle !important;
text-align: center;
}
#genres th {
text-align: center;
}
</style>
<table id="genres">
<tr>
<td>mel(.jpg, 11.4s)</td>
<td>cqt(.jpg, 11.4s)</td>
<td>chroma(.jpg, 11.4s)</td>
<td>fst_level_label(2-class)</td>
<td>sec_level_label(9-class)</td>
<td>thr_level_label(16-class)</td>
</tr>
<tr>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/PqdpQP__ls-xo6lz93Q4y.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/EZfYLng40hh_FUudB9vvx.jpeg"></td>
<td><img src="https://cdn-uploads.huggingface.co/production/uploads/655e0a5b8c2d4379a71882a9/zviZ-rEKAvBCVFvKFml4R.jpeg"></td>
<td>1_Classic / 2_Non_classic</td>
<td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 7_Pop / 8_Dance_and_house / 9_Indie / 10_Soul_or_r_and_b / 11_Rock</td>
<td>3_Symphony / 4_Opera / 5_Solo / 6_Chamber / 12_Pop_vocal_ballad / 13_Adult_contemporary / 14_Teen_pop / 15_Contemporary_dance_pop / 16_Dance_pop / 17_Classic_indie_pop / 18_Chamber_cabaret_and_art_pop / 10_Soul_or_r_and_b / 19_Adult_alternative_rock / 20_Uplifting_anthemic_rock / 21_Soft_rock / 22_Acoustic_pop</td>
</tr>
<tr>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
<td>...</td>
</tr>
</table>
### Data Instances
.zip(.jpg, .wav)
### Data Fields
```
1_Classic
3_Symphony
4_Opera
5_Solo
6_Chamber
2_Non_classic
7_Pop
12_Pop_vocal_ballad
13_Adult_contemporary
14_Teen_pop
8_Dance_and_house
15_Contemporary_dance_pop
16_Dance_pop
9_Indie
17_Classic_indie_pop
18_Chamber_cabaret_and_art_pop
10_Soul_or_r_and_b
11_Rock
19_Adult_alternative_rock
20_Uplifting_anthemic_rock
21_Soft_rock
22_Acoustic_pop
```
### Data Splits
| total | 36375 |
| :-------------: | :---: |
| train(80%) | 29100 |
| validation(10%) | 3637 |
| test(10%) | 3638 |
## Dataset Creation
### Curation Rationale
Promoting the development of AI in the music industry
### Source Data
#### Initial Data Collection and Normalization
Zhaorui Liu, Monan Zhou
#### Who are the source language producers?
Composers of the songs in dataset
### Annotations
#### Annotation process
Students collected about 1700 musical pieces (.mp3 format) with lengths of 270-300s divided into 17 genres in total.
#### Who are the annotators?
Students from CCMUSIC
### Personal and Sensitive Information
Due to copyright issues with the original music, only mel spectrograms are provided in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
Promoting the development of AI in the music industry
### Discussion of Biases
Most are English songs
### Other Known Limitations
Samples are not balanced enough
## Additional Information
### Dataset Curators
Zijin Li
### Evaluation
Coming soon...
### Licensing Information
```
MIT License
Copyright (c) CCMUSIC
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.
```
### Citation Information
```
@dataset{zhaorui_liu_2021_5676893,
author = {Zhaorui Liu, Monan Zhou, Shenyang Xu, Yuan Wang, Zhaowen Wang, Wei Li and Zijin Li},
title = {CCMUSIC DATABASE: A Music Data Sharing Platform for Computational Musicology Research},
month = {nov},
year = {2021},
publisher = {Zenodo},
version = {1.1},
doi = {10.5281/zenodo.5676893},
url = {https://doi.org/10.5281/zenodo.5676893}
}
```
### Contributions
Provide a dataset for music genre classification | [
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raminass/full_opinions_1994_2020 | raminass | 2023-12-11T15:26:13Z | 70 | 0 | [
"region:us"
] | null | 2023-12-11T15:25:42Z | ---
dataset_info:
features:
- name: author_name
dtype: string
- name: category
dtype: string
- name: per_curiam
dtype: bool
- name: case_name
dtype: string
- name: date_filed
dtype: string
- name: federal_cite_one
dtype: string
- name: absolute_url
dtype: string
- name: cluster
dtype: string
- name: year_filed
dtype: int64
- name: scdb_id
dtype: string
- name: scdb_decision_direction
dtype: float64
- name: scdb_votes_majority
dtype: float64
- name: scdb_votes_minority
dtype: float64
- name: text
dtype: string
- name: clean_text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 180294148
num_examples: 3790
download_size: 94040799
dataset_size: 180294148
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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] |
open-llm-leaderboard/details_TheBloke__Mixtral-8x7B-v0.1-GPTQ | open-llm-leaderboard | 2023-12-16T08:28:17Z | 70 | 0 | [
"region:us"
] | null | 2023-12-16T08:28:03Z | ---
pretty_name: Evaluation run of TheBloke/Mixtral-8x7B-v0.1-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/Mixtral-8x7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated 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_TheBloke__Mixtral-8x7B-v0.1-GPTQ\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-16T08:25:08.853393](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Mixtral-8x7B-v0.1-GPTQ/blob/main/results_2023-12-16T08-25-08.853393.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\": 0.6905952821572917,\n\
\ \"acc_stderr\": 0.030759474803252005,\n \"acc_norm\": 0.696151240116133,\n\
\ \"acc_norm_stderr\": 0.03135740142119605,\n \"mc1\": 0.2998776009791922,\n\
\ \"mc1_stderr\": 0.016040352966713616,\n \"mc2\": 0.4543006274006171,\n\
\ \"mc2_stderr\": 0.014099486144642947\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6168941979522184,\n \"acc_stderr\": 0.014206472661672883,\n\
\ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.013921008595179347\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6413065126468831,\n\
\ \"acc_stderr\": 0.004786368011500458,\n \"acc_norm\": 0.8472415853415655,\n\
\ \"acc_norm_stderr\": 0.0035901923719696637\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.034597776068105365,\n\
\ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.034597776068105365\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.68,\n\
\ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \
\ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7547169811320755,\n \"acc_stderr\": 0.0264803571798957,\n\
\ \"acc_norm\": 0.7547169811320755,\n \"acc_norm_stderr\": 0.0264803571798957\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.03216600808802269,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.03216600808802269\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956911,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956911\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.55,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.6647398843930635,\n \"acc_stderr\": 0.03599586301247077,\n\
\ \"acc_norm\": 0.6647398843930635,\n \"acc_norm_stderr\": 0.03599586301247077\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.49019607843137253,\n\
\ \"acc_stderr\": 0.04974229460422817,\n \"acc_norm\": 0.49019607843137253,\n\
\ \"acc_norm_stderr\": 0.04974229460422817\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\
\ 0.6425531914893617,\n \"acc_stderr\": 0.031329417894764254,\n \"\
acc_norm\": 0.6425531914893617,\n \"acc_norm_stderr\": 0.031329417894764254\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.543859649122807,\n\
\ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.543859649122807,\n\
\ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6620689655172414,\n \"acc_stderr\": 0.039417076320648906,\n\
\ \"acc_norm\": 0.6620689655172414,\n \"acc_norm_stderr\": 0.039417076320648906\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4576719576719577,\n \"acc_stderr\": 0.02565886886205832,\n \"\
acc_norm\": 0.4576719576719577,\n \"acc_norm_stderr\": 0.02565886886205832\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8290322580645161,\n\
\ \"acc_stderr\": 0.021417242936321582,\n \"acc_norm\": 0.8290322580645161,\n\
\ \"acc_norm_stderr\": 0.021417242936321582\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5960591133004927,\n \"acc_stderr\": 0.03452453903822033,\n\
\ \"acc_norm\": 0.5960591133004927,\n \"acc_norm_stderr\": 0.03452453903822033\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695482995,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695482995\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8383838383838383,\n \"acc_stderr\": 0.02622591986362928,\n \"\
acc_norm\": 0.8383838383838383,\n \"acc_norm_stderr\": 0.02622591986362928\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.023901157979402534,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.023901157979402534\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.02889774874113114,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.02889774874113114\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7563025210084033,\n \"acc_stderr\": 0.027886828078380558,\n\
\ \"acc_norm\": 0.7563025210084033,\n \"acc_norm_stderr\": 0.027886828078380558\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.44370860927152317,\n \"acc_stderr\": 0.04056527902281732,\n \"\
acc_norm\": 0.44370860927152317,\n \"acc_norm_stderr\": 0.04056527902281732\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8752293577981651,\n \"acc_stderr\": 0.014168298359156327,\n \"\
acc_norm\": 0.8752293577981651,\n \"acc_norm_stderr\": 0.014168298359156327\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.625,\n \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.625,\n\
\ \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.8480392156862745,\n \"acc_stderr\": 0.025195658428931785,\n\
\ \"acc_norm\": 0.8480392156862745,\n \"acc_norm_stderr\": 0.025195658428931785\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8481012658227848,\n \"acc_stderr\": 0.02336387809663245,\n \
\ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.02336387809663245\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.726457399103139,\n\
\ \"acc_stderr\": 0.029918586707798824,\n \"acc_norm\": 0.726457399103139,\n\
\ \"acc_norm_stderr\": 0.029918586707798824\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\":\
\ 0.8842975206611571,\n \"acc_stderr\": 0.02919980245562281,\n \"\
acc_norm\": 0.8842975206611571,\n \"acc_norm_stderr\": 0.02919980245562281\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8425925925925926,\n\
\ \"acc_stderr\": 0.03520703990517965,\n \"acc_norm\": 0.8425925925925926,\n\
\ \"acc_norm_stderr\": 0.03520703990517965\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.0335195387952127,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.0335195387952127\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5,\n\
\ \"acc_stderr\": 0.04745789978762494,\n \"acc_norm\": 0.5,\n \
\ \"acc_norm_stderr\": 0.04745789978762494\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.883495145631068,\n \"acc_stderr\": 0.031766839486404075,\n\
\ \"acc_norm\": 0.883495145631068,\n \"acc_norm_stderr\": 0.031766839486404075\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9230769230769231,\n\
\ \"acc_stderr\": 0.017456987872436183,\n \"acc_norm\": 0.9230769230769231,\n\
\ \"acc_norm_stderr\": 0.017456987872436183\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8786717752234994,\n\
\ \"acc_stderr\": 0.011675913883906723,\n \"acc_norm\": 0.8786717752234994,\n\
\ \"acc_norm_stderr\": 0.011675913883906723\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7601156069364162,\n \"acc_stderr\": 0.022989592543123563,\n\
\ \"acc_norm\": 0.7601156069364162,\n \"acc_norm_stderr\": 0.022989592543123563\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3743016759776536,\n\
\ \"acc_stderr\": 0.01618544417945717,\n \"acc_norm\": 0.3743016759776536,\n\
\ \"acc_norm_stderr\": 0.01618544417945717\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8104575163398693,\n \"acc_stderr\": 0.02244235826333621,\n\
\ \"acc_norm\": 0.8104575163398693,\n \"acc_norm_stderr\": 0.02244235826333621\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7717041800643086,\n\
\ \"acc_stderr\": 0.023839303311398195,\n \"acc_norm\": 0.7717041800643086,\n\
\ \"acc_norm_stderr\": 0.023839303311398195\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8148148148148148,\n \"acc_stderr\": 0.021613809395224805,\n\
\ \"acc_norm\": 0.8148148148148148,\n \"acc_norm_stderr\": 0.021613809395224805\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5709219858156028,\n \"acc_stderr\": 0.029525914302558562,\n \
\ \"acc_norm\": 0.5709219858156028,\n \"acc_norm_stderr\": 0.029525914302558562\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5071707953063885,\n\
\ \"acc_stderr\": 0.012768922739553304,\n \"acc_norm\": 0.5071707953063885,\n\
\ \"acc_norm_stderr\": 0.012768922739553304\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7683823529411765,\n \"acc_stderr\": 0.025626533803777562,\n\
\ \"acc_norm\": 0.7683823529411765,\n \"acc_norm_stderr\": 0.025626533803777562\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.7418300653594772,\n \"acc_stderr\": 0.01770453165325007,\n \
\ \"acc_norm\": 0.7418300653594772,\n \"acc_norm_stderr\": 0.01770453165325007\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.763265306122449,\n \"acc_stderr\": 0.027212835884073142,\n\
\ \"acc_norm\": 0.763265306122449,\n \"acc_norm_stderr\": 0.027212835884073142\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\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\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\": 0.2998776009791922,\n\
\ \"mc1_stderr\": 0.016040352966713616,\n \"mc2\": 0.4543006274006171,\n\
\ \"mc2_stderr\": 0.014099486144642947\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8113654301499605,\n \"acc_stderr\": 0.010995172318019785\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4829416224412434,\n \
\ \"acc_stderr\": 0.013764467123761316\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|arc:challenge|25_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|gsm8k|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hellaswag|10_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T08-25-08.853393.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-16T08-25-08.853393.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- '**/details_harness|winogrande|5_2023-12-16T08-25-08.853393.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-16T08-25-08.853393.parquet'
- config_name: results
data_files:
- split: 2023_12_16T08_25_08.853393
path:
- results_2023-12-16T08-25-08.853393.parquet
- split: latest
path:
- results_2023-12-16T08-25-08.853393.parquet
---
# Dataset Card for Evaluation run of TheBloke/Mixtral-8x7B-v0.1-GPTQ
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TheBloke/Mixtral-8x7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated 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_TheBloke__Mixtral-8x7B-v0.1-GPTQ",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-16T08:25:08.853393](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__Mixtral-8x7B-v0.1-GPTQ/blob/main/results_2023-12-16T08-25-08.853393.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": 0.6905952821572917,
"acc_stderr": 0.030759474803252005,
"acc_norm": 0.696151240116133,
"acc_norm_stderr": 0.03135740142119605,
"mc1": 0.2998776009791922,
"mc1_stderr": 0.016040352966713616,
"mc2": 0.4543006274006171,
"mc2_stderr": 0.014099486144642947
},
"harness|arc:challenge|25": {
"acc": 0.6168941979522184,
"acc_stderr": 0.014206472661672883,
"acc_norm": 0.6518771331058021,
"acc_norm_stderr": 0.013921008595179347
},
"harness|hellaswag|10": {
"acc": 0.6413065126468831,
"acc_stderr": 0.004786368011500458,
"acc_norm": 0.8472415853415655,
"acc_norm_stderr": 0.0035901923719696637
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7631578947368421,
"acc_stderr": 0.034597776068105365,
"acc_norm": 0.7631578947368421,
"acc_norm_stderr": 0.034597776068105365
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7547169811320755,
"acc_stderr": 0.0264803571798957,
"acc_norm": 0.7547169811320755,
"acc_norm_stderr": 0.0264803571798957
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.03216600808802269,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.03216600808802269
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.53,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6647398843930635,
"acc_stderr": 0.03599586301247077,
"acc_norm": 0.6647398843930635,
"acc_norm_stderr": 0.03599586301247077
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.49019607843137253,
"acc_stderr": 0.04974229460422817,
"acc_norm": 0.49019607843137253,
"acc_norm_stderr": 0.04974229460422817
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6425531914893617,
"acc_stderr": 0.031329417894764254,
"acc_norm": 0.6425531914893617,
"acc_norm_stderr": 0.031329417894764254
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.543859649122807,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.543859649122807,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6620689655172414,
"acc_stderr": 0.039417076320648906,
"acc_norm": 0.6620689655172414,
"acc_norm_stderr": 0.039417076320648906
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4576719576719577,
"acc_stderr": 0.02565886886205832,
"acc_norm": 0.4576719576719577,
"acc_norm_stderr": 0.02565886886205832
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4603174603174603,
"acc_stderr": 0.04458029125470973,
"acc_norm": 0.4603174603174603,
"acc_norm_stderr": 0.04458029125470973
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8290322580645161,
"acc_stderr": 0.021417242936321582,
"acc_norm": 0.8290322580645161,
"acc_norm_stderr": 0.021417242936321582
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5960591133004927,
"acc_stderr": 0.03452453903822033,
"acc_norm": 0.5960591133004927,
"acc_norm_stderr": 0.03452453903822033
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.031922715695482995,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.031922715695482995
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8383838383838383,
"acc_stderr": 0.02622591986362928,
"acc_norm": 0.8383838383838383,
"acc_norm_stderr": 0.02622591986362928
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328972,
"acc_norm": 0.9067357512953368,
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```
## Dataset Details
### Dataset Description
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[More Information Needed] | [
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Zakia/drugscom_reviews | Zakia | 2023-12-11T16:21:20Z | 69 | 0 | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"health",
"medicine",
"patient reviews",
"drug reviews",
"sentiment analysis",
"region:us"
] | [
"text-classification"
] | 2023-11-30T16:29:12Z | ---
pretty_name: DrugsCom Reviews
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
tags:
- health
- medicine
- patient reviews
- drug reviews
- sentiment analysis
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
---
# Dataset Card for "DrugsCom Reviews"
## 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)
- [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:** [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/462/drug+review+dataset+drugs+com)
- **Repository:** [DrugsCom Reviews on Hugging Face](https://huggingface.co/datasets/Zakia/drugscom_reviews)
- **Size of the training dataset file:** 82.314 MB
- **Size of the test dataset file:** 27.414 MB
- **Total size of downloaded dataset files:** 109.73 MB
### Dataset Summary
The DrugsCom Reviews dataset is originally sourced from the UCI Machine Learning Repository. It provides patient reviews on specific drugs along with related conditions and a 10-star patient rating reflecting overall patient satisfaction. The dataset has been uploaded to Hugging Face to facilitate easier access and use by the machine learning community. It contains 161,297 instances in the training set and 53,766 instances in the test set.
### Supported Tasks and Leaderboards
This dataset can be used for sentiment analysis and text classification tasks.
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
A data instance from the `train` split:
```
{
"drugName": "Buprenex",
"condition": "Pain",
"review": "I have severe drug allergies especially opiates, I have been on Buprenorphine for many years now and found it to be an excellent pain reliever. I have found that at times though it is hard to find and have had to go to the patch.",
"rating": 10,
"date": "May 11, 2012",
"usefulCount": 27
}
```
A data instance from the `test` split:
```
{
"drugName": "Nasacort Allergy 24HR",
"condition": "Allergic Rhinitis",
"review": "Since I start using this product I experienced change of vision and headaches.",
"rating": 3,
"date": "September 8, 2015",
"usefulCount": 27
}
```
#### plain_text
- **Size of the training dataset file:** 82.314 MB
- **Size of the test dataset file:** 27.414 MB
- **Total size of downloaded dataset files:** 109.73 MB
A data instance consists of the following fields:
- `drugName`: The name of the drug reviewed.
- `condition`: The condition for which the drug was prescribed.
- `review`: The text of the review by the patient.
- `rating`: A patient satisfaction rating out of 10.
- `date`: The date when the review was posted.
- `usefulCount`: The number of users who found the review useful.
### Data Fields
- `drugName`: string
- `condition`: string
- `review`: string
- `rating`: integer (0-10)
- `date`: date
- `usefulCount`: integer
### Data Splits
The dataset is split into training and testing sets as follows:
- `train`: 161,297 instances
- `test`: 53,766 instances
## Dataset Creation
### Curation Rationale
The dataset was curated with the intention to study sentiment analysis of drug experience and the transferability of models among different domains and data sources.
### Source Data
#### Initial Data Collection and Normalization
The dataset was collected by crawling online pharmaceutical review sites. No additional preprocessing or normalization has been conducted on the data provided in this repository; it is presented in its original form as obtained from the source.
#### Who are the source language producers?
The reviews were written by patients and users of the drugs.com website.
### Annotations
#### Annotation process
No additional annotation process was followed as the data contains self-reported patient ratings.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
The dataset contains sensitive information in the form of patient drug reviews.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset can be used to study the impact of drugs on patients, which can be beneficial for healthcare research.
### Discussion of Biases
No known biases but users should consider the self-reported nature of the data.
### Other Known Limitations
The dataset may not generalize well to drugs or conditions not represented in the data.
## Additional Information
### Dataset Curators
Curated by the UCI Machine Learning Repository.
### Licensing Information
Licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0).
### Citation Information
When using this dataset, please cite the original source and also the Hugging Face dataset repository:
```
@misc{misc_drug_review_dataset_(drugs.com)_462,
author = {Kallumadi,Surya and Grer,Felix},
title = {Drug Review Dataset (Drugs.com)},
year = {2018},
howpublished = {UCI Machine Learning Repository},
note = {DOI: https://doi.org/10.24432/C5SK5S}
}
```
And:
```
@misc{huggingface:drugscom_reviews,
title = {Drug Review Dataset (Drugs.com) - Hugging Face Version},
author = {Salod, Zakia},
year = {2023},
publisher = {Hugging Face},
howpublished = {Hugging Face Datasets Library},
url = {https://huggingface.co/datasets/Zakia/drugscom_reviews}
}
```
### Contributions
This dataset was uploaded to Hugging Face by [Zakia](https://huggingface.co/Zakia). Special thanks to the community for their interest and engagement. Future contributions, including discussions, issues, and improvements to the dataset card, are welcomed and greatly appreciated in the [discussions section of this dataset's Hugging Face page](https://huggingface.co/datasets/Zakia/drugscom_reviews/discussions).
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ArtifactAI/dataset_modality_benchmark | ArtifactAI | 2023-12-13T20:29:52Z | 67 | 0 | [
"region:us"
] | null | 2023-12-05T19:48:42Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: answer
dtype:
class_label:
names:
'0': Audio
'1': Graphs
'2': Images
'3': Tabular
'4': Texts
'5': Videos
- name: choices
sequence: string
splits:
- name: train
num_bytes: 602747
num_examples: 3172
- name: test
num_bytes: 150501
num_examples: 794
download_size: 87493
dataset_size: 753248
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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proserve/FedML_PubMedQA_instruction_stf_dataset | proserve | 2023-12-17T17:07:12Z | 67 | 0 | [
"region:us"
] | null | 2023-12-17T17:06:52Z | ---
dataset_info:
features:
- name: content
dtype: string
splits:
- name: train
num_bytes: 545857127
num_examples: 272518
download_size: 266576180
dataset_size: 545857127
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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allenai/dolma | allenai | 2023-12-15T22:53:10Z | 66 | 354 | [
"task_categories:text-generation",
"size_categories:n>1T",
"language:en",
"license:other",
"language-modeling",
"casual-lm",
"llm",
"region:us"
] | [
"text-generation"
] | 2023-06-30T20:14:39Z | ---
license: other
license_name: impact-license-medium-risk
license_link: https://allenai.org/licenses/impact-mr
viewer: false
task_categories:
- text-generation
language:
- en
tags:
- language-modeling
- casual-lm
- llm
pretty_name: Dolma
size_categories:
- n>1T
extra_gated_prompt: "Access to this dataset is automatically granted upon accepting the [**AI2 ImpACT License - Medium Risk Artifacts (“MR Agreement”)**](https://allenai.org/licenses/impact-mr) and completing all fields below."
extra_gated_fields:
Your full name: text
Organization or entity you are affiliated with: text
State or country you are located in: text
Contact email: text
Please describe your intended use of the medium risk artifact(s): text
I AGREE to the terms and conditions of the MR Agreement above: checkbox
I AGREE to AI2’s use of my information for legal notices and administrative matters: checkbox
I CERTIFY that the information I have provided is true and accurate: checkbox
---
# Dolma
<img alt="Dolma's official logo. It's dolma written in yellow, round lowercase letters over a blue background." src="https://raw.githubusercontent.com/allenai/dolma/main/docs/assets/AI2_Blog_1400x685_2x.webp" width="100%">
Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials. It is openly released under AI2’s ImpACT license as a medium risk artifact.
More information:
- Read Dolma **announcement blogpost** [on Medium](https://soldni.medium.com/dolma-3-trillion-tokens-open-llm-corpus-9a0ff4b8da64);
- Learn more about Dolma on its [**Data Sheet**](https://drive.google.com/file/d/12gOf5I5RytsD159nSP7iim_5zN31FCXq/view?usp=drive_link);
- Review Dolma's [**ImpACT license** for medium risk artifacts](https://allenai.org/licenses/impact-mr);
- Explore the [**open source tools**](https://github.com/allenai/dolma) we created to curate Dolma.
- Want to request removal of personal data? Use [this form](https://forms.gle/q4BNUUxUxKwKkfdT6) to notify us of documents containing PII about a specific user.
To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our [GitHub project page](https://github.com/allenai/dolma/tree/main/docs)!
## Summary Statistics
|**Source**|**Type**|**Gzip files (GB)**|**Documents (millions)**|**[GPT-NeoX](https://huggingface.co/EleutherAI/gpt-neox-20b) Tokens (billions)**|
|:---|:---:|:---:|:---:|:----:|
|[CommonCrawl](https://commoncrawl.org/)|web|4,197|4,600|2,415|
|[C4](https://huggingface.co/datasets/allenai/c4)|web|302|364|175|
|[peS2o](https://huggingface.co/datasets/allenai/peS2o)|academic|150|38.8|57|
|[The Stack](https://huggingface.co/datasets/bigcode/the-stack)|code|319|236|430|
|[Project Gutenberg](https://www.gutenberg.org/)|books|6.6|0.052|4.8|
|[Wikipedia](https://dumps.wikimedia.org/)|encyclopedic|5.8|6.1|3.6|
||**Total** |**4980.4**|**5,245**|**3,084**|
## Download
The fastest way to download Dolma is to directly download the individual files across multiple threads.
This can be achieved using wget or [aria2](https://github.com/aria2/aria2) Linux/Mac/Windows package (`sudo apt-get install aria2` on Ubuntu).
For downloading individual files, simply use `wget` as follows:
`wget --header 'Authorization: Bearer YOUR_HF_HUB_ACCESS_TOKEN' https://huggingface.co/datasets/allenai/dolma/resolve/main/data/peS2o/s2_v3-0000.json.gz`
For downloading many files across multiple threads, first prepare a `.txt` file with the urls you would like such as via the script below:
```python
OUT_DIRECTORY = "/scratch/dolma/data"
# URLs for cc_en_head
cc_en_head_base_url = "https://huggingface.co/datasets/allenai/dolma/resolve/main/data/common-crawl/cc_en_head/cc_en_head-"
cc_en_head_url_list = [f"{cc_en_head_base_url}{str(i).zfill(4)}.json.gz\n dir={OUT_DIRECTORY}/cc_en_head\n out=cc_en_head-{str(i).zfill(4)}.json.gz" for i in range(612)]
# URLs for cc_en_middle
cc_en_middle_base_url = "https://huggingface.co/datasets/allenai/dolma/resolve/main/data/common-crawl/cc_en_middle/cc_en_middle-"
cc_en_middle_url_list = [f"{cc_en_middle_base_url}{str(i).zfill(4)}.json.gz\n dir={OUT_DIRECTORY}/cc_en_middle\n out=cc_en_middle-{str(i).zfill(4)}.json.gz" for i in range(777)]
# URLs for cc_en_tail
cc_en_tail_base_url = "https://huggingface.co/datasets/allenai/dolma/resolve/main/data/common-crawl/cc_en_tail/cc_en_tail-"
cc_en_tail_url_list = [f"{cc_en_tail_base_url}{str(i).zfill(4)}.json.gz\n dir={OUT_DIRECTORY}/cc_en_tail\n out=cc_en_tail-{str(i).zfill(4)}.json.gz" for i in range(1493)]
# URLs for s2_v3
s2_v3_base_url = "https://huggingface.co/datasets/allenai/dolma/resolve/main/data/peS2o/s2_v3-"
s2_v3_url_list = [f"{s2_v3_base_url}{str(i).zfill(4)}.json.gz\n dir={OUT_DIRECTORY}/peS2o\n out=s2_v3-{str(i).zfill(4)}.json.gz" for i in range(42)]
# URLs for The Stack
LANG_TO_FILES = {'lasso': 1, 'nsis': 1, 'literate-agda': 1, 'metal': 1, 'xojo': 1, 'max': 8, 'jupyter-notebook': 101, 'asp': 7, 'elixir': 14, 'html+erb': 19, 'julia': 22, 'dart': 63, 'ragel-in-ruby-host': 1, 'api-blueprint': 1, 'gams': 1, 'tex': 71, 'xml': 101, 'smalltalk': 17, 'cmake': 11, 'piglatin': 1, "cap'n-proto": 1, 'common-lisp': 21, 'stylus': 3, 'typescript': 101, 'jflex': 1, 'factor': 1, 'arc': 1, 'parrot-internal-representation': 1, 'aspectj': 1, 'go': 101, 'urweb': 1, 'dns-zone': 1, 'purebasic': 1, 'toml': 15, 'erlang': 11, 'hy': 1, 'component-pascal': 2, 'oz': 1, 'opa': 1, 'handlebars': 10, 'gas': 15, 'less': 17, 'gnuplot': 15, 'harbour': 1, 'vhdl': 16, 'octave': 1, 'powershell': 21, 'clips': 1, 'fish': 1, 'prolog': 1, 'sparql': 1, 'objective-j': 1, 'scaml': 1, 'twig': 20, 'gettext-catalog': 101, 'purescript': 2, 'vala': 1, 'gosu': 1, 'apacheconf': 1, 'xc': 1, 'lean': 3, 'mako': 1, 'r': 4, 'unrealscript': 1, 'solidity': 21, 'pike': 1, 'cartocss': 1, 'maple': 1, 'graphql': 3, 'unity3d-asset': 101, 'swift': 101, 'dockerfile': 13, 'digital-command-language': 1, 'scala': 83, 'sqf': 2, 'logtalk': 1, 'coq': 1, 'shellsession': 1, 'befunge': 1, 'nu': 1, 'ecere-projects': 1, 'zimpl': 1, 'shen': 1, 'golo': 1, 'web-ontology-language': 12, 'sas': 2, 'uno': 1, 'livescript': 1, 'literate-haskell': 1, 'clojure': 8, 'perl6': 1, 'zig': 3, 'liquid': 2, 'ec': 1, 'blitzbasic': 1, 'sql': 101, 'http': 2, 'xproc': 1, 'kit': 1, 'textile': 1, 'netlinx': 1, 'propeller-spin': 1, 'cython': 5, 'realbasic': 1, 'dogescript': 1, 'llvm': 9, 'pawn': 1, 'groff': 40, 'html+django': 3, 'csound': 1, 'd': 1, 'agda': 2, 'css': 101, 'yacc': 7, 'robotframework': 1, 'kotlin': 101, 'grace': 1, 'abap': 2, 'blitzmax': 1, 'webassembly': 3, 'ampl': 1, 'postscript': 16, 'nit': 1, 'gentoo-eclass': 1, 'xpages': 1, 'linker-script': 2, 'yang': 3, 'jade': 4, 'standard-ml': 6, 'javascript': 101, 'moonscript': 1, 'mtml': 1, 'saltstack': 1, 'freemarker': 5, 'ston': 1, 'html+eex': 1, 'xs': 1, 'c++': 101, 'matlab': 1, 'm4': 2, 'xbase': 1, 'perl': 37, 'emacs-lisp': 7, 'bison': 1, 'slim': 2, 'grammatical-framework': 1, 'rdoc': 1, 'nix': 10, 'clean': 1, 'module-management-system': 1, 'nimrod': 6, 'raml': 1, 'forth': 1, 'squirrel': 1, 'alloy': 1, 'opencl': 3, 'c': 101, 'sass': 4, 'eiffel': 2, 'papyrus': 1, 'html': 109, 'java': 101, 'hcl': 14, 'isabelle': 2, 'markdown': 101, 'gentoo-ebuild': 2, 'objdump': 1, 'emberscript': 1, 'text': 101, 'bro': 1, 'opal': 1, 'haskell': 35, 'mupad': 1, 'desktop': 1, 'modelica': 2, 'coldfusion-cfc': 2, 'fantom': 1, 'glsl': 10, 'ocaml': 16, 'nesc': 2, 'scheme': 7, 'crystal': 5, 'tcsh': 1, 'c2hs-haskell': 1, 'idris': 1, 'logos': 4, 'coffeescript': 13, 'g-code': 10, 'sage': 1, 'haml': 4, 'tcl': 7, 'smt': 5, 'ox': 1, 'chuck': 1, 'xquery': 1, 'batchfile': 7, 'pod': 2, 'xtend': 1, 'restructuredtext': 61, 'rmarkdown': 1, 'turtle': 33, 'jsx': 45, 'protocol-buffer': 8, "ren'py": 2, 'diff': 32, 'slash': 1, 'darcs-patch': 1, 'numpy': 1, 'augeas': 1, 'wisp': 1, 'edn': 15, 'ooc': 1, 'bitbake': 2, 'labview': 1, 'inform-7': 1, 'rust': 101, 'creole': 1, 'apl': 1, 'arduino': 11, 'openscad': 2, 'cuda': 9, 'thrift': 1, 'yaml': 101, 'fancy': 1, 'coldfusion': 1, 'python': 101, 'clarion': 1, 'glyph': 1, 'parrot': 1, 'lookml': 1, 'java-server-pages': 19, 'oxygene': 1, 'flux': 1, 'scilab': 1, 'groovy-server-pages': 2, 'rhtml': 1, 'eagle': 52, 'parrot-assembly': 1, 'igor-pro': 1, 'webidl': 1, 'bluespec': 1, 'unified-parallel-c': 1, 'smali': 38, 'haxe': 9, 'ada': 7, 'lua': 48, 'pascal': 21, 'html+php': 6, 'irc-log': 1, 'x10': 1, 'netlogo': 1, 'ioke': 1, 'dm': 1, 'self': 1, 'elm': 5, 'ats': 1, 'brainfuck': 1, 'mask': 1, 'rouge': 1, 'turing': 1, 'lex': 2, 'gap': 1, 'pogoscript': 1, 'kicad': 30, 'io': 1, 'objective-c++': 8, 'qml': 4, 'redcode': 1, 'autoit': 2, 'processing': 4, 'systemverilog': 6, 'gdscript': 5, 'f-sharp': 12, 'fortran': 23, 'monkey': 1, 'c-sharp': 101, 'xslt': 9, 'viml': 6, 'renderscript': 1, 'scss': 84, 'cucumber': 4, 'verilog': 1, 'genshi': 1, 'racket': 1, 'krl': 1, 'actionscript': 10, 'pan': 1, 'cirru': 1, 'chapel': 1, 'pure-data': 2, 'm': 1, 'applescript': 1, 'inno-setup': 1, 'volt': 1, 'myghty': 1, 'groovy': 17, 'ags-script': 1, 'mirah': 1, 'lsl': 1, 'brightscript': 1, 'python-traceback': 1, 'sourcepawn': 2, 'maxscript': 1, 'zephir': 1, 'supercollider': 1, 'mathematica': 20, 'awk': 1, 'autohotkey': 2, 'lfe': 1, 'ruby': 101, 'visual-basic': 20, 'ini': 59, 'red': 1, 'omgrofl': 1, 'idl': 1, 'rebol': 1, 'vue': 101, 'ninja': 2, 'ecl': 1, 'lolcode': 1, 'tea': 1, 'txl': 1, 'smarty': 9, 'vcl': 1, 'php': 101, 'literate-coffeescript': 1, 'click': 1, 'pony': 1, 'mediawiki': 5, 'stata': 5, 'stan': 1, 'nginx': 1, 'asciidoc': 16, 'antlr': 1, 'cobol': 1, 'org': 5, 'latte': 1, 'makefile': 32, 'ceylon': 1, 'graphviz-(dot)': 13, 'lilypond': 1, 'dylan': 1, 'qmake': 1, 'muf': 1, 'j': 1, 'pov-ray-sdl': 1, 'jasmin': 1, 'shell': 73, 'cycript': 1, 'boo': 1, 'hlsl': 2}
stack_base_url = "https://huggingface.co/datasets/allenai/dolma/resolve/main/data/stack-code/"
stack_url_list = []
for lang, num_files in sorted(LANG_TO_FILES.items()):
for i in range(num_files):
stack_url_list.append(f"{stack_base_url}{lang}/v3-{str(i).zfill(4)}.json.gz\n dir={OUT_DIRECTORY}/stack-code/{lang}\n out=v3-{str(i).zfill(4)}.json.gz")
# Combine all URL lists
all_url_list = cc_en_head_url_list + cc_en_middle_url_list + cc_en_tail_url_list + s2_v3_url_list + stack_url_list
out = open("files.txt", "a")
# Print the combined list of URLs
for i, url in enumerate(all_url_list):
out.write(url + "\n")
```
Then you can download them all in parallel using:
`aria2c --input-file files.txt --header 'Authorization: Bearer YOUR_HF_HUB_ACCESS_TOKEN'`
You can also add `-s` to increase the number of connections, e.g. `-s 10` (defaults to 5).
To get the exact file counts that are used for The Stack in the above script (`LANG_TO_FILES`), you can follow the below:
Fetch all files (does not download them, so should be fast): `GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/allenai/dolma.git`
Then run:
```python
import os
directory = "dolma/data/stack-code"
folder_dict = {}
for folder in os.listdir(directory):
folder_path = os.path.join(directory, folder)
if os.path.isdir(folder_path):
file_count = len([f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))])
folder_dict[folder] = file_count
print(folder_dict)
```
## Bibtex
If you use our dataset or tooling, please cite us at:
```
@article{dolma,
title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
author = {Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo},
year = {2023},
journal={arXiv preprint},
}
``` | [
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open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v2 | open-llm-leaderboard | 2023-12-15T00:57:28Z | 66 | 0 | [
"region:us"
] | null | 2023-12-15T00:56:44Z | ---
pretty_name: Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [kyujinpy/PlatYi-34B-Llama-Q-v2](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated 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_kyujinpy__PlatYi-34B-Llama-Q-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-15T00:53:56.888909](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v2/blob/main/results_2023-12-15T00-53-56.888909.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\": 0.7577128670517151,\n\
\ \"acc_stderr\": 0.0282619036411761,\n \"acc_norm\": 0.7647346696571936,\n\
\ \"acc_norm_stderr\": 0.028784585052284057,\n \"mc1\": 0.37821297429620565,\n\
\ \"mc1_stderr\": 0.01697633590754687,\n \"mc2\": 0.5264877146338937,\n\
\ \"mc2_stderr\": 0.014681108343134457\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5938566552901023,\n \"acc_stderr\": 0.014351656690097862,\n\
\ \"acc_norm\": 0.6109215017064846,\n \"acc_norm_stderr\": 0.014247309976045607\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6506671977693687,\n\
\ \"acc_stderr\": 0.00475784902341196,\n \"acc_norm\": 0.8509261103365864,\n\
\ \"acc_norm_stderr\": 0.0035543339768972517\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620332,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620332\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.725925925925926,\n\
\ \"acc_stderr\": 0.03853254836552003,\n \"acc_norm\": 0.725925925925926,\n\
\ \"acc_norm_stderr\": 0.03853254836552003\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.881578947368421,\n \"acc_stderr\": 0.026293995855474928,\n\
\ \"acc_norm\": 0.881578947368421,\n \"acc_norm_stderr\": 0.026293995855474928\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.78,\n\
\ \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n \
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.024618298195866514,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.024618298195866514\n \
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.02628055093284809,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.02628055093284809\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.53,\n \"acc_stderr\": 0.05016135580465919,\n \
\ \"acc_norm\": 0.53,\n \"acc_norm_stderr\": 0.05016135580465919\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \"acc_norm\": 0.65,\n\
\ \"acc_norm_stderr\": 0.047937248544110196\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.42,\n \"acc_stderr\": 0.04960449637488583,\n \
\ \"acc_norm\": 0.42,\n \"acc_norm_stderr\": 0.04960449637488583\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7514450867052023,\n\
\ \"acc_stderr\": 0.03295304696818318,\n \"acc_norm\": 0.7514450867052023,\n\
\ \"acc_norm_stderr\": 0.03295304696818318\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.5196078431372549,\n \"acc_stderr\": 0.04971358884367405,\n\
\ \"acc_norm\": 0.5196078431372549,\n \"acc_norm_stderr\": 0.04971358884367405\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.8,\n \"acc_stderr\": 0.04020151261036846,\n \"acc_norm\": 0.8,\n\
\ \"acc_norm_stderr\": 0.04020151261036846\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7617021276595745,\n \"acc_stderr\": 0.027851252973889778,\n\
\ \"acc_norm\": 0.7617021276595745,\n \"acc_norm_stderr\": 0.027851252973889778\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5701754385964912,\n\
\ \"acc_stderr\": 0.04657047260594963,\n \"acc_norm\": 0.5701754385964912,\n\
\ \"acc_norm_stderr\": 0.04657047260594963\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.0333333333333333,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.0333333333333333\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.6825396825396826,\n\
\ \"acc_stderr\": 0.02397386199899207,\n \"acc_norm\": 0.6825396825396826,\n\
\ \"acc_norm_stderr\": 0.02397386199899207\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.5793650793650794,\n \"acc_stderr\": 0.04415438226743745,\n\
\ \"acc_norm\": 0.5793650793650794,\n \"acc_norm_stderr\": 0.04415438226743745\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.51,\n\
\ \"acc_stderr\": 0.05024183937956912,\n \"acc_norm\": 0.51,\n \
\ \"acc_norm_stderr\": 0.05024183937956912\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.8935483870967742,\n \"acc_stderr\": 0.017545102951656635,\n\
\ \"acc_norm\": 0.8935483870967742,\n \"acc_norm_stderr\": 0.017545102951656635\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.6502463054187192,\n \"acc_stderr\": 0.03355400904969566,\n \"\
acc_norm\": 0.6502463054187192,\n \"acc_norm_stderr\": 0.03355400904969566\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\"\
: 0.82,\n \"acc_norm_stderr\": 0.038612291966536955\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\"\
: 0.9040404040404041,\n \"acc_stderr\": 0.020984808610047933,\n \"\
acc_norm\": 0.9040404040404041,\n \"acc_norm_stderr\": 0.020984808610047933\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527043,\n\
\ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527043\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8128205128205128,\n \"acc_stderr\": 0.019776601086550036,\n\
\ \"acc_norm\": 0.8128205128205128,\n \"acc_norm_stderr\": 0.019776601086550036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.42592592592592593,\n \"acc_stderr\": 0.030149135601365944,\n \
\ \"acc_norm\": 0.42592592592592593,\n \"acc_norm_stderr\": 0.030149135601365944\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8445378151260504,\n \"acc_stderr\": 0.023536818625398904,\n\
\ \"acc_norm\": 0.8445378151260504,\n \"acc_norm_stderr\": 0.023536818625398904\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5827814569536424,\n \"acc_stderr\": 0.04026141497634611,\n \"\
acc_norm\": 0.5827814569536424,\n \"acc_norm_stderr\": 0.04026141497634611\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9137614678899083,\n \"acc_stderr\": 0.012035597300116245,\n \"\
acc_norm\": 0.9137614678899083,\n \"acc_norm_stderr\": 0.012035597300116245\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6620370370370371,\n \"acc_stderr\": 0.03225941352631295,\n \"\
acc_norm\": 0.6620370370370371,\n \"acc_norm_stderr\": 0.03225941352631295\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9264705882352942,\n \"acc_stderr\": 0.018318855850089678,\n \"\
acc_norm\": 0.9264705882352942,\n \"acc_norm_stderr\": 0.018318855850089678\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.9156118143459916,\n \"acc_stderr\": 0.01809424711647332,\n \
\ \"acc_norm\": 0.9156118143459916,\n \"acc_norm_stderr\": 0.01809424711647332\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\": 0.8702290076335878,\n \"acc_stderr\": 0.029473649496907065,\n\
\ \"acc_norm\": 0.8702290076335878,\n \"acc_norm_stderr\": 0.029473649496907065\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.9008264462809917,\n \"acc_stderr\": 0.027285246312758957,\n \"\
acc_norm\": 0.9008264462809917,\n \"acc_norm_stderr\": 0.027285246312758957\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8796296296296297,\n\
\ \"acc_stderr\": 0.031457038543062504,\n \"acc_norm\": 0.8796296296296297,\n\
\ \"acc_norm_stderr\": 0.031457038543062504\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.8834355828220859,\n \"acc_stderr\": 0.025212327210507108,\n\
\ \"acc_norm\": 0.8834355828220859,\n \"acc_norm_stderr\": 0.025212327210507108\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\": 0.9223300970873787,\n \"acc_stderr\": 0.02650144078476276,\n\
\ \"acc_norm\": 0.9223300970873787,\n \"acc_norm_stderr\": 0.02650144078476276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n\
\ \"acc_stderr\": 0.017893784904018536,\n \"acc_norm\": 0.9188034188034188,\n\
\ \"acc_norm_stderr\": 0.017893784904018536\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.9054916985951469,\n\
\ \"acc_stderr\": 0.01046101533819307,\n \"acc_norm\": 0.9054916985951469,\n\
\ \"acc_norm_stderr\": 0.01046101533819307\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8352601156069365,\n \"acc_stderr\": 0.019971040982442265,\n\
\ \"acc_norm\": 0.8352601156069365,\n \"acc_norm_stderr\": 0.019971040982442265\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.735195530726257,\n\
\ \"acc_stderr\": 0.014756906483260659,\n \"acc_norm\": 0.735195530726257,\n\
\ \"acc_norm_stderr\": 0.014756906483260659\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8594771241830066,\n \"acc_stderr\": 0.019899435463539953,\n\
\ \"acc_norm\": 0.8594771241830066,\n \"acc_norm_stderr\": 0.019899435463539953\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8263665594855305,\n\
\ \"acc_stderr\": 0.02151405158597041,\n \"acc_norm\": 0.8263665594855305,\n\
\ \"acc_norm_stderr\": 0.02151405158597041\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8672839506172839,\n \"acc_stderr\": 0.01887735383957184,\n\
\ \"acc_norm\": 0.8672839506172839,\n \"acc_norm_stderr\": 0.01887735383957184\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6666666666666666,\n \"acc_stderr\": 0.02812163604063989,\n \
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.02812163604063989\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6003911342894394,\n\
\ \"acc_stderr\": 0.01251018163696068,\n \"acc_norm\": 0.6003911342894394,\n\
\ \"acc_norm_stderr\": 0.01251018163696068\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8125,\n \"acc_stderr\": 0.023709788253811766,\n \
\ \"acc_norm\": 0.8125,\n \"acc_norm_stderr\": 0.023709788253811766\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8120915032679739,\n \"acc_stderr\": 0.015803565736776687,\n \
\ \"acc_norm\": 0.8120915032679739,\n \"acc_norm_stderr\": 0.015803565736776687\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7090909090909091,\n\
\ \"acc_stderr\": 0.043502714429232425,\n \"acc_norm\": 0.7090909090909091,\n\
\ \"acc_norm_stderr\": 0.043502714429232425\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.8489795918367347,\n \"acc_stderr\": 0.02292300409473685,\n\
\ \"acc_norm\": 0.8489795918367347,\n \"acc_norm_stderr\": 0.02292300409473685\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8955223880597015,\n\
\ \"acc_stderr\": 0.021628920516700637,\n \"acc_norm\": 0.8955223880597015,\n\
\ \"acc_norm_stderr\": 0.021628920516700637\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.02876234912646613,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.02876234912646613\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.572289156626506,\n\
\ \"acc_stderr\": 0.03851597683718533,\n \"acc_norm\": 0.572289156626506,\n\
\ \"acc_norm_stderr\": 0.03851597683718533\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8771929824561403,\n \"acc_stderr\": 0.02517298435015578,\n\
\ \"acc_norm\": 0.8771929824561403,\n \"acc_norm_stderr\": 0.02517298435015578\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.37821297429620565,\n\
\ \"mc1_stderr\": 0.01697633590754687,\n \"mc2\": 0.5264877146338937,\n\
\ \"mc2_stderr\": 0.014681108343134457\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8279400157853196,\n \"acc_stderr\": 0.010607731615247022\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.49052312357846856,\n \
\ \"acc_stderr\": 0.013770010651168823\n }\n}\n```"
repo_url: https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|arc:challenge|25_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|gsm8k|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hellaswag|10_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-15T00-53-56.888909.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-15T00-53-56.888909.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- '**/details_harness|winogrande|5_2023-12-15T00-53-56.888909.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-15T00-53-56.888909.parquet'
- config_name: results
data_files:
- split: 2023_12_15T00_53_56.888909
path:
- results_2023-12-15T00-53-56.888909.parquet
- split: latest
path:
- results_2023-12-15T00-53-56.888909.parquet
---
# Dataset Card for Evaluation run of kyujinpy/PlatYi-34B-Llama-Q-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [kyujinpy/PlatYi-34B-Llama-Q-v2](https://huggingface.co/kyujinpy/PlatYi-34B-Llama-Q-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated 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_kyujinpy__PlatYi-34B-Llama-Q-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-15T00:53:56.888909](https://huggingface.co/datasets/open-llm-leaderboard/details_kyujinpy__PlatYi-34B-Llama-Q-v2/blob/main/results_2023-12-15T00-53-56.888909.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": {
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"acc_stderr": 0.0282619036411761,
"acc_norm": 0.7647346696571936,
"acc_norm_stderr": 0.028784585052284057,
"mc1": 0.37821297429620565,
"mc1_stderr": 0.01697633590754687,
"mc2": 0.5264877146338937,
"mc2_stderr": 0.014681108343134457
},
"harness|arc:challenge|25": {
"acc": 0.5938566552901023,
"acc_stderr": 0.014351656690097862,
"acc_norm": 0.6109215017064846,
"acc_norm_stderr": 0.014247309976045607
},
"harness|hellaswag|10": {
"acc": 0.6506671977693687,
"acc_stderr": 0.00475784902341196,
"acc_norm": 0.8509261103365864,
"acc_norm_stderr": 0.0035543339768972517
},
"harness|hendrycksTest-abstract_algebra|5": {
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"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.725925925925926,
"acc_norm_stderr": 0.03853254836552003
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.881578947368421,
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"acc_norm_stderr": 0.026293995855474928
},
"harness|hendrycksTest-business_ethics|5": {
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"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8,
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"acc_norm": 0.8,
"acc_norm_stderr": 0.024618298195866514
},
"harness|hendrycksTest-college_biology|5": {
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"acc_norm": 0.8888888888888888,
"acc_norm_stderr": 0.02628055093284809
},
"harness|hendrycksTest-college_chemistry|5": {
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"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.42,
"acc_norm_stderr": 0.04960449637488583
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7514450867052023,
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"acc_norm": 0.7514450867052023,
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},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.5196078431372549,
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"acc_norm": 0.5196078431372549,
"acc_norm_stderr": 0.04971358884367405
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.8,
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"acc_norm": 0.8,
"acc_norm_stderr": 0.04020151261036846
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7617021276595745,
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"acc_norm_stderr": 0.027851252973889778
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5701754385964912,
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"acc_norm": 0.5701754385964912,
"acc_norm_stderr": 0.04657047260594963
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.8,
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"acc_norm": 0.8,
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},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.6825396825396826,
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},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm_stderr": 0.04415438226743745
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.51,
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"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8935483870967742,
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"acc_norm": 0.8935483870967742,
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},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.6502463054187192,
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.82,
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8445378151260504,
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},
"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.9137614678899083,
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"acc_norm_stderr": 0.012035597300116245
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.6620370370370371,
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},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.9264705882352942,
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"acc_norm": 0.9264705882352942,
"acc_norm_stderr": 0.018318855850089678
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.9156118143459916,
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"acc_norm": 0.9156118143459916,
"acc_norm_stderr": 0.01809424711647332
},
"harness|hendrycksTest-human_aging|5": {
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"harness|hendrycksTest-human_sexuality|5": {
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8796296296296297,
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-machine_learning|5": {
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},
"harness|hendrycksTest-management|5": {
"acc": 0.9223300970873787,
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"acc_norm": 0.9223300970873787,
"acc_norm_stderr": 0.02650144078476276
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"harness|hendrycksTest-marketing|5": {
"acc": 0.9188034188034188,
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-moral_disputes|5": {
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},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.8594771241830066,
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"acc_norm_stderr": 0.019899435463539953
},
"harness|hendrycksTest-philosophy|5": {
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},
"harness|hendrycksTest-prehistory|5": {
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},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.6666666666666666,
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},
"harness|hendrycksTest-professional_law|5": {
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},
"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-professional_psychology|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.8489795918367347,
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"acc_norm_stderr": 0.02292300409473685
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.021628920516700637
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.91,
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"acc_norm": 0.91,
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},
"harness|hendrycksTest-virology|5": {
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"acc_norm": 0.572289156626506,
"acc_norm_stderr": 0.03851597683718533
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8771929824561403,
"acc_stderr": 0.02517298435015578,
"acc_norm": 0.8771929824561403,
"acc_norm_stderr": 0.02517298435015578
},
"harness|truthfulqa:mc|0": {
"mc1": 0.37821297429620565,
"mc1_stderr": 0.01697633590754687,
"mc2": 0.5264877146338937,
"mc2_stderr": 0.014681108343134457
},
"harness|winogrande|5": {
"acc": 0.8279400157853196,
"acc_stderr": 0.010607731615247022
},
"harness|gsm8k|5": {
"acc": 0.49052312357846856,
"acc_stderr": 0.013770010651168823
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
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madhaviit/llama10kT1 | madhaviit | 2023-12-12T15:09:23Z | 65 | 0 | [
"region:us"
] | null | 2023-12-12T15:09:21Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 14141408
num_examples: 10000
download_size: 4940492
dataset_size: 14141408
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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arbitropy/kuetdata | arbitropy | 2023-12-17T17:24:19Z | 64 | 0 | [
"region:us"
] | null | 2023-11-25T14:31:59Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: Prompt
dtype: string
- name: Answer
dtype: string
splits:
- name: train
num_bytes: 374868
num_examples: 1574
download_size: 163596
dataset_size: 374868
---
# Dataset Card for "kuetdata"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bastienp/visible-watermark-pita | bastienp | 2023-12-11T14:16:05Z | 64 | 3 | [
"task_categories:object-detection",
"watermak",
"computer-vision",
"object-detection",
"region:us"
] | [
"object-detection"
] | 2023-12-11T08:27:29Z | ---
task_categories:
- object-detection
tags:
- watermak
- computer-vision
- object-detection
---
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Jason773/w1024_part4 | Jason773 | 2023-12-12T07:58:43Z | 64 | 0 | [
"region:us"
] | null | 2023-12-12T07:56:47Z | ---
dataset_info:
features:
- name: target
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
- name: target_img
dtype: image
- name: source_img
dtype: image
splits:
- name: train
num_bytes: 3742119939.542
num_examples: 14986
download_size: 3729248947
dataset_size: 3742119939.542
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Denis431/atomic_data | Denis431 | 2023-12-16T15:46:06Z | 64 | 0 | [
"region:us"
] | null | 2023-12-16T15:34:46Z | ---
dataset_info:
features:
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
- name: embeddings
sequence: float32
splits:
- name: train
num_bytes: 210870
num_examples: 50
download_size: 260325
dataset_size: 210870
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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ChaiML/20231206_chai_prize_reward_model_data | ChaiML | 2023-12-15T10:27:25Z | 63 | 0 | [
"region:us"
] | null | 2023-12-07T00:42:19Z | ---
dataset_info:
features:
- name: input_text
dtype: string
- name: labels
dtype: int64
splits:
- name: train
num_bytes: 147932875
num_examples: 87763
download_size: 83237596
dataset_size: 147932875
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "20231206_chai_prize_reward_model_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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open-llm-leaderboard/details_Felladrin__TinyMistral-248M-SFT-v4 | open-llm-leaderboard | 2023-12-12T04:19:10Z | 63 | 0 | [
"region:us"
] | null | 2023-12-12T04:18:24Z | ---
pretty_name: Evaluation run of Felladrin/TinyMistral-248M-SFT-v4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Felladrin/TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated 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_Felladrin__TinyMistral-248M-SFT-v4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-12T04:15:32.627780](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__TinyMistral-248M-SFT-v4/blob/main/results_2023-12-12T04-15-32.627780.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\": 0.25943849313327083,\n\
\ \"acc_stderr\": 0.03081669921999169,\n \"acc_norm\": 0.26059009573086195,\n\
\ \"acc_norm_stderr\": 0.03163906495514162,\n \"mc1\": 0.20807833537331702,\n\
\ \"mc1_stderr\": 0.014210503473576618,\n \"mc2\": 0.3956118679297354,\n\
\ \"mc2_stderr\": 0.01494264576082401\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.2022184300341297,\n \"acc_stderr\": 0.011737454431872104,\n\
\ \"acc_norm\": 0.24914675767918087,\n \"acc_norm_stderr\": 0.012639407111926435\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.2742481577375025,\n\
\ \"acc_stderr\": 0.004452228541043549,\n \"acc_norm\": 0.2815176259709221,\n\
\ \"acc_norm_stderr\": 0.004488201756642581\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.2,\n \
\ \"acc_stderr\": 0.034554737023254366,\n \"acc_norm\": 0.2,\n \"\
acc_norm_stderr\": 0.034554737023254366\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.2894736842105263,\n \"acc_stderr\": 0.036906779861372814,\n\
\ \"acc_norm\": 0.2894736842105263,\n \"acc_norm_stderr\": 0.036906779861372814\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.24,\n\
\ \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n \
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.2528301886792453,\n \"acc_stderr\": 0.02674989977124124,\n\
\ \"acc_norm\": 0.2528301886792453,\n \"acc_norm_stderr\": 0.02674989977124124\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.22916666666666666,\n\
\ \"acc_stderr\": 0.035146974678623884,\n \"acc_norm\": 0.22916666666666666,\n\
\ \"acc_norm_stderr\": 0.035146974678623884\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.24,\n \"acc_stderr\": 0.04292346959909282,\n \"acc_norm\"\
: 0.24,\n \"acc_norm_stderr\": 0.04292346959909282\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.27167630057803466,\n\
\ \"acc_stderr\": 0.03391750322321659,\n \"acc_norm\": 0.27167630057803466,\n\
\ \"acc_norm_stderr\": 0.03391750322321659\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.04389869956808778,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.04389869956808778\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.21,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.21,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.30638297872340425,\n \"acc_stderr\": 0.03013590647851756,\n\
\ \"acc_norm\": 0.30638297872340425,\n \"acc_norm_stderr\": 0.03013590647851756\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.24561403508771928,\n\
\ \"acc_stderr\": 0.04049339297748141,\n \"acc_norm\": 0.24561403508771928,\n\
\ \"acc_norm_stderr\": 0.04049339297748141\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.22758620689655173,\n \"acc_stderr\": 0.03493950380131184,\n\
\ \"acc_norm\": 0.22758620689655173,\n \"acc_norm_stderr\": 0.03493950380131184\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2724867724867725,\n \"acc_stderr\": 0.02293097307163335,\n \"\
acc_norm\": 0.2724867724867725,\n \"acc_norm_stderr\": 0.02293097307163335\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\": 0.2806451612903226,\n\
\ \"acc_stderr\": 0.025560604721022902,\n \"acc_norm\": 0.2806451612903226,\n\
\ \"acc_norm_stderr\": 0.025560604721022902\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2955665024630542,\n \"acc_stderr\": 0.03210494433751458,\n\
\ \"acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.03210494433751458\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.22,\n \"acc_stderr\": 0.04163331998932269,\n \"acc_norm\"\
: 0.22,\n \"acc_norm_stderr\": 0.04163331998932269\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.23030303030303031,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.23030303030303031,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.29797979797979796,\n \"acc_stderr\": 0.03258630383836557,\n \"\
acc_norm\": 0.29797979797979796,\n \"acc_norm_stderr\": 0.03258630383836557\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.39378238341968913,\n \"acc_stderr\": 0.03526077095548236,\n\
\ \"acc_norm\": 0.39378238341968913,\n \"acc_norm_stderr\": 0.03526077095548236\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.31794871794871793,\n \"acc_stderr\": 0.02361088430892786,\n\
\ \"acc_norm\": 0.31794871794871793,\n \"acc_norm_stderr\": 0.02361088430892786\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2962962962962963,\n \"acc_stderr\": 0.027840811495871937,\n \
\ \"acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.027840811495871937\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.030684737115135363,\n\
\ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.030684737115135363\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.25165562913907286,\n \"acc_stderr\": 0.035433042343899844,\n \"\
acc_norm\": 0.25165562913907286,\n \"acc_norm_stderr\": 0.035433042343899844\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.26238532110091745,\n \"acc_stderr\": 0.01886188502153473,\n \"\
acc_norm\": 0.26238532110091745,\n \"acc_norm_stderr\": 0.01886188502153473\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.39814814814814814,\n \"acc_stderr\": 0.03338473403207401,\n \"\
acc_norm\": 0.39814814814814814,\n \"acc_norm_stderr\": 0.03338473403207401\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.29901960784313725,\n \"acc_stderr\": 0.03213325717373617,\n \"\
acc_norm\": 0.29901960784313725,\n \"acc_norm_stderr\": 0.03213325717373617\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.21518987341772153,\n \"acc_stderr\": 0.02675082699467618,\n \
\ \"acc_norm\": 0.21518987341772153,\n \"acc_norm_stderr\": 0.02675082699467618\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.17040358744394618,\n\
\ \"acc_stderr\": 0.025234593447136165,\n \"acc_norm\": 0.17040358744394618,\n\
\ \"acc_norm_stderr\": 0.025234593447136165\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.19083969465648856,\n \"acc_stderr\": 0.03446513350752599,\n\
\ \"acc_norm\": 0.19083969465648856,\n \"acc_norm_stderr\": 0.03446513350752599\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.09917355371900827,\n \"acc_stderr\": 0.02728524631275895,\n \"\
acc_norm\": 0.09917355371900827,\n \"acc_norm_stderr\": 0.02728524631275895\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.21296296296296297,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.21296296296296297,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.26380368098159507,\n \"acc_stderr\": 0.03462419931615623,\n\
\ \"acc_norm\": 0.26380368098159507,\n \"acc_norm_stderr\": 0.03462419931615623\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.29464285714285715,\n\
\ \"acc_stderr\": 0.043270409325787296,\n \"acc_norm\": 0.29464285714285715,\n\
\ \"acc_norm_stderr\": 0.043270409325787296\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.2912621359223301,\n \"acc_stderr\": 0.044986763205729224,\n\
\ \"acc_norm\": 0.2912621359223301,\n \"acc_norm_stderr\": 0.044986763205729224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23076923076923078,\n\
\ \"acc_stderr\": 0.02760192138141759,\n \"acc_norm\": 0.23076923076923078,\n\
\ \"acc_norm_stderr\": 0.02760192138141759\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.27330779054916987,\n\
\ \"acc_stderr\": 0.015936681062628556,\n \"acc_norm\": 0.27330779054916987,\n\
\ \"acc_norm_stderr\": 0.015936681062628556\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.21965317919075145,\n \"acc_stderr\": 0.022289638852617904,\n\
\ \"acc_norm\": 0.21965317919075145,\n \"acc_norm_stderr\": 0.022289638852617904\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2558659217877095,\n\
\ \"acc_stderr\": 0.014593620923210756,\n \"acc_norm\": 0.2558659217877095,\n\
\ \"acc_norm_stderr\": 0.014593620923210756\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.26143790849673204,\n \"acc_stderr\": 0.025160998214292456,\n\
\ \"acc_norm\": 0.26143790849673204,\n \"acc_norm_stderr\": 0.025160998214292456\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24115755627009647,\n\
\ \"acc_stderr\": 0.02429659403476343,\n \"acc_norm\": 0.24115755627009647,\n\
\ \"acc_norm_stderr\": 0.02429659403476343\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.24382716049382716,\n \"acc_stderr\": 0.0238918795419596,\n\
\ \"acc_norm\": 0.24382716049382716,\n \"acc_norm_stderr\": 0.0238918795419596\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432403,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432403\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.24837027379400262,\n\
\ \"acc_stderr\": 0.011035212598034503,\n \"acc_norm\": 0.24837027379400262,\n\
\ \"acc_norm_stderr\": 0.011035212598034503\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4007352941176471,\n \"acc_stderr\": 0.029768263528933105,\n\
\ \"acc_norm\": 0.4007352941176471,\n \"acc_norm_stderr\": 0.029768263528933105\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25980392156862747,\n \"acc_stderr\": 0.017740899509177798,\n \
\ \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.017740899509177798\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.22727272727272727,\n\
\ \"acc_stderr\": 0.04013964554072775,\n \"acc_norm\": 0.22727272727272727,\n\
\ \"acc_norm_stderr\": 0.04013964554072775\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.33877551020408164,\n \"acc_stderr\": 0.030299506562154185,\n\
\ \"acc_norm\": 0.33877551020408164,\n \"acc_norm_stderr\": 0.030299506562154185\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.27860696517412936,\n\
\ \"acc_stderr\": 0.03170056183497308,\n \"acc_norm\": 0.27860696517412936,\n\
\ \"acc_norm_stderr\": 0.03170056183497308\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.17,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.17,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.1686746987951807,\n\
\ \"acc_stderr\": 0.029152009627856544,\n \"acc_norm\": 0.1686746987951807,\n\
\ \"acc_norm_stderr\": 0.029152009627856544\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.22807017543859648,\n \"acc_stderr\": 0.03218093795602357,\n\
\ \"acc_norm\": 0.22807017543859648,\n \"acc_norm_stderr\": 0.03218093795602357\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.20807833537331702,\n\
\ \"mc1_stderr\": 0.014210503473576618,\n \"mc2\": 0.3956118679297354,\n\
\ \"mc2_stderr\": 0.01494264576082401\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.505130228887135,\n \"acc_stderr\": 0.014051745961790516\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|arc:challenge|25_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|gsm8k|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hellaswag|10_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-15-32.627780.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T04-15-32.627780.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- '**/details_harness|winogrande|5_2023-12-12T04-15-32.627780.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-12T04-15-32.627780.parquet'
- config_name: results
data_files:
- split: 2023_12_12T04_15_32.627780
path:
- results_2023-12-12T04-15-32.627780.parquet
- split: latest
path:
- results_2023-12-12T04-15-32.627780.parquet
---
# Dataset Card for Evaluation run of Felladrin/TinyMistral-248M-SFT-v4
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Felladrin/TinyMistral-248M-SFT-v4](https://huggingface.co/Felladrin/TinyMistral-248M-SFT-v4) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated 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_Felladrin__TinyMistral-248M-SFT-v4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-12T04:15:32.627780](https://huggingface.co/datasets/open-llm-leaderboard/details_Felladrin__TinyMistral-248M-SFT-v4/blob/main/results_2023-12-12T04-15-32.627780.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
{
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},
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] | [
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] |
open-llm-leaderboard/details_uukuguy__speechless-mistral-six-in-one-7b-orth-1.0 | open-llm-leaderboard | 2023-12-13T11:16:55Z | 63 | 0 | [
"region:us"
] | null | 2023-12-13T11:16:12Z | ---
pretty_name: Evaluation run of uukuguy/speechless-mistral-six-in-one-7b-orth-1.0
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [uukuguy/speechless-mistral-six-in-one-7b-orth-1.0](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b-orth-1.0)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated 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_uukuguy__speechless-mistral-six-in-one-7b-orth-1.0\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-13T11:13:22.485134](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-six-in-one-7b-orth-1.0/blob/main/results_2023-12-13T11-13-22.485134.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\": 0.23196194129343728,\n\
\ \"acc_stderr\": 0.029934654752561563,\n \"acc_norm\": 0.2314240573187148,\n\
\ \"acc_norm_stderr\": 0.03071122006512167,\n \"mc1\": 1.0,\n \
\ \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n \"mc2_stderr\": NaN\n\
\ },\n \"harness|arc:challenge|25\": {\n \"acc\": 0.22696245733788395,\n\
\ \"acc_stderr\": 0.012240491536132861,\n \"acc_norm\": 0.22696245733788395,\n\
\ \"acc_norm_stderr\": 0.012240491536132861\n },\n \"harness|hellaswag|10\"\
: {\n \"acc\": 0.2504481179047998,\n \"acc_stderr\": 0.004323856300539177,\n\
\ \"acc_norm\": 0.2504481179047998,\n \"acc_norm_stderr\": 0.004323856300539177\n\
\ },\n \"harness|hendrycksTest-abstract_algebra|5\": {\n \"acc\": 0.22,\n\
\ \"acc_stderr\": 0.04163331998932268,\n \"acc_norm\": 0.22,\n \
\ \"acc_norm_stderr\": 0.04163331998932268\n },\n \"harness|hendrycksTest-anatomy|5\"\
: {\n \"acc\": 0.18518518518518517,\n \"acc_stderr\": 0.03355677216313142,\n\
\ \"acc_norm\": 0.18518518518518517,\n \"acc_norm_stderr\": 0.03355677216313142\n\
\ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.17763157894736842,\n\
\ \"acc_stderr\": 0.031103182383123398,\n \"acc_norm\": 0.17763157894736842,\n\
\ \"acc_norm_stderr\": 0.031103182383123398\n },\n \"harness|hendrycksTest-business_ethics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.21509433962264152,\n\
\ \"acc_stderr\": 0.02528839450289137,\n \"acc_norm\": 0.21509433962264152,\n\
\ \"acc_norm_stderr\": 0.02528839450289137\n },\n \"harness|hendrycksTest-college_biology|5\"\
: {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n\
\ \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n\
\ },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\":\
\ 0.2,\n \"acc_stderr\": 0.04020151261036845,\n \"acc_norm\": 0.2,\n\
\ \"acc_norm_stderr\": 0.04020151261036845\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.21,\n\
\ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \
\ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.20809248554913296,\n \"acc_stderr\": 0.030952890217749874,\n\
\ \"acc_norm\": 0.20809248554913296,\n \"acc_norm_stderr\": 0.030952890217749874\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n\
\ \"acc_stderr\": 0.04092563958237654,\n \"acc_norm\": 0.21568627450980393,\n\
\ \"acc_norm_stderr\": 0.04092563958237654\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\
\ 0.26382978723404255,\n \"acc_stderr\": 0.028809989854102973,\n \"\
acc_norm\": 0.26382978723404255,\n \"acc_norm_stderr\": 0.028809989854102973\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813365,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813365\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2413793103448276,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.2413793103448276,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.20899470899470898,\n \"acc_stderr\": 0.02094048156533486,\n \"\
acc_norm\": 0.20899470899470898,\n \"acc_norm_stderr\": 0.02094048156533486\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04040610178208841,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04040610178208841\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.1774193548387097,\n \"acc_stderr\": 0.02173254068932927,\n \"\
acc_norm\": 0.1774193548387097,\n \"acc_norm_stderr\": 0.02173254068932927\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.15270935960591134,\n \"acc_stderr\": 0.02530890453938063,\n \"\
acc_norm\": 0.15270935960591134,\n \"acc_norm_stderr\": 0.02530890453938063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.17676767676767677,\n \"acc_stderr\": 0.027178752639044915,\n \"\
acc_norm\": 0.17676767676767677,\n \"acc_norm_stderr\": 0.027178752639044915\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.19689119170984457,\n \"acc_stderr\": 0.028697873971860664,\n\
\ \"acc_norm\": 0.19689119170984457,\n \"acc_norm_stderr\": 0.028697873971860664\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.20256410256410257,\n \"acc_stderr\": 0.020377660970371372,\n\
\ \"acc_norm\": 0.20256410256410257,\n \"acc_norm_stderr\": 0.020377660970371372\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2111111111111111,\n \"acc_stderr\": 0.024882116857655075,\n \
\ \"acc_norm\": 0.2111111111111111,\n \"acc_norm_stderr\": 0.024882116857655075\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.1986754966887417,\n \"acc_stderr\": 0.03257847384436776,\n \"\
acc_norm\": 0.1986754966887417,\n \"acc_norm_stderr\": 0.03257847384436776\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.1926605504587156,\n \"acc_stderr\": 0.016909276884936094,\n \"\
acc_norm\": 0.1926605504587156,\n \"acc_norm_stderr\": 0.016909276884936094\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.1527777777777778,\n \"acc_stderr\": 0.024536326026134224,\n \"\
acc_norm\": 0.1527777777777778,\n \"acc_norm_stderr\": 0.024536326026134224\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25,\n \"acc_stderr\": 0.03039153369274154,\n \"acc_norm\": 0.25,\n\
\ \"acc_norm_stderr\": 0.03039153369274154\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.270042194092827,\n \"acc_stderr\": 0.028900721906293426,\n\
\ \"acc_norm\": 0.270042194092827,\n \"acc_norm_stderr\": 0.028900721906293426\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.31390134529147984,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.31390134529147984,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.2595419847328244,\n \"acc_stderr\": 0.03844876139785271,\n\
\ \"acc_norm\": 0.2595419847328244,\n \"acc_norm_stderr\": 0.03844876139785271\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.2396694214876033,\n \"acc_stderr\": 0.03896878985070417,\n \"\
acc_norm\": 0.2396694214876033,\n \"acc_norm_stderr\": 0.03896878985070417\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25925925925925924,\n\
\ \"acc_stderr\": 0.042365112580946336,\n \"acc_norm\": 0.25925925925925924,\n\
\ \"acc_norm_stderr\": 0.042365112580946336\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22085889570552147,\n \"acc_stderr\": 0.032591773927421776,\n\
\ \"acc_norm\": 0.22085889570552147,\n \"acc_norm_stderr\": 0.032591773927421776\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.3125,\n\
\ \"acc_stderr\": 0.043994650575715215,\n \"acc_norm\": 0.3125,\n\
\ \"acc_norm_stderr\": 0.043994650575715215\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.17475728155339806,\n \"acc_stderr\": 0.037601780060266224,\n\
\ \"acc_norm\": 0.17475728155339806,\n \"acc_norm_stderr\": 0.037601780060266224\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2905982905982906,\n\
\ \"acc_stderr\": 0.02974504857267404,\n \"acc_norm\": 0.2905982905982906,\n\
\ \"acc_norm_stderr\": 0.02974504857267404\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.23754789272030652,\n\
\ \"acc_stderr\": 0.015218733046150193,\n \"acc_norm\": 0.23754789272030652,\n\
\ \"acc_norm_stderr\": 0.015218733046150193\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.023267528432100174,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.023267528432100174\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.23798882681564246,\n\
\ \"acc_stderr\": 0.014242630070574915,\n \"acc_norm\": 0.23798882681564246,\n\
\ \"acc_norm_stderr\": 0.014242630070574915\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.023929155517351284,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.023929155517351284\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.1864951768488746,\n\
\ \"acc_stderr\": 0.02212243977248077,\n \"acc_norm\": 0.1864951768488746,\n\
\ \"acc_norm_stderr\": 0.02212243977248077\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.21604938271604937,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.21604938271604937,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.23404255319148937,\n \"acc_stderr\": 0.025257861359432417,\n \
\ \"acc_norm\": 0.23404255319148937,\n \"acc_norm_stderr\": 0.025257861359432417\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2457627118644068,\n\
\ \"acc_stderr\": 0.010996156635142692,\n \"acc_norm\": 0.2457627118644068,\n\
\ \"acc_norm_stderr\": 0.010996156635142692\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.18382352941176472,\n \"acc_stderr\": 0.023529242185193106,\n\
\ \"acc_norm\": 0.18382352941176472,\n \"acc_norm_stderr\": 0.023529242185193106\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.25,\n \"acc_stderr\": 0.01751781884501444,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.01751781884501444\n },\n \"harness|hendrycksTest-public_relations|5\"\
: {\n \"acc\": 0.21818181818181817,\n \"acc_stderr\": 0.03955932861795833,\n\
\ \"acc_norm\": 0.21818181818181817,\n \"acc_norm_stderr\": 0.03955932861795833\n\
\ },\n \"harness|hendrycksTest-security_studies|5\": {\n \"acc\": 0.18775510204081633,\n\
\ \"acc_stderr\": 0.02500025603954621,\n \"acc_norm\": 0.18775510204081633,\n\
\ \"acc_norm_stderr\": 0.02500025603954621\n },\n \"harness|hendrycksTest-sociology|5\"\
: {\n \"acc\": 0.24378109452736318,\n \"acc_stderr\": 0.03036049015401465,\n\
\ \"acc_norm\": 0.24378109452736318,\n \"acc_norm_stderr\": 0.03036049015401465\n\
\ },\n \"harness|hendrycksTest-us_foreign_policy|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-virology|5\"\
: {\n \"acc\": 0.28313253012048195,\n \"acc_stderr\": 0.03507295431370518,\n\
\ \"acc_norm\": 0.28313253012048195,\n \"acc_norm_stderr\": 0.03507295431370518\n\
\ },\n \"harness|hendrycksTest-world_religions|5\": {\n \"acc\": 0.3216374269005848,\n\
\ \"acc_stderr\": 0.03582529442573122,\n \"acc_norm\": 0.3216374269005848,\n\
\ \"acc_norm_stderr\": 0.03582529442573122\n },\n \"harness|truthfulqa:mc|0\"\
: {\n \"mc1\": 1.0,\n \"mc1_stderr\": 0.0,\n \"mc2\": NaN,\n\
\ \"mc2_stderr\": NaN\n },\n \"harness|winogrande|5\": {\n \"\
acc\": 0.4956590370955012,\n \"acc_stderr\": 0.014051956064076911\n },\n\
\ \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n\
\ }\n}\n```"
repo_url: https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b-orth-1.0
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: [email protected]
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|arc:challenge|25_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|gsm8k|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hellaswag|10_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-13-22.485134.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-13T11-13-22.485134.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- '**/details_harness|winogrande|5_2023-12-13T11-13-22.485134.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-13T11-13-22.485134.parquet'
- config_name: results
data_files:
- split: 2023_12_13T11_13_22.485134
path:
- results_2023-12-13T11-13-22.485134.parquet
- split: latest
path:
- results_2023-12-13T11-13-22.485134.parquet
---
# Dataset Card for Evaluation run of uukuguy/speechless-mistral-six-in-one-7b-orth-1.0
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [uukuguy/speechless-mistral-six-in-one-7b-orth-1.0](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b-orth-1.0) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated 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_uukuguy__speechless-mistral-six-in-one-7b-orth-1.0",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-13T11:13:22.485134](https://huggingface.co/datasets/open-llm-leaderboard/details_uukuguy__speechless-mistral-six-in-one-7b-orth-1.0/blob/main/results_2023-12-13T11-13-22.485134.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": 0.23196194129343728,
"acc_stderr": 0.029934654752561563,
"acc_norm": 0.2314240573187148,
"acc_norm_stderr": 0.03071122006512167,
"mc1": 1.0,
"mc1_stderr": 0.0,
"mc2": NaN,
"mc2_stderr": NaN
},
"harness|arc:challenge|25": {
"acc": 0.22696245733788395,
"acc_stderr": 0.012240491536132861,
"acc_norm": 0.22696245733788395,
"acc_norm_stderr": 0.012240491536132861
},
"harness|hellaswag|10": {
"acc": 0.2504481179047998,
"acc_stderr": 0.004323856300539177,
"acc_norm": 0.2504481179047998,
"acc_norm_stderr": 0.004323856300539177
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.22,
"acc_stderr": 0.04163331998932268,
"acc_norm": 0.22,
"acc_norm_stderr": 0.04163331998932268
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.18518518518518517,
"acc_stderr": 0.03355677216313142,
"acc_norm": 0.18518518518518517,
"acc_norm_stderr": 0.03355677216313142
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.21509433962264152,
"acc_stderr": 0.02528839450289137,
"acc_norm": 0.21509433962264152,
"acc_norm_stderr": 0.02528839450289137
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.2569444444444444,
"acc_stderr": 0.03653946969442099,
"acc_norm": 0.2569444444444444,
"acc_norm_stderr": 0.03653946969442099
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.26,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.26,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.20809248554913296,
"acc_stderr": 0.030952890217749874,
"acc_norm": 0.20809248554913296,
"acc_norm_stderr": 0.030952890217749874
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.21568627450980393,
"acc_stderr": 0.04092563958237654,
"acc_norm": 0.21568627450980393,
"acc_norm_stderr": 0.04092563958237654
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.26382978723404255,
"acc_stderr": 0.028809989854102973,
"acc_norm": 0.26382978723404255,
"acc_norm_stderr": 0.028809989854102973
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813365,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813365
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2413793103448276,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.2413793103448276,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.20899470899470898,
"acc_stderr": 0.02094048156533486,
"acc_norm": 0.20899470899470898,
"acc_norm_stderr": 0.02094048156533486
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04040610178208841,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04040610178208841
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.1774193548387097,
"acc_stderr": 0.02173254068932927,
"acc_norm": 0.1774193548387097,
"acc_norm_stderr": 0.02173254068932927
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.15270935960591134,
"acc_stderr": 0.02530890453938063,
"acc_norm": 0.15270935960591134,
"acc_norm_stderr": 0.02530890453938063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.21818181818181817,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.17676767676767677,
"acc_stderr": 0.027178752639044915,
"acc_norm": 0.17676767676767677,
"acc_norm_stderr": 0.027178752639044915
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.19689119170984457,
"acc_stderr": 0.028697873971860664,
"acc_norm": 0.19689119170984457,
"acc_norm_stderr": 0.028697873971860664
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.20256410256410257,
"acc_stderr": 0.020377660970371372,
"acc_norm": 0.20256410256410257,
"acc_norm_stderr": 0.020377660970371372
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2111111111111111,
"acc_stderr": 0.024882116857655075,
"acc_norm": 0.2111111111111111,
"acc_norm_stderr": 0.024882116857655075
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.21008403361344538,
"acc_stderr": 0.026461398717471874,
"acc_norm": 0.21008403361344538,
"acc_norm_stderr": 0.026461398717471874
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.1986754966887417,
"acc_stderr": 0.03257847384436776,
"acc_norm": 0.1986754966887417,
"acc_norm_stderr": 0.03257847384436776
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.1926605504587156,
"acc_stderr": 0.016909276884936094,
"acc_norm": 0.1926605504587156,
"acc_norm_stderr": 0.016909276884936094
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.1527777777777778,
"acc_stderr": 0.024536326026134224,
"acc_norm": 0.1527777777777778,
"acc_norm_stderr": 0.024536326026134224
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25,
"acc_stderr": 0.03039153369274154,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03039153369274154
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```
## Dataset Details
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[More Information Needed] | [
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habanoz/lima-chat-format | habanoz | 2023-12-12T19:29:37Z | 60 | 0 | [
"region:us"
] | null | 2023-12-12T19:02:55Z | ---
dataset_info:
features:
- name: category
dtype: string
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 2929351.0
num_examples: 1030
download_size: 1681823
dataset_size: 2929351.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This dataset is a copy of [GAIR/lima](https://huggingface.co/datasets/GAIR/lima) transformed to work with hugging face chat templates.
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dendimaki/apeiron | dendimaki | 2023-12-15T03:24:01Z | 60 | 0 | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"license:openrail",
"code",
"region:us"
] | [
"text-classification"
] | 2023-12-14T12:02:18Z | ---
license: openrail
task_categories:
- text-classification
tags:
- code
pretty_name: apeiron
size_categories:
- 1K<n<10K
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] |
ScandEval/germanquad-mini | ScandEval | 2023-12-12T15:15:49Z | 59 | 0 | [
"region:us"
] | null | 2023-12-12T15:15:44Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int32
- name: text
sequence: string
splits:
- name: train
num_bytes: 1821859
num_examples: 1024
- name: val
num_bytes: 436338
num_examples: 256
- name: test
num_bytes: 3712894
num_examples: 2048
download_size: 3238348
dataset_size: 5971091
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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] |
memray/FacetSum | memray | 2023-12-17T03:46:51Z | 58 | 0 | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-sa-4.0",
"region:us"
] | [
"summarization"
] | 2023-10-02T04:36:10Z | ---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
language:
- en
size_categories:
- 10K<n<100K
---
## FacetSum dataset
**Due to the strict copyright restriction, the dataset is only available for non-commercial research use ONLY.**
**Currently it requires manual approval for access. Please send an email to [email protected], stating (1) Huggingface account name; (2) institute/company name; (3) the purpose of using this dataset.**
### FacetSum dataset
Paper: ACL 2021, [Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents](https://aclanthology.org/2021.acl-short.137.pdf)
Over 60k Emerald journal articles (long documents) with faceted summaries (purpose, method, findings, and value).
Train: 46,289 / Dev: 6,000 / Test: 6,000 / OA-Test: 2,243
The code for scraping the Emerald full-text data can be found here: https://github.com/hfthair/emerald_crawler/
```
@inproceedings{meng2021facetsum,
title={Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents},
author={Meng, Rui and Thaker, Khushboo and Zhang, Lei and Dong, Yue and Yuan, Xingdi and Wang, Tong and He, Daqing},
booktitle={Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
pages={1080--1089},
year={2021}
}
``` | [
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Locutusque/inst_mix_v2_top_100k | Locutusque | 2023-12-14T01:56:46Z | 58 | 2 | [
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:en",
"region:us"
] | [
"text-generation",
"conversational",
"question-answering"
] | 2023-12-11T04:14:41Z | ---
task_categories:
- text-generation
- conversational
- question-answering
language:
- en
size_categories:
- 100K<n<1M
---
I deep-cleaned a version of the Locutusque/InstructMix-V2 dataset, which was originally quite large. To manage this, I sampled 123,590 examples from the dataset and then removed any OpenAI refusals, referencing the refusals list from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered. The resulting dataset now contains 106,149 cleaned examples. | [
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] |
balzanilo/dialogs-emotions-dataset | balzanilo | 2023-12-15T08:43:49Z | 58 | 0 | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"region:us"
] | [
"text-classification"
] | 2023-12-11T15:20:54Z | ---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- text-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1763035
num_examples: 27764
- name: test
num_bytes: 223518
num_examples: 3558
- name: eval
num_bytes: 233130
num_examples: 3678
download_size: 1097871
dataset_size: 2219683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
---
# Emotions mapping
This table presents a mapping between numerical Emotion IDs and their corresponding Emotion Names used during model training. The emotions are categorized into seven distinct categories, each identified by a unique numerical identifier.
| Emotion ID | Emotion Name |
|------------|--------------|
| 0 | Neutral |
| 1 | Joy |
| 2 | Surprise |
| 3 | Disgust |
| 4 | Fear |
| 5 | Sadness |
| 6 | Anger | | [
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ScandEval/germeval-mini | ScandEval | 2023-12-11T15:00:57Z | 57 | 0 | [
"region:us"
] | null | 2023-12-11T15:00:52Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: labels
sequence: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 417548
num_examples: 1024
- name: val
num_bytes: 105665
num_examples: 256
- name: test
num_bytes: 416033
num_examples: 1024
download_size: 450764
dataset_size: 939246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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TeamUNIVA/mathgen | TeamUNIVA | 2023-12-17T18:06:23Z | 57 | 0 | [
"region:us"
] | null | 2023-12-17T16:59:34Z | ---
dataset_info:
features:
- name: question_filename
dtype: string
- name: id
dtype: string
- name: question_text
dtype: string
- name: question_bbox
dtype: string
- name: answer_text
dtype: string
- name: answer_bbox
dtype: string
- name: topic_number
dtype: string
- name: group_datas
sequence: string
splits:
- name: test
num_bytes: 20647882
num_examples: 1000
- name: valid
num_bytes: 181195373
num_examples: 8320
- name: train
num_bytes: 1599243382
num_examples: 74883
download_size: 698312116
dataset_size: 1801086637
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: valid
path: data/valid-*
- split: train
path: data/train-*
---
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m-a-p/COIG-CQIA | m-a-p | 2023-12-13T23:04:10Z | 56 | 33 | [
"task_categories:question-answering",
"task_categories:text-classification",
"task_categories:text-generation",
"task_categories:conversational",
"task_categories:text2text-generation",
"size_categories:10K<n<100K",
"language:zh",
"arxiv:2307.09705",
"region:us"
] | [
"question-answering",
"text-classification",
"text-generation",
"conversational",
"text2text-generation"
] | 2023-12-04T07:04:37Z | ---
task_categories:
- question-answering
- text-classification
- text-generation
- conversational
- text2text-generation
language:
- zh
size_categories:
- 10K<n<100K
---
# COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
欢迎来到COIG-CQIA，COIG-CQIA全称为**Chinese Open Instruction Generalist - Quality is All You Need**， 是一个开源的高质量指令微调数据集，旨在为中文NLP社区提供**高质量**且符合**人类交互行为**的指令微调数据。COIG-CQIA以中文互联网获取到的问答及文章作为原始数据，经过深度清洗、重构及人工审核构建而成。本项目受*LIMA: Less Is More for Alignment*等研究启发，使用少量高质量的数据即可让大语言模型学习到人类交互行为，因此在数据构建中我们十分注重数据的来源、质量与多样性，数据集详情请见数据介绍以及我们接下来的论文。
Welcome to the COIG-CQIA project page. COIG-CQIA stands for **Chinese Open Instruction Generalist - Quality is All You Need**, a high-quality Chinese instruction fine-tuning dataset. This dataset is designed to provide the Chinese NLP community with **high-quality** and **human interaction-aligned** instruction fine-tuning data.Inspired by studies like *LIMA: Less Is More for Alignment*, COIG-CQIA focuses on creating a dataset from Chinese internet sources including Q&A and articles. These are deeply cleansed, restructured, and manually reviewed to ensure quality, diversity, and relevance.
- **Curated by:** 来自零一万物、中科院深圳先进技术研究院，和M-A-P等机构的研究者们。
- **Language(s) (NLP):** 本数据集主要语言为中文。
- **License:** [More Information Needed]
本数据集当前为v0.1版本，如果您在使用中发现数据集存在问题或者有可以改进的地方，欢迎留言交流！
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
本数据集适用于指令微调，训练模型具备响应指令的能力。
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## 数据
### 数据格式
```json
{
"instruction": "示例问题或者指令。",
"input": "示例问题或指令的补充。",
"output": "对输入的回复。",
"task_type": {
"major": ["问答"],
"minor": ["百科问答"]
},
"domain": ["百科", "医疗"],
"answer_from": "human",
"human_verified": true,
"copyright": "作者及版权信息。",
}
```
### 数据字段
- `instruction`: 用于输入的指令或者问题。
- `input`: 问题或指令的补充内容。
- `output`: 输入对应的回答。
- `task_type`: 表示该数据所属的主要任务类型和细分任务类型。
- `domain`: 该数据所属领域。
- `answer_from`: 回答是人类撰写的还是大模型撰写的，本数据集中绝大部分是由人类撰写的回答，少部分由大模型生成（经过了人工验证）。
- `human_verified`: 该数据是否又人类核验过。
- `copyright`: 包括该数据的版权信息，包括作者等。
当前版本的数据字段中仍有不完善的部分，我们将在近期的下一版本中补充。
### 数据详情
<details>
<summary><b>社交媒体&论坛</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 知乎 | 8837 | [[网址链接]](https://www.zhihu.com/) | 经过多阶段的数据质量筛选和人工验证。 |
| 豆瓣 | 3132 | [[网址链接]](https://www.douban.com/) | 人工撰写多样的prompt模板构造而成。 |
| 小红书 | 1508 | [[网址链接]](https://www.xiaohongshu.com/explore) | 人工撰写多样的prompt模板构造而成。 |
| Segmentfault | 458 | [[网址链接]](https://segmentfault.com/) | 规则方式清洗与筛选，并经过人工验证。 |
| **总量** | **13935** | - | - |
</details>
<details>
<summary><b>通用百科</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 百科文章 | 980 | 从网络中收集。[[网址链接]](https://10why.net/) [[网址链接]](https://www.eetree.cn/wiki/eebaike) [[网址链接]](https://www.nongyie.com/) [[网址链接]](https://www.gkket.com/gkwk/) | 规则方式清洗与筛选，并经过人工验证。 |
| 中国大百科全书 | 1706 | [[网址链接]](https://www.zgbk.com/) | 人工撰写多样的prompt模板构造而成。 |
| wikiHow中文 | 1876 | [[网址链接]](https://zh.wikihow.com/首页)&[[公开数据集]](https://github.com/esbatmop/MNBVC/tree/main) | 规则方式清洗与筛选。 |
| **总量** | **4571** | - | - |
</details>
</details>
<details>
<summary><b>通用NLP任务</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| COIG-PC-Core | 3000 | [[Open Dataset]](https://huggingface.co/datasets/BAAI/COIG-PC-core) | 人工验证数据质量。 |
| **总量** | **3000** | - | - |
</details>
<details>
<summary><b>考试&试题</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 高考&中考 | 2000 | [[公开数据集]](https://huggingface.co/datasets/BAAI/COIG) | - |
| 研究生入学考试 | 475 | 从网络中收集 | 规则方式清洗与筛选。 |
| 逻辑推理题 | 422 | 从网络中收集 | 规则方式清洗与筛选。 |
| **总量** | **2897** | - | - |
</details>
<details>
<summary><b>人类价值观</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 100poison | 906 | [[公开数据集]](https://modelscope.cn/datasets/damo/100PoisonMpts/summary) | - |
| COIG-human-value | 101 | [[公开数据集]](https://huggingface.co/datasets/BAAI/COIG) | 经人工审核数据质量 |
| **总量** | **1007** | - | - |
</details>
<details>
<summary><b>中国传统文化</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 中华传统文化试题 | 232 | 从网络中收集 | 规则方式清洗与筛选，并经过人工验证。 |
| 成语释义 | 112 | [[公开数据集]](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) | 规则方式清洗与筛选，并经过人工验证。 |
| 古诗词撰写 | 47 | [[公开数据集]](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) | 规则方式清洗与筛选，并经过人工验证。 |
| 文言文互译 | 112 | [[公开数据集]](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) | 规则方式清洗与筛选，并经过人工验证。 |
| **总量** | **503** | - | - |
</details>
<details>
<summary><b>金融&经管领域</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| MBA百科 | 10689 | [[网址链接]](https://wiki.mbalib.com/wiki/首页) | 人工撰写多样的prompt模板构造而成。 |
| 金融NLP任务 | 600 | [[公开数据集]](https://huggingface.co/datasets/BAAI/COIG-PC) | 人工核验数据质量 |
| **总量** | **11289** | - | - |
</details>
<details>
<summary><b>医疗领域</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 医疗百科 | 8351 | [[网址链接]](www.baikemy.com) | 人工撰写多样的prompt模板构造而成。 |
| 医疗文章 | 186 | [[网址链接]](https://51zyzy.com/article/list.html) [[网址链接]](https://baobao.baidu.com/dailyjnl/list/13.html) | 规则方式清洗与筛选。 |
| **总量** | **8537** | - | - |
</details>
<details>
<summary><b>法律领域</b></summary>
| 类别 | 数量 | 来源 | 构造方式 |
| ----------------- | -------- | ------ | --------------------------------------- |
| 法律研究生入学考试 | 2645 | 从网络中收集 | 规则方式清洗与筛选。 |
| **总量** | **2645** | - | - |
</details>
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
如果本项目为您的研究带来了帮助，欢迎引用！
```bibtex
@misc{COIG-CQIA,
author = {},
title = {COIG-CQIA: Quality is All you need for Chinese Instruction Fine-tuning},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/paralym/COIG-CQIA}},
}
```
本数据集中也包含了以下公开数据：
```bibtex
@article{zhang2023chinese,
title={Chinese open instruction generalist: A preliminary release},
author={Zhang, Ge and Shi, Yemin and Liu, Ruibo and Yuan, Ruibin and Li, Yizhi and Dong, Siwei and Shu, Yu and Li, Zhaoqun and Wang, Zekun and Lin, Chenghua and others},
journal={arXiv preprint arXiv:2304.07987},
year={2023}
}
@misc{Firefly,
author = {Jianxin Yang},
title = {Firefly(流萤): 中文对话式大语言模型},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/yangjianxin1/Firefly}},
}
@misc{xu2023cvalues,
title={CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility},
author={Guohai Xu and Jiayi Liu and Ming Yan and Haotian Xu and Jinghui Si and Zhuoran Zhou and Peng Yi and Xing Gao and Jitao Sang and Rong Zhang and Ji Zhang and Chao Peng and Fei Huang and Jingren Zhou},
year={2023},
eprint={2307.09705},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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-0.4645143747329712
] |
sproos/scifact-es | sproos | 2023-12-14T17:53:01Z | 56 | 0 | [
"region:us"
] | null | 2023-12-14T17:20:50Z | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 139085
num_examples: 1109
- name: corpus
num_bytes: 9174934
num_examples: 5183
download_size: 76742
dataset_size: 9314019
---
# Dataset Card for "scifact-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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seonglae/resrer-nq | seonglae | 2023-12-14T06:55:22Z | 55 | 0 | [
"region:us"
] | null | 2023-11-22T12:12:18Z | ---
dataset_info:
features:
- name: document_text
dtype: string
- name: long_answer_candidates
list:
- name: end_token
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: question_text
dtype: string
- name: annotations
list:
- name: annotation_id
dtype: float64
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_token
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
list:
- name: end_token
dtype: int64
- name: start_token
dtype: int64
- name: yes_no_answer
dtype: string
- name: document_url
dtype: string
- name: example_id
dtype: int64
- name: long_answer_text
dtype: string
- name: short_answer_text
dtype: string
- name: split_id
dtype: string
- name: answer_exist_chunk
dtype: bool
- name: summarization_text
dtype: string
splits:
- name: train
num_bytes: 497862929.4517183
num_examples: 55113
download_size: 121306017
dataset_size: 497862929.4517183
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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erbacher/perso-proactive-convs-sample | erbacher | 2023-12-12T12:45:43Z | 55 | 0 | [
"region:us"
] | null | 2023-12-08T10:29:04Z | ---
dataset_info:
- config_name: default
features:
- name: id
dtype: int64
- name: uid
dtype: int64
- name: user
dtype: string
- name: conversation
list:
- name: assistant
dtype: string
- name: turn_id
dtype: int64
- name: user
dtype: string
- name: __index_level_0__
dtype: int64
- name: queries
list:
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sequence: string
- name: turnid
dtype: int64
- name: messages
list:
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dtype: string
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dtype: string
splits:
- name: train
num_bytes: 76784028.64190687
num_examples: 7016
- name: test
num_bytes: 2188826.358093126
num_examples: 200
download_size: 36598806
dataset_size: 78972855.0
- config_name: with_queries
features:
- name: id
dtype: int64
- name: uid
dtype: int64
- name: user
dtype: string
- name: conversation
list:
- name: assistant
dtype: string
- name: turn_id
dtype: int64
- name: user
dtype: string
- name: __index_level_0__
dtype: int64
- name: queries
list:
- name: queries
sequence: string
- name: turnid
dtype: int64
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 79043970
num_examples: 7224
download_size: 36592475
dataset_size: 79043970
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: with_queries
data_files:
- split: train
path: with_queries/train-*
---
# Dataset Card for "perso-proactive-convs-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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SAGI-1/SYMBOLIC_DATA_FACTS_2_FOL | SAGI-1 | 2023-12-11T10:32:57Z | 54 | 0 | [
"region:us"
] | null | 2023-11-29T11:13:23Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 58739284
num_examples: 125203
download_size: 22403574
dataset_size: 58739284
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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chathuru/SplunkAttackRangeAlerts | chathuru | 2023-12-17T12:28:50Z | 54 | 0 | [
"task_categories:text-classification",
"Cybersecurity",
"Network Intrusion Detection",
"SIEM Alerts",
"region:us"
] | [
"text-classification"
] | 2023-12-02T14:04:51Z | ---
task_categories:
- text-classification
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 18353
num_examples: 100
- name: test
num_bytes: 2777
num_examples: 20
download_size: 8501
dataset_size: 21130
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- Cybersecurity
- Network Intrusion Detection
- SIEM Alerts
---
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IlyaGusev/pippa_ru | IlyaGusev | 2023-12-16T22:08:48Z | 54 | 0 | [
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:ru",
"license:apache-2.0",
"not-for-all-audiences",
"conversational",
"roleplay",
"region:us"
] | [
"conversational"
] | 2023-12-03T21:44:46Z | ---
language:
- ru
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- conversational
pretty_name: PIPPA Russian
tags:
- not-for-all-audiences
- conversational
- roleplay
viewer: true
dataset_info:
config_name: pippa_ru
features:
- name: bot_name
dtype: string
- name: bot_definitions
dtype: string
- name: bot_description
dtype: string
- name: translation_model
dtype: string
- name: gpt_35_turbo_result
dtype: string
- name: gpt_35_turbo_explanation
dtype: string
- name: conversation
sequence:
- name: message
dtype: string
- name: is_human
dtype: bool
- name: orig_bot_definitions
dtype: string
- name: orig_bot_description
dtype: string
- name: orig_conversation
sequence:
- name: message
dtype: string
- name: is_human
dtype: bool
splits:
- name: train
num_bytes: 86258579
num_examples: 5675
download_size: 95108813
dataset_size: 86258579
---
Russian translation of [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) dataset.
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corto-ai/contracts-donut | corto-ai | 2023-12-17T21:52:13Z | 54 | 0 | [
"region:us"
] | null | 2023-12-07T06:46:19Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': contract
'1': other
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 31088307.0
num_examples: 72
- name: test
num_bytes: 11427625.0
num_examples: 26
download_size: 35811375
dataset_size: 42515932.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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Rijgersberg/no_robots_nl | Rijgersberg | 2023-12-15T15:24:43Z | 54 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-nc-4.0",
"GEITje",
"region:us"
] | [
"conversational",
"text-generation"
] | 2023-12-09T03:54:11Z | ---
configs:
- config_name: default
data_files:
- split: test_sft
path: data/test_sft-*
- split: train_sft
path: data/train_sft-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: category
dtype: string
- name: messages_nl
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: test_sft
num_bytes: 1517416
num_examples: 500
- name: train_sft
num_bytes: 28407005
num_examples: 9500
download_size: 18675565
dataset_size: 29924421
license: cc-by-nc-4.0
language:
- nl
- en
tags:
- GEITje
task_categories:
- conversational
- text-generation
size_categories:
- 10K<n<100K
pretty_name: No Robots NL
---
# Dataset Card for "no_robots_nl"
A translated version of all 10k examples from [HuggingFaceH4/no_robots](https://huggingface.co/datasets/HuggingFaceH4/no_robots).
Automatically translated by GPT-3.5.
## More info
Read more about GEITje-chat, the datasets and the translation code in the [📄 README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub. | [
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ScandEval/conll-nl-mini | ScandEval | 2023-12-11T15:01:57Z | 54 | 0 | [
"region:us"
] | null | 2023-12-11T15:01:53Z | ---
dataset_info:
features:
- name: tokens
sequence: string
- name: labels
sequence: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 284693
num_examples: 1024
- name: val
num_bytes: 66183
num_examples: 256
- name: test
num_bytes: 268933
num_examples: 1024
download_size: 273634
dataset_size: 619809
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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theojiang/contrastive_conditional_vid_diff_std_1_6_webvid-test | theojiang | 2023-12-12T11:37:08Z | 54 | 0 | [
"region:us"
] | null | 2023-12-12T11:37:03Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 73413092.0
num_examples: 2000
download_size: 72714847
dataset_size: 73413092.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Gummybear05/E_normal_over70 | Gummybear05 | 2023-12-11T09:03:55Z | 53 | 0 | [
"region:us"
] | null | 2023-12-11T04:48:58Z | ---
dataset_info:
features:
- name: audio
struct:
- name: array
sequence: float64
- name: path
dtype: string
- name: sample_rate
dtype: int64
- name: text
dtype: string
- name: scriptId
dtype: int64
- name: fileNm
dtype: string
- name: recrdTime
dtype: float64
- name: recrdQuality
dtype: int64
- name: recrdDt
dtype: string
- name: scriptSetNo
dtype: string
- name: recrdEnvrn
dtype: string
- name: colctUnitCode
dtype: string
- name: cityCode
dtype: string
- name: recrdUnit
dtype: string
- name: convrsThema
dtype: string
- name: gender
dtype: string
- name: recorderId
dtype: string
- name: age
dtype: int64
splits:
- name: train
num_bytes: 13273866538
num_examples: 12401
- name: test
num_bytes: 722618850
num_examples: 605
download_size: 3128009221
dataset_size: 13996485388
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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drewtray/instructpix2pix-spatial | drewtray | 2023-12-11T16:23:29Z | 53 | 0 | [
"region:us"
] | null | 2023-12-11T16:15:05Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: transformed_image
dtype: image
splits:
- name: train
num_bytes: 11378934045.176
num_examples: 11557
download_size: 11039896281
dataset_size: 11378934045.176
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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deutsche-telekom/NLU-Evaluation-Data-en-de | deutsche-telekom | 2023-12-17T17:42:48Z | 52 | 1 | [
"task_categories:text-classification",
"task_ids:intent-classification",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:extended|nlu_evaluation_data",
"language:en",
"language:de",
"license:cc-by-4.0",
"arxiv:1903.05566",
"region:us"
] | [
"text-classification"
] | 2022-12-01T16:54:19Z | ---
license: cc-by-4.0
source_datasets:
- extended|nlu_evaluation_data
multilinguality:
- multilingual
language:
- en
- de
size_categories:
- 10K<n<100K
task_categories:
- text-classification
task_ids:
- intent-classification
---
# NLU Evaluation Data - English and German
A labeled English **and German** language multi-domain dataset (21 domains) with 25K user utterances for human-robot interaction.
This dataset is collected and annotated for evaluating NLU services and platforms.
The detailed paper on this dataset can be found at arXiv.org:
[Benchmarking Natural Language Understanding Services for building Conversational Agents](https://arxiv.org/abs/1903.05566)
The dataset builds on the annotated data of the [xliuhw/NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data)
repository. We have added an additional column (`answer_de`)
by translating the texts in column `answer` into German.
The translation was made with [DeepL](https://www.deepl.com/translator).
## Creator
This data set was compiled and open sourced by [Philip May](https://may.la/)
of [Deutsche Telekom](https://www.telekom.de/).
## Labels
The columns `scenario` and `intent` can be used for classification tasks.
However, we recommend to use even more fine-grained labels.
For this purpose, a new label can be derived by concatenating `scenario` and `intent`.
For example, this would turn "alarm" and "set" into "alarm_set".
## Dataset Quirks
The original dataset contains some `NaN` values in the `answer` column.
This means that there are also `NaN` values in the translations (`answer_de` column).
These rows should be filtered.
The dataset also contains duplicate values.
## Copyright
Copyright (c) the authors of [xliuhw/NLU-Evaluation-Data](https://github.com/xliuhw/NLU-Evaluation-Data)\
Copyright (c) 2022 [Philip May](https://may.la/)
All data is released under the
[Creative Commons Attribution 4.0 International License (CC BY 4.0)](http://creativecommons.org/licenses/by/4.0/).
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] |
Jasontth/climate_fever_plus | Jasontth | 2023-12-12T13:07:14Z | 52 | 1 | [
"task_categories:text-classification",
"language:en",
"climate",
"region:us"
] | [
"text-classification"
] | 2023-12-06T06:03:08Z | ---
dataset_info:
features:
- name: claim
dtype: string
- name: evidence
dtype: string
- name: evidence_label
dtype: string
splits:
- name: train
num_bytes: 1849006
num_examples: 5680
- name: test
num_bytes: 402208
num_examples: 1217
- name: valid
num_bytes: 392173
num_examples: 1218
download_size: 1381515
dataset_size: 2643387
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
task_categories:
- text-classification
language:
- en
tags:
- climate
---
The dataset provided combines the Climate-Fever dataset and claim reviews from the website Climate Feedback (https://climatefeedback.org/)
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ScandEval/nqii-mini | ScandEval | 2023-12-11T14:55:06Z | 52 | 0 | [
"region:us"
] | null | 2023-12-11T14:55:01Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int32
- name: text
sequence: string
splits:
- name: train
num_bytes: 770131
num_examples: 1024
- name: val
num_bytes: 190602
num_examples: 256
- name: test
num_bytes: 759390
num_examples: 1024
download_size: 997781
dataset_size: 1720123
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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NextDayAI/MultipleResponsesChat_labelled_unlabelled_all_engines_20230901_20231211 | NextDayAI | 2023-12-12T15:03:24Z | 52 | 0 | [
"region:us"
] | null | 2023-12-12T15:03:02Z | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: rejected_response
dtype: string
- name: selected_response
dtype: string
splits:
- name: train
num_bytes: 636045152.508617
num_examples: 203270
- name: valid
num_bytes: 18774393.245691456
num_examples: 6000
- name: test
num_bytes: 18774393.245691456
num_examples: 6000
download_size: 424452174
dataset_size: 673593938.9999999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
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TeamSODA/cl-awgn_commonvoice | TeamSODA | 2023-12-13T09:24:56Z | 51 | 0 | [
"region:us"
] | null | 2023-12-10T18:49:43Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': benign
'1': attacked
splits:
- name: SNR_0
num_bytes: 52068544.0
num_examples: 80
- name: SNR_5
num_bytes: 51414208.0
num_examples: 80
- name: SNR_10
num_bytes: 52027072.0
num_examples: 80
- name: SNR_20
num_bytes: 54109888.0
num_examples: 80
- name: SNR_30
num_bytes: 55506112.0
num_examples: 80
- name: clean
num_bytes: 2571727.0
num_examples: 80
download_size: 267711841
dataset_size: 267697551.0
configs:
- config_name: default
data_files:
- split: SNR_0
path: data/SNR_0-*
- split: SNR_5
path: data/SNR_5-*
- split: SNR_10
path: data/SNR_10-*
- split: SNR_20
path: data/SNR_20-*
- split: SNR_30
path: data/SNR_30-*
- split: clean
path: data/clean-*
---
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ScandEval/sb10k-mini | ScandEval | 2023-12-11T13:21:14Z | 51 | 0 | [
"region:us"
] | null | 2023-12-11T13:21:10Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 92744
num_examples: 1024
- name: val
num_bytes: 23374
num_examples: 256
- name: test
num_bytes: 93614
num_examples: 1024
download_size: 147912
dataset_size: 209732
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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ScandEval/dutch-social-mini | ScandEval | 2023-12-11T13:24:36Z | 51 | 0 | [
"region:us"
] | null | 2023-12-11T13:24:32Z | ---
dataset_info:
features:
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download_size: 266112
dataset_size: 377139
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
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erbacher/personalized-interactive-conversations | erbacher | 2023-12-16T09:32:17Z | 51 | 0 | [
"region:us"
] | null | 2023-12-16T09:32:10Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: uid
dtype: int64
- name: user
dtype: string
- name: conversation
list:
- name: assistant
dtype: string
- name: turn_id
dtype: int64
- name: user
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 130143792
num_examples: 28105
download_size: 64414511
dataset_size: 130143792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "personalized-interactive-conversations"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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OswaldHe123/novel-text | OswaldHe123 | 2023-12-12T20:48:23Z | 50 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-11T21:10:01Z | ---
license: mit
---
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ScandEval/scala-de | ScandEval | 2023-12-11T12:51:45Z | 49 | 0 | [
"region:us"
] | null | 2023-12-11T12:51:39Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: corruption_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 153423
num_examples: 1024
- name: val
num_bytes: 35237
num_examples: 256
- name: test
num_bytes: 296640
num_examples: 2048
- name: full_train
num_bytes: 3786681
num_examples: 26096
download_size: 2614289
dataset_size: 4271981
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
- split: full_train
path: data/full_train-*
---
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ScandEval/scala-nl | ScandEval | 2023-12-11T12:51:53Z | 49 | 0 | [
"region:us"
] | null | 2023-12-11T12:51:48Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: corruption_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 128728
num_examples: 1024
- name: val
num_bytes: 31254
num_examples: 256
- name: test
num_bytes: 252959
num_examples: 2048
- name: full_train
num_bytes: 2224913
num_examples: 18100
download_size: 1552833
dataset_size: 2637854
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
- split: full_train
path: data/full_train-*
---
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LabHC/Moji_stratify | LabHC | 2023-12-15T10:31:43Z | 49 | 0 | [
"region:us"
] | null | 2023-12-15T10:28:42Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
- name: sa
dtype: int64
splits:
- name: train
num_bytes: 3843951
num_examples: 50000
- name: test
num_bytes: 154027
num_examples: 2000
- name: dev
num_bytes: 152283
num_examples: 2000
download_size: 2169706
dataset_size: 4150261
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
---
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booksouls/goodreads-book-descriptions | booksouls | 2023-12-15T05:09:20Z | 48 | 0 | [
"size_categories:100K<n<1M",
"language:en",
"book",
"region:us"
] | null | 2023-12-15T00:56:41Z | ---
language:
- en
size_categories:
- 100K<n<1M
pretty_name: Goodreads Book Descriptions
dataset_info:
features:
- name: title
dtype: string
- name: description
dtype: string
- name: description_normalized
dtype: string
splits:
- name: train
num_bytes: 1217122044.2024436
num_examples: 866810
- name: test
num_bytes: 135236874.7975565
num_examples: 96313
download_size: 922115746
dataset_size: 1352358919
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- book
---
# Goodreads Book Descriptions
A dataset of book descriptions from Goodreads.
The [original dataset] has 2.3 million books total with many more fields.
This dataset contains only descriptions of English books with reasonable length
and some basic cleaning like removal of duplicates and empty descriptions.
The `description_normalized` column is the `description` with the following transformations applied:
- Removal of punctuation.
- Word tokenization.
- Lowercasing.
- Lemmatization.
- Removal of stop words.
# Citations
- Mengting Wan, Julian McAuley, "[Item Recommendation on Monotonic Behavior Chains]", in RecSys'18.
- Mengting Wan, Rishabh Misra, Ndapa Nakashole, Julian McAuley, "[Fine-Grained Spoiler Detection from Large-Scale Review Corpora]", in ACL'19.
[original dataset]: https://mengtingwan.github.io/data/goodreads.html
[Item Recommendation on Monotonic Behavior Chains]: https://mengtingwan.github.io/paper/recsys18_mwan.pdf
[Fine-Grained Spoiler Detection from Large-Scale Review Corpora]: https://mengtingwan.github.io/paper/acl19_mwan.pdf | [
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BlahBlah314/AcolyteAxT | BlahBlah314 | 2023-12-15T11:21:17Z | 47 | 0 | [
"region:us"
] | null | 2023-12-11T18:26:50Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 206842113.6
num_examples: 351
- name: test
num_bytes: 23905646.4
num_examples: 39
download_size: 225310378
dataset_size: 230747760.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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seba3y/speechocean762 | seba3y | 2023-12-12T10:31:25Z | 46 | 0 | [
"task_categories:audio-classification",
"size_categories:1K<n<10K",
"language:en",
"region:us"
] | [
"audio-classification"
] | 2023-12-11T09:35:14Z | ---
language:
- en
size_categories:
- 1K<n<10K
task_categories:
- audio-classification
dataset_info:
features:
- name: utt_name
dtype: int64
- name: text
dtype: string
- name: accuracy
dtype: int64
- name: completeness
dtype: float64
- name: fluency
dtype: int64
- name: prosodic
dtype: int64
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 580334479.0
num_examples: 5000
download_size: 611260599
dataset_size: 580334479.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Ericu950/ParaLoebSent | Ericu950 | 2023-12-16T06:45:02Z | 46 | 0 | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:la",
"language:el",
"language:en",
"region:us"
] | [
"translation"
] | 2023-12-16T06:40:43Z | ---
task_categories:
- translation
language:
- la
- el
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
Paralell sentences in latin/greek english
## Dataset Details
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[More Information Needed] | [
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castorini/wura | castorini | 2023-12-11T16:06:49Z | 45 | 6 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"language:afr",
"language:amh",
"language:arz",
"language:eng",
"language:fra",
"language:hau",
"language:ibo",
"language:kin",
"language:mlg",
"language:nya",
"language:orm",
"language:por",
"language:sna",
"language:som",
"language:sot",
"language:swa",
"language:tir",
"language:xho",
"language:yor",
"language:zul",
"license:apache-2.0",
"region:us"
] | [
"text-generation"
] | 2023-11-27T15:26:39Z | ---
license: apache-2.0
mutilinguality:
- multilingual
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- eng
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- ibo
- kin
- mlg
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- orm
- por
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- som
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- swa
- tir
- xho
- yor
- zul
viewer: true
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---
# Dataset Summary
`WURA` is a document-level dataset covering 16 African Languages and 4 high-resource languages widely spoken in Africa (English, French, Arabic and Portuguese). This dataset was created by auditing mC4 and crawling additional verified news sources. It was first used to train AfriTeVa V2.
# Dataset Structure
```
>>> from datasets import load_dataset
```
Although the document-level dataset is loaded by default, you may also optionally load a passage-level dataset as follows
```
>>> data = load_dataset("castorini/wura, "yor", level="passage", verification_mode="no_checks")
```
Note that we must pass `verification_mode="no_checks` to prevent HF from verifying checksums against the document-level checksum infos.
# Citation
```
@inproceedings{oladipo-etal-2023-better,
title = "Better Quality Pre-training Data and T5 Models for {A}frican Languages",
author = "Oladipo, Akintunde and
Adeyemi, Mofetoluwa and
Ahia, Orevaoghene and
Owodunni, Abraham and
Ogundepo, Odunayo and
Adelani, David and
Lin, Jimmy",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.11",
pages = "158--168",
abstract = "In this study, we highlight the importance of enhancing the quality of pretraining data in multilingual language models. Existing web crawls have demonstrated quality issues, particularly in the context of low-resource languages. Consequently, we introduce a new multilingual pretraining corpus for 16 African languages, designed by carefully auditing existing pretraining corpora to understand and rectify prevalent quality issues. To compile this dataset, we undertake a rigorous examination of current data sources for thirteen languages within one of the most extensive multilingual web crawls, mC4, and extract cleaner data through meticulous auditing and improved web crawling strategies. Subsequently, we pretrain a new T5-based model on this dataset and evaluate its performance on multiple downstream tasks. Our model demonstrates better downstream effectiveness over existing pretrained models across four NLP tasks, underscoring the critical role data quality plays in pretraining language models in low-resource scenarios. Specifically, on cross-lingual QA evaluation, our new model is more than twice as effective as multilingual T5. All code, data and models are publicly available at https://github.com/castorini/AfriTeVa-keji.",
}
``` | [
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Yehoon/imdb | Yehoon | 2023-12-14T05:22:31Z | 45 | 1 | [
"region:us"
] | null | 2023-12-11T05:17:47Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 13003200
num_examples: 10195
download_size: 8156718
dataset_size: 13003200
---
# Dataset Card for "imdb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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shireesh-uop/nhs_qna | shireesh-uop | 2023-12-12T22:52:21Z | 45 | 0 | [
"region:us"
] | null | 2023-12-12T18:50:25Z | ---
dataset_info:
features:
- name: q
dtype: string
- name: i
dtype: string
- name: a
dtype: string
- name: filename
dtype: string
- name: context
dtype: string
- name: count
dtype: int64
- name: text
dtype: string
- name: text1
dtype: string
splits:
- name: train
num_bytes: 196338950
num_examples: 21281
download_size: 19781209
dataset_size: 196338950
configs:
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data_files:
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path: data/train-*
---
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Hunzla/xls-r-crema-d-300-each | Hunzla | 2023-12-13T10:04:07Z | 45 | 0 | [
"region:us"
] | null | 2023-12-13T10:02:13Z | ---
dataset_info:
features:
- name: emotionG_label
dtype: string
- name: gender
dtype: string
- name: emotion
dtype: string
- name: path
dtype: string
- name: label
sequence: int64
- name: input_values
sequence:
sequence: float32
- name: input_length
dtype: int64
splits:
- name: train
num_bytes: 415072955
num_examples: 1296
- name: test
num_bytes: 115298158
num_examples: 360
- name: val
num_bytes: 46119146
num_examples: 144
download_size: 511175257
dataset_size: 576490259
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: val
path: data/val-*
---
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CalvinU/project-gutenberg | CalvinU | 2023-12-13T04:07:54Z | 44 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-08T02:09:42Z | ---
license: mit
---
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ChatRDM/RDMkit_training_data | ChatRDM | 2023-12-13T14:26:52Z | 44 | 0 | [
"task_categories:conversational",
"task_categories:text-generation",
"size_categories:1K<n<100k",
"language:en",
"license:mit",
"region:us"
] | [
"conversational",
"text-generation"
] | 2023-12-13T08:40:41Z | ---
language:
- en
license: mit
size_categories:
- 1K<n<100k
task_categories:
- conversational
- text-generation
pretty_name: chatrdm 0.8k
configs:
- config_name: default
data_files:
- split: train_sft
path: updated_train.json
- split: test_sft
path: updated_train.json
- split: train_gen
path: updated_train.json
- split: test_gen
path: updated_train.json
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train_sft
num_bytes: 662915
num_examples: 820
- name: test_sft
num_bytes: 662915
num_examples: 820
- name: train_gen
num_bytes: 662915
num_examples: 820
- name: test_gen
num_bytes: 662915
num_examples: 820
download_size:
dataset_size:
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pphuc25/gsm8k_rd500_42 | pphuc25 | 2023-12-14T07:07:34Z | 44 | 0 | [
"region:us"
] | null | 2023-12-14T07:06:08Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: test
num_bytes: 270557.99848369975
num_examples: 500
download_size: 137948
dataset_size: 270557.99848369975
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
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davidfant/rapidapi-example-responses-workflow-steps | davidfant | 2023-12-12T01:18:02Z | 43 | 0 | [
"region:us"
] | null | 2023-12-10T14:42:05Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: index
dtype: int64
- name: workflow
dtype: string
- name: plan
dtype: string
- name: reasoning
dtype: string
- name: label
dtype: string
- name: data
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 24204115
num_examples: 6018
download_size: 2084270
dataset_size: 24204115
---
# Dataset Card for "rapidapi-example-responses-workflow-steps"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Djulo/Wider_FaceSegLite | Djulo | 2023-12-18T01:17:42Z | 43 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T16:00:22Z | ---
license: apache-2.0
---
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Asif-Saad/Bangla_News_Article | Asif-Saad | 2023-12-14T21:36:20Z | 42 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-10-16T08:31:58Z | ---
license: apache-2.0
---
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ugursa/yahooFinanceNews | ugursa | 2023-12-15T18:42:56Z | 42 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-01T19:39:26Z | ---
license: mit
---
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davidfant/rapidapi-example-responses-workflows | davidfant | 2023-12-11T22:19:14Z | 42 | 0 | [
"region:us"
] | null | 2023-12-10T14:19:02Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: name
dtype: string
- name: description
dtype: string
- name: steps
dtype: string
splits:
- name: train
num_bytes: 3227895
num_examples: 1000
download_size: 1173873
dataset_size: 3227895
---
# Dataset Card for "rapidapi-example-responses-workflows"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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theojiang/contrastive_conditional_vid_diff_std_1_18_MSRVTT-train | theojiang | 2023-12-12T10:28:10Z | 42 | 0 | [
"region:us"
] | null | 2023-12-11T15:47:24Z | ---
dataset_info:
features:
- name: original_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 6930335.0
num_examples: 200
download_size: 6797378
dataset_size: 6930335.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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bipulai/skillate_helpdesk | bipulai | 2023-12-16T14:46:02Z | 42 | 0 | [
"region:us"
] | null | 2023-12-16T14:45:19Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: cleaned_question
dtype: string
- name: cleaned_answer
dtype: string
splits:
- name: train
num_bytes: 617367
num_examples: 302
download_size: 236993
dataset_size: 617367
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "skillate_helpdesk"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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k2141255/RealMedQA | k2141255 | 2023-12-11T11:43:55Z | 41 | 0 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-nc-4.0",
"medical",
"region:us"
] | [
"question-answering"
] | 2023-12-10T17:42:15Z | ---
license: cc-by-nc-4.0
task_categories:
- question-answering
language:
- en
tags:
- medical
pretty_name: d
size_categories:
- 1K<n<10K
---
# RealMedQA
RealMedQA is a biomedical question answering dataset consisting of realistic question and answer pairs.
The questions were created by medical students and a large language model (LLM), while the answers are guideline
recommendations provided by the UK's National Institute for Health and Care Excellence (NICE).
## Dataset creation
## Data collection
Initially, 12,543 guidelines were retrieved using the [NICE syndication API](https://www.nice.org.uk/about/what-we-do/nice-syndication-api).
As we were interested in only the guidelines that pertain to clinical practice, we only used the guidelines
that came under 'Conditions and diseases' which reduced the number to 7,385.
## Question generation
We created an instruction sheet with examples which we provided to both the humans (medical students)
and the LLM to generate the several questions for each guideline recommendation. The instruction sheet
was fed as a prompt along with each recommendation to the LLM, while the humans created the questions using
Google forms.
## Question verification
Both the QA pairs generated by the LLM and those generated by human annotators were verified by humans for quality.
The verifiers were asked whether each question:
* Could plausibly be asked by a clinician would ask in practice;
* Was adequately answered by the accompanying recommendation.
A total of 800 human QA pairs and 400 LLM QA pairs were verified.
## Dataset structure
The dataset is structured according to the following columns:
* **Generator:** whether the question was generated by a 'Human' or 'LLM';
* **Question:** the question created by either the human or LLM;
* **Recommendation:** the recommendation that is supposed to address the question;
* **Plausible:** whether the question could plausibly be asked by a clinician in practice (as assessed by the verifier);
* **Answered:** whether the question is adequately answered by the clinician (as assessed by the verifier).
## Contact
* Gregory Kell: [email protected]
* Iain Marshall: [email protected] | [
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VictorNGomes/CorpusTeMario | VictorNGomes | 2023-12-16T03:45:00Z | 41 | 1 | [
"language:pt",
"region:us"
] | null | 2023-12-15T21:50:22Z | ---
language:
- pt
---
# Dataset Card for Dataset Name
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[More Information Needed] | [
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mhenrichsen/creator | mhenrichsen | 2023-12-12T20:46:44Z | 40 | 0 | [
"region:us"
] | null | 2023-11-08T09:20:23Z | ---
dataset_info:
features:
- name: id
dtype: int64
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 184006
num_examples: 1000
download_size: 10392
dataset_size: 184006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "creator"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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bdsaglam/web_nlg-erx-sft-llama2 | bdsaglam | 2023-12-17T20:14:14Z | 40 | 0 | [
"region:us"
] | null | 2023-12-09T18:13:57Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 46034310
num_examples: 35426
- name: dev
num_bytes: 5802673
num_examples: 4464
download_size: 12650537
dataset_size: 51836983
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
---
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ktennyson6/augmented-vsr | ktennyson6 | 2023-12-11T14:59:29Z | 40 | 0 | [
"region:us"
] | null | 2023-12-11T10:10:01Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: relation
dtype: string
splits:
- name: train
num_bytes: 897911834.0
num_examples: 6237
download_size: 886692853
dataset_size: 897911834.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "augmented-vsr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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malaysia-ai/crawl-youtube | malaysia-ai | 2023-12-14T03:19:20Z | 40 | 0 | [
"region:us"
] | null | 2023-12-12T08:24:27Z | ---
dataset_info:
features:
- name: filename
dtype:
audio:
sampling_rate: 16000
- name: url
dtype: string
splits:
- name: train
num_bytes: 1068464089483.938
num_examples: 59879
download_size: 16395869337
dataset_size: 1068464089483.938
---
# Crawl Youtube
We crawled Malaysian and Singaporean youtube channels, total up to 60k audio files.
URLs data at https://github.com/mesolitica/malaya-speech/tree/master/data/youtube/data
Notebooks at https://github.com/mesolitica/malaya-speech/tree/master/data/youtube
## Licensing
```
All the videos, songs, images, and graphics used in the video belong to their respective owners and I does not claim any right over them.
Copyright Disclaimer under section 107 of the Copyright Act of 1976, allowance is made for "fair use" for purposes such as criticism, comment, news reporting, teaching, scholarship, education and research. Fair use is a use permitted by copyright statute that might otherwise be infringing.
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Hypersniper/philosophy_dialogue | Hypersniper | 2023-12-12T21:52:05Z | 40 | 0 | [
"license:apache-2.0",
"region:us"
] | null | 2023-12-12T20:48:33Z | ---
license: apache-2.0
---
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mnoukhov/test_ds | mnoukhov | 2023-12-16T23:10:20Z | 39 | 0 | [
"region:us"
] | null | 2023-11-27T16:08:35Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: prompt
dtype: string
- name: label
dtype: string
- name: reward_baseline
dtype: float32
splits:
- name: train
num_bytes: 158890
num_examples: 100
- name: valid
num_bytes: 159279
num_examples: 100
download_size: 0
dataset_size: 318169
---
# Dataset Card for "test_ds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Lkhagvasurenam/STT1 | Lkhagvasurenam | 2023-12-11T10:12:16Z | 39 | 0 | [
"region:us"
] | null | 2023-12-11T05:31:16Z | ---
dataset_info:
features:
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 17222551527.257793
num_examples: 25367
- name: test
num_bytes: 1809885857.0052073
num_examples: 2819
download_size: 17792010503
dataset_size: 19032437384.263
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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ctam8736/papi_asr_test | ctam8736 | 2023-12-12T20:31:53Z | 39 | 0 | [
"license:mit",
"region:us"
] | null | 2023-12-12T19:37:19Z | ---
license: mit
dataset_info:
features:
- name: audio
dtype: audio
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 2230870597.36
num_examples: 11615
download_size: 2482064836
dataset_size: 2230870597.36
---
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AMead10/Universal-Pure-Dove | AMead10 | 2023-12-14T07:48:39Z | 39 | 0 | [
"region:us"
] | null | 2023-12-14T07:48:37Z | ---
dataset_info:
features:
- name: conversation
list:
- name: input
dtype: string
- name: output
dtype: string
- name: system
dtype: string
splits:
- name: train
num_bytes: 11565500
num_examples: 3857
download_size: 5954760
dataset_size: 11565500
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Universal-Pure-Dove"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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Blazej/banking_alignment_preference_ds | Blazej | 2023-12-17T05:10:35Z | 39 | 0 | [
"region:us"
] | null | 2023-12-17T05:10:31Z | ---
dataset_info:
features:
- name: guideline
dtype: string
- name: query
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 2286243
num_examples: 1200
download_size: 1112482
dataset_size: 2286243
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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nikhil-c-r/llm-test-dataset | nikhil-c-r | 2023-12-17T16:24:35Z | 39 | 0 | [
"region:us"
] | null | 2023-12-17T16:23:18Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 104651270
num_examples: 95000
download_size: 63458811
dataset_size: 104651270
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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Ransaka/Sinhala-400M | Ransaka | 2023-12-11T03:03:00Z | 38 | 0 | [
"task_categories:text-generation",
"task_categories:feature-extraction",
"size_categories:10M<n<100M",
"language:si",
"license:apache-2.0",
"region:us"
] | [
"text-generation",
"feature-extraction"
] | 2023-04-13T05:28:53Z | ---
dataset_info:
features:
- name: text
sequence: string
splits:
- name: train
num_bytes: 2802808058.089643
num_examples: 8854185
- name: test
num_bytes: 1201203543.9103568
num_examples: 3794651
download_size: 1826451430
dataset_size: 4004011602
license: apache-2.0
task_categories:
- text-generation
- feature-extraction
language:
- si
pretty_name: Sinhala Large Scale Corpus
size_categories:
- 10M<n<100M
---
# Dataset Card for "Sinhala-400M"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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llm-book/ner-wikinews-dataset | llm-book | 2023-12-12T02:22:26Z | 38 | 0 | [
"task_categories:token-classification",
"size_categories:n<1K",
"language:ja",
"license:cc-by-2.5",
"news",
"region:us"
] | [
"token-classification"
] | 2023-04-22T14:32:21Z | ---
license:
- cc-by-2.5
task_categories:
- token-classification
language:
- ja
tags:
- news
pretty_name: ner-wikinews-dataset
size_categories:
- n<1K
---
# Dataset Card for llm-book/ner-wikinews-dataset
書籍『大規模言語モデル入門』で使用する、[Wikinews](https://ja.wikinews.org/wiki/%E3%83%A1%E3%82%A4%E3%83%B3%E3%83%9A%E3%83%BC%E3%82%B8)の記事に固有表現ラベルを付与したデータセットです。
固有表現ラベルは[llm-book/ner-wikipedia-dataset](https://huggingface.co/datasets/llm-book/ner-wikipedia-dataset)と同様のものを採用しており、全部で8種類 (人名、法人名、地名、製品名、政治的組織名、施設名、その他の組織名、イベント名)あります。
テストセットのみのデータセットとなっています。
## Licence
ウィキニュース日本語版の記事を使用しているため、そのライセンスに従い、「クリエイティブ・コモンズ 表示 2.5 (CC BY 2.5)」とします。
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cwchoi/whisper_base_tele | cwchoi | 2023-12-15T00:59:29Z | 38 | 0 | [
"region:us"
] | null | 2023-12-08T01:38:19Z | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 9605715992
num_examples: 10000
- name: test
num_bytes: 28817104464
num_examples: 30000
- name: valid
num_bytes: 9605734528
num_examples: 10000
download_size: 7504847823
dataset_size: 48028554984
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
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correll/semanticsegmentationandposeestimationfromrgbd | correll | 2023-12-14T15:43:51Z | 37 | 0 | [
"task_categories:image-segmentation",
"task_categories:image-classification",
"task_categories:object-detection",
"license:mit",
"region:us"
] | [
"image-segmentation",
"image-classification",
"object-detection"
] | 2023-11-24T10:37:39Z | ---
license: mit
task_categories:
- image-segmentation
- image-classification
- object-detection
pretty_name: Semantic segmenation and pose estimation from RGB-D
dataset_info:
features:
- name: rgb
dtype: image
- name: depth
dtype: image
- name: mask
dtype: image
- name: meta
list:
- name: colors
sequence: float64
- name: file
dtype: string
- name: id
dtype: int64
- name: model
dtype: string
- name: numberOfColors
dtype: int64
- name: orientation
sequence: float64
- name: position
sequence: float64
- name: positionOnImage
sequence: int64
- name: size
sequence: float64
- name: sizeOnImage
sequence: int64
splits:
- name: train
num_bytes: 3340733260.96
num_examples: 1106
download_size: 3319212411
dataset_size: 3340733260.96
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
RGB-D dataset for instance segmentation (from RGB or depth) and pose estimation of individual objects. Data has been generated by randomizing bin contents in Webots.
Each instance contains a mask image as well meta data containing labels, position, and size of each object.
<video src='https://cdn-uploads.huggingface.co/production/uploads/655b1b359d249b4ab388d4a2/l6b76ezxkPi6lG3Fr6_kj.mp4' width=720/>
You can create your own data by opening webots_grasp.wbt in the world directory using [Webots](https://www.cyberbotics.com). | [
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ArtifactAI/model_research_area_benchmark | ArtifactAI | 2023-12-13T20:41:26Z | 37 | 0 | [
"region:us"
] | null | 2023-12-05T19:51:43Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: answer
dtype:
class_label:
names:
'0': Audio
'1': Computer Vision
'2': Graphs
'3': Natural Language Processing
'4': Reinforcement Learning
'5': Sequential
- name: choices
sequence: string
splits:
- name: train
num_bytes: 233889
num_examples: 970
- name: test
num_bytes: 58478
num_examples: 243
download_size: 33207
dataset_size: 292367
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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