datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
billxbf/rewoo-instruction-finetuning | ---
license: mit
---
|
EgilKarlsen/Spirit_GPTNEO_FT | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
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- name: label
dtype: string
splits:
- name: train
num_bytes: 307650093
num_examples: 37500
- name: test
num_bytes: 102549993
num_examples: 12500
download_size: 565171727
dataset_size: 410200086
---
# Dataset Card for "Spirit_GPTNEO_FT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
James4Ever0/the_frozen_forest | ---
license: wtfpl
---
After extracting the RAR file, you will find a bunch of folders named by timestamps, in which you can find these files:
```
hid_record.jsonl video_record.mp4 video_timestamps.json
hid_timestamps.json video_record_script.sh
```
`video_record.mp4` is a video file at 30fps with 1280x768 resolution, in which each frame is a screenshot taken not at the video play speed.
In hid_record.jsonl you shall find:
```
{"HIDEvents": []}
{"HIDEvents": []}
{"HIDEvents": []}
{"HIDEvents": [["key_press", "Key.ctrl"], ["key_press", "Key.shift"], ["key_press", "Key.page_up"], ["key_release", "Key.page_up"], ["key_release", "Key.shift"], ["key_release", "Key.ctrl"]]}
{"HIDEvents": []}
{"HIDEvents": []}
{"HIDEvents": []}
{"HIDEvents": []}
{"HIDEvents": [["mouse_move", [782, 682]]]}
{"HIDEvents": []}
{"HIDEvents": []}
{"HIDEvents": [["key_press", "Key.alt"], ["key_press", "'l'"], ["key_release", "'l'"], ["key_release", "Key.alt"]]}
```
`video_timestamps.json` contains the corresponding UNIX timestamps for every frame recorded:
```
[
1685664003.6361628,
1685664003.6745877,
1685664003.6882446,
1685664003.715868,
1685664003.7464304,
1685664003.7711987,
1685664003.7833188,
1685664003.8149195,
...
]
```
`hid_timestamps.json` is similar to `video_timestamps.json` and contains every timestamp for every HID action, event, including those empty ones, found in `hid_record.jsonl`.
|
TornikeO/nitrosocke-ghibli-diffusion-dreambooth-cache | ---
license: mit
---
|
iamkovtun/dataset_0.01t | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 919266
num_examples: 1000
download_size: 488602
dataset_size: 919266
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TANUJT/FSA-LLaMA3b | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 165070
num_examples: 1000
download_size: 91728
dataset_size: 165070
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ShrinivasSK/kn_en_2 | ---
dataset_info:
features:
- name: idx
dtype: int64
- name: tgt
dtype: string
- name: src
dtype: string
splits:
- name: train
num_bytes: 3982082.4
num_examples: 18000
- name: test
num_bytes: 442453.6
num_examples: 2000
download_size: 2369798
dataset_size: 4424536.0
---
# Dataset Card for "kn_en_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
juancopi81/lmd_clean_8bars_32th_resolution | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6417909784
num_examples: 244436
- name: test
num_bytes: 1221971111
num_examples: 46005
- name: validation
num_bytes: 1465985310
num_examples: 54947
download_size: 974110589
dataset_size: 9105866205
---
# Dataset Card for "lmd_clean_8bars_32th_resolution"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HPGomes/DinhoVozNormal | ---
license: openrail
---
|
oliver003/Naly | ---
license: openrail
---
|
BEE-spoke-data/medium-articles-en | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: title
dtype: string
- name: text
dtype: string
- name: url
dtype: string
- name: authors
dtype: string
- name: timestamp
dtype: string
- name: tags
dtype: string
- name: token_count
dtype: int64
splits:
- name: train
num_bytes: 930797692.9172074
num_examples: 171340
- name: validation
num_bytes: 24494962.048346493
num_examples: 4509
- name: test
num_bytes: 24494962.048346493
num_examples: 4509
download_size: 615394671
dataset_size: 979787617.0139004
license: mit
language:
- en
size_categories:
- 100K<n<1M
source_datasets: fabiochiu/medium-articles
task_categories:
- text-classification
- text-generation
---
# Dataset Card for "medium-articles-en"
`fabiochiu/medium-articles` filtered for `en` only and 100 GPT-4 tiktoken tokens or more. |
cutesylvia79/TestAddress | ---
pretty_name: Address_Dataset
--- |
rogozinushka/povarenok-recipes | ---
language:
- ru
---
# Кулинарные рецепты с сайта [povarenok.ru](https://www.povarenok.ru)
Данные актуальны на 2021-06-16. Парсер, с помощью которого получили датасет, можно найти в [этом репозитории](https://github.com/rogozinushka/povarenok_recipes_parser)
Внимание. Согласно [правилам размещения рецептов](https://www.povarenok.ru/wiki/pravilorecept), все права на рецепты принадлежат сайту, так что имейте это в виду, если планируете использовать датасет
Датафрейм имеет такую структуру:
- url - ссылка на рецепт
- name - название рецепта
- ingredients - словарь с ингредиентами. Ключ - ингридиент, значение - количество
|url|name|ingredients|
|---|---|---|
|https://www.povarenok.ru/recipes/show/171921/|Омлет с сыром и ветчиной|{'Яйцо куриное': '5 шт', 'Ветчина': '150 г', 'Сыр твердый': '150 г', 'Соль': '1 щепот.', 'Масло сливочное': '10 г', 'Молоко': '50 мл'}|
# Culinary recipes from [povarenok.ru](https://www.povarenok.ru) site
The data is current for 2021-06-16. The parser used to get the dataset can be found in [this repository] (https://github.com/rogozinushka/povarenok_recipes_parser)
Data structure:
- url - culinary recipe url
- name - culinary recipe name
- ingredients - ingredients dict. Key is ingredient, value is amount
|url|name|ingredients|
|---|---|---|
|https://www.povarenok.ru/recipes/show/171921/|Омлет с сыром и ветчиной|{'Яйцо куриное': '5 шт', 'Ветчина': '150 г', 'Сыр твердый': '150 г', 'Соль': '1 щепот.', 'Масло сливочное': '10 г', 'Молоко': '50 мл'} |
Zenodia/dreambooth-lego | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 14731012.0
num_examples: 12
download_size: 14673354
dataset_size: 14731012.0
---
# Dataset Card for "dreambooth-lego"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zirui3/cMedQA2-instructions | ---
license: cc-by-4.0
---
|
CintiaJOS/cintia2 | ---
license: openrail
---
|
bantunagarjuna/orca-1k-1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1369602
num_examples: 586
download_size: 733935
dataset_size: 1369602
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
TigerResearch/tigerbot-OIG-multichat-en-50k | ---
license: apache-2.0
language:
- en
---
[Tigerbot](https://github.com/TigerResearch/TigerBot) 基于开源OIG数据集加工生成的多轮对话sft数据集
<p align="center" width="40%">
原始来源：[https://huggingface.co/datasets/laion/OIG](https://huggingface.co/datasets/laion/OIG)
## Usage
```python
import datasets
ds_sft = datasets.load_dataset('TigerResearch/tigerbot-OIG-multichat-en-50k')
``` |
dariolopez/ms-marco-es | ---
dataset_info:
features:
- name: query
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 34534407690
num_examples: 39780811
download_size: 13523306019
dataset_size: 34534407690
task_categories:
- question-answering
language:
- es
size_categories:
- 10M<n<100M
license: apache-2.0
---
# Dataset Card for "ms-marco-es"
QA asymmetric Spanish dataset filtered from [multilingual version of MS Marco](https://huggingface.co/datasets/unicamp-dl/mmarco)
```python
import datasets
ms_marco_es = datasets.load_dataset('unicamp-dl/mmarco', name='spanish', split='train')
ms_marco_es.push_to_hub("dariolopez/ms-marco-es", token=os.environ['hg_token'])
``` |
vwxyzjn/openhermes-dev__mistralai_Mixtral-8x7B-Instruct-v0.1__1706896441 | ---
dataset_info:
features:
- name: topic
dtype: 'null'
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dtype: 'null'
- name: conversations
list:
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dtype: string
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list:
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dtype: string
- name: role
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- name: token_length
dtype: int64
- name: rejected
list:
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dtype: string
- name: role
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- name: rejected_policy
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splits:
- name: train_prefs
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- name: test_prefs
num_bytes: 0
num_examples: 0
download_size: 106929
dataset_size: 1293423
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
---
|
hazyresearch/based-swde-deprecated | ---
dataset_info:
features:
- name: doc_id
dtype: string
- name: file_name
dtype: string
- name: key
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: validation
num_bytes: 18402899
num_examples: 12385
download_size: 4958697
dataset_size: 18402899
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
Anthrall/rauco | ---
license: afl-3.0
---
|
re_dial | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: redial
pretty_name: ReDial (Recommendation Dialogues)
tags:
- dialogue-sentiment-classification
dataset_info:
features:
- name: movieMentions
list:
- name: movieId
dtype: string
- name: movieName
dtype: string
- name: respondentQuestions
list:
- name: movieId
dtype: string
- name: suggested
dtype: int32
- name: seen
dtype: int32
- name: liked
dtype: int32
- name: messages
list:
- name: timeOffset
dtype: int32
- name: text
dtype: string
- name: senderWorkerId
dtype: int32
- name: messageId
dtype: int32
- name: conversationId
dtype: int32
- name: respondentWorkerId
dtype: int32
- name: initiatorWorkerId
dtype: int32
- name: initiatorQuestions
list:
- name: movieId
dtype: string
- name: suggested
dtype: int32
- name: seen
dtype: int32
- name: liked
dtype: int32
splits:
- name: train
num_bytes: 13496125
num_examples: 10006
- name: test
num_bytes: 1731449
num_examples: 1342
download_size: 5765261
dataset_size: 15227574
---
# Dataset Card for ReDial (Recommendation Dialogues)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [ReDial Dataset](https://redialdata.github.io/website/)
- **Repository:** [ReDialData](https://github.com/ReDialData/website/tree/data)
- **Paper:** [Towards Deep Conversational Recommendations](https://proceedings.neurips.cc/paper/2018/file/800de15c79c8d840f4e78d3af937d4d4-Paper.pdf)
- **Point of Contact:** [ReDial Google Group](https://groups.google.com/forum/embed/?place=forum/redial-dataset&showpopout=true#!forum/redial-dataset)
### Dataset Summary
ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users
recommend movies to each other. The dataset was collected by a team of researchers working at
Polytechnique Montréal, MILA – Quebec AI Institute, Microsoft Research Montréal, HEC Montreal, and Element AI.
The dataset allows research at the intersection of goal-directed dialogue systems
(such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
JSON-formatted example of a typical instance in the dataset.
```
{
"movieMentions":{
"203371":"Final Fantasy: The Spirits Within (2001)",
"84779":"The Triplets of Belleville (2003)",
"122159":"Mary and Max (2009)",
"151313":"A Scanner Darkly (2006)",
"191602":"Waking Life (2001)",
"165710":"The Boss Baby (2017)"
},
"respondentQuestions":{
"203371":{
"suggested":1,
"seen":0,
"liked":1
},
"84779":{
"suggested":0,
"seen":1,
"liked":1
},
"122159":{
"suggested":0,
"seen":1,
"liked":1
},
"151313":{
"suggested":0,
"seen":1,
"liked":1
},
"191602":{
"suggested":0,
"seen":1,
"liked":1
},
"165710":{
"suggested":1,
"seen":0,
"liked":1
}
},
"messages":[
{
"timeOffset":0,
"text":"Hi there, how are you? I'm looking for movie recommendations",
"senderWorkerId":0,
"messageId":1021
},
{
"timeOffset":15,
"text":"I am doing okay. What kind of movies do you like?",
"senderWorkerId":1,
"messageId":1022
},
{
"timeOffset":66,
"text":"I like animations like @84779 and @191602",
"senderWorkerId":0,
"messageId":1023
},
{
"timeOffset":86,
"text":"I also enjoy @122159",
"senderWorkerId":0,
"messageId":1024
},
{
"timeOffset":95,
"text":"Anything artistic",
"senderWorkerId":0,
"messageId":1025
},
{
"timeOffset":135,
"text":"You might like @165710 that was a good movie.",
"senderWorkerId":1,
"messageId":1026
},
{
"timeOffset":151,
"text":"What's it about?",
"senderWorkerId":0,
"messageId":1027
},
{
"timeOffset":207,
"text":"It has Alec Baldwin it is about a baby that works for a company and gets adopted it is very funny",
"senderWorkerId":1,
"messageId":1028
},
{
"timeOffset":238,
"text":"That seems like a nice comedy",
"senderWorkerId":0,
"messageId":1029
},
{
"timeOffset":272,
"text":"Do you have any animated recommendations that are a bit more dramatic? Like @151313 for example",
"senderWorkerId":0,
"messageId":1030
},
{
"timeOffset":327,
"text":"I like comedies but I prefer films with a little more depth",
"senderWorkerId":0,
"messageId":1031
},
{
"timeOffset":467,
"text":"That is a tough one but I will remember something",
"senderWorkerId":1,
"messageId":1032
},
{
"timeOffset":509,
"text":"@203371 was a good one",
"senderWorkerId":1,
"messageId":1033
},
{
"timeOffset":564,
"text":"Ooh that seems cool! Thanks for the input. I'm ready to submit if you are.",
"senderWorkerId":0,
"messageId":1034
},
{
"timeOffset":571,
"text":"It is animated, sci fi, and has action",
"senderWorkerId":1,
"messageId":1035
},
{
"timeOffset":579,
"text":"Glad I could help",
"senderWorkerId":1,
"messageId":1036
},
{
"timeOffset":581,
"text":"Nice",
"senderWorkerId":0,
"messageId":1037
},
{
"timeOffset":591,
"text":"Take care, cheers!",
"senderWorkerId":0,
"messageId":1038
},
{
"timeOffset":608,
"text":"bye",
"senderWorkerId":1,
"messageId":1039
}
],
"conversationId":"391",
"respondentWorkerId":1,
"initiatorWorkerId":0,
"initiatorQuestions":{
"203371":{
"suggested":1,
"seen":0,
"liked":1
},
"84779":{
"suggested":0,
"seen":1,
"liked":1
},
"122159":{
"suggested":0,
"seen":1,
"liked":1
},
"151313":{
"suggested":0,
"seen":1,
"liked":1
},
"191602":{
"suggested":0,
"seen":1,
"liked":1
},
"165710":{
"suggested":1,
"seen":0,
"liked":1
}
}
}
```
### Data Fields
The dataset is published in the “jsonl” format, i.e., as a text file where each line corresponds to a Dialogue given as a valid JSON document.
A Dialogue contains these fields:
**conversationId:** an integer
**initiatorWorkerId:** an integer identifying to the worker initiating the conversation (the recommendation seeker)
**respondentWorkerId:** an integer identifying the worker responding to the initiator (the recommender)
**messages:** a list of Message objects
**movieMentions:** a dict mapping movie IDs mentioned in this dialogue to movie names
**initiatorQuestions:** a dictionary mapping movie IDs to the labels supplied by the initiator. Each label is a bool corresponding to whether the initiator has said he saw the movie, liked it, or suggested it.
**respondentQuestions:** a dictionary mapping movie IDs to the labels supplied by the respondent. Each label is a bool corresponding to whether the initiator has said he saw the movie, liked it, or suggested it.
Each Message contains these fields:
**messageId:** a unique ID for this message
**text:** a string with the actual message. The string may contain a token starting with @ followed by an integer. This is a movie ID which can be looked up in the movieMentions field of the Dialogue object.
**timeOffset:** time since start of dialogue in seconds
**senderWorkerId:** the ID of the worker sending the message, either initiatorWorkerId or respondentWorkerId.
The labels in initiatorQuestions and respondentQuestions have the following meaning:
*suggested:* 0 if it was mentioned by the seeker, 1 if it was a suggestion from the recommender
*seen:* 0 if the seeker has not seen the movie, 1 if they have seen it, 2 if they did not say
*liked:* 0 if the seeker did not like the movie, 1 if they liked it, 2 if they did not say
### Data Splits
The dataset contains a total of 11348 dialogues, 10006 for training and model selection, and 1342 for testing.
## Dataset Creation
### Curation Rationale
The dataset allows research at the intersection of goal-directed dialogue systems (such as restaurant recommendation) and free-form (also called “chit-chat”) dialogue systems.
In the dataset, users talk about which movies they like and which ones they do not like, which ones they have seen or not etc., and labels which we ensured agree between the two participants. This allows to research how sentiment is expressed in dialogues, which differs a lot from e.g. review websites.
The dialogues and the movies they mention form a curious bi-partite graph structure, which is related to how users talk about the movie (e.g. genre information).
Ignoring label information, this dataset can also be viewed as a limited domain chit-chat dialogue dataset.
### Source Data
#### Initial Data Collection and Normalization
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
#### Who are the source language producers?
Here we formalize the setup of a conversation involving recommendations for the purposes of data collection. To provide some additional structure to our data (and models) we define one person in the dialogue as the recommendation seeker and the other as the recommender.
To obtain data in this form, we developed an interface and pairing mechanism mediated by Amazon Mechanical Turk (AMT).
We pair up AMT workers and give each of them a role. The movie seeker has to explain what kind of movie he/she likes, and asks for movie suggestions. The recommender tries to understand the seeker’s movie tastes, and recommends movies. All exchanges of information and recommendations are made using natural language.
We add additional instructions to improve the data quality and guide the workers to dialogue the way we expect them to. Thus we ask to use formal language and that conversations contain roughly ten messages minimum. We also require that at least four different movies are mentioned in every conversation. Finally, we also ask to converse only about movies, and notably not to mention Mechanical Turk or the task itself.
In addition, we ask that every movie mention is tagged using the ‘@’ symbol. When workers type ‘@’, the following characters are used to find matching movie names, and workers can choose a movie from that list. This allows us to detect exactly what movies are mentioned and when. We gathered entities from DBpedia that were of type http://dbpedia.org/ontology/Film to obtain a list of movies, but also allow workers to add their own movies to the list if it is not present already. We obtained the release dates from the movie titles (e.g. http://dbpedia.org/page/American_Beauty_(1999_film), or, if the movie title does not contain that information, from an additional SPARQL request. Note that the year or release date of a movie can be essential to differentiate movies with the same name, but released at different dates.
We will refer to these additional labels as movie dialogue forms. Both workers have to answer these forms even though it really concerns the seeker’s movie tastes. Ideally, the two participants would give the same answer to every form, but it is possible that their answers do not coincide (because of carelessness, or dialogue ambiguity). The movie dialogue forms therefore allow us to evaluate sub-components of an overall neural dialogue system more systematically, for example one can train and evaluate a sentiment analysis model directly using these labels. %which could produce a reward for the dialogue agent.
In each conversation, the number of movies mentioned varies, so we have different numbers of movie dialogue form answers for each conversation. The distribution of the different classes of the movie dialogue form is shown in Table 1a. The liked/disliked/did not say label is highly imbalanced. This is standard for recommendation data, since people are naturally more likely to talk about movies that they like, and the recommender’s objective is to recommend movies that the seeker is likely to like.
### Annotations
#### Annotation process
Mentioned in above sub-section.
#### Who are the annotators?
For the AMT HIT we collect data in English and chose to restrict the data collection to countries where English is the main language. The fact that we pair workers together slows down the data collection since we ask that at least two persons are online at the same time to do the task, so a good amount of workers is required to make the collection possible. Meanwhile, the task is quite demanding, and we have to select qualified workers. HIT reward and qualification requirement were decisive to get good conversation quality while still ensuring that people could get paired together. We launched preliminary HITs to find a compromise and finally set the reward to $0.50 per person for each completed conversation (so each conversation costs us $1, plus taxes), and ask that workers meet the following requirements: (1)~Approval percentage greater than 95, (2)~Number of approved HITs greater than 1000, (3)~Their location must be in United States, Canada, United Kingdom, Australia, or New Zealand.
### Personal and Sensitive Information
Workers had to confirm a consent form before every task that explains what the data is being collected for and how it is going to be used.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset collection was funded by Google, IBM, and NSERC, with editorial support from Microsoft Research.
### Licensing Information
The data is published under the CC BY 4.0 License.
### Citation Information
```
@inproceedings{li2018conversational,
title={Towards Deep Conversational Recommendations},
author={Li, Raymond and Kahou, Samira Ebrahimi and Schulz, Hannes and Michalski, Vincent and Charlin, Laurent and Pal, Chris},
booktitle={Advances in Neural Information Processing Systems 31 (NIPS 2018)},
year={2018}
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. |
matteopilotto/porkypig | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 2107384.0
num_examples: 11
download_size: 2108606
dataset_size: 2107384.0
---
# Dataset Card for "porkypig"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jpawan33/NC | ---
license: other
---
|
JotDe/mscoco_100k_30k_test | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2740227007.482
num_examples: 29997
download_size: 507305710
dataset_size: 2740227007.482
---
# Dataset Card for "mscoco_100k_30k_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dsfsi/daily-news-dikgang | ---
license: cc-by-sa-4.0
task_categories:
- text-classification
language:
- tn
size_categories:
- 1K<n<10K
---
# Daily News Dikgang
[](https://arxiv.org/abs/2310.09141)
Give Feedback 📑: [DSFSI Resource Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSf7S36dyAUPx2egmXbFpnTBuzoRulhL5Elu-N1eoMhaO7v10w/formResponse)
## About dataset
The dataset contains annotated categorised data from Dikgang - Daily News [https://dailynews.gov.bw/news-list/srccategory/10](https://dailynews.gov.bw/news-list/srccategory/10). The data is in setswana.
See the [Data Statement](DataStatementPuoBERTaDailyNewsDikgang.pdf) for foll details.
Disclaimer
-------
This dataset contains machine-readable data extracted from online news articles, from [https://dailynews.gov.bw/news-list/srccategory/10](https://dailynews.gov.bw/news-list/srccategory/10), provided by the Botswana Government. While efforts were made to ensure the accuracy and completeness of this data, there may be errors or discrepancies between the original publications and this dataset. No warranties, guarantees or representations are given in relation to the information contained in the dataset. The members of the Data Science for Societal Impact Research Group bear no responsibility and/or liability for any such errors or discrepancies in this dataset. The Botswana Government bears no responsibility and/or liability for any such errors or discrepancies in this dataset. It is recommended that users verify all information contained herein before making decisions based upon this information.
Authors
-------
- Vukosi Marivate - [@vukosi](https://twitter.com/vukosi)
- Valencia Wagner
Citation
--------
Bibtex Reference
```
@inproceedings{marivate2023puoberta,
title = {PuoBERTa: Training and evaluation of a curated language model for Setswana},
author = {Vukosi Marivate and Moseli Mots'Oehli and Valencia Wagner and Richard Lastrucci and Isheanesu Dzingirai},
year = {2023},
booktitle= {SACAIR 2023 (To Appear)},
keywords = {NLP},
preprint_url = {https://arxiv.org/abs/2310.09141},
dataset_url = {https://github.com/dsfsi/PuoBERTa},
software_url = {https://huggingface.co/dsfsi/PuoBERTa}
}
```
Licences
-------
The license of the News Categorisation dataset is in CC-BY-SA-4.0. the monolingual data have difference licenses depending on the news website license
* License for Data - [CC-BY-SA-4.0](LICENSE.data.md) |
rntc/blurb_bc5chem_a-0-tm | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: type
dtype: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B
'2': I
splits:
- name: train
num_bytes: 12559349
num_examples: 4560
- name: validation
num_bytes: 13337850
num_examples: 4581
- name: test
num_bytes: 12530047
num_examples: 4797
download_size: 5462479
dataset_size: 38427246
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
PierreVie/mm_reviews | ---
dataset_info:
features:
- name: message_customer
dtype: string
- name: Description technique/conformité/montage
dtype: string
- name: Livraison (vitesse, problème)
dtype: string
- name: Etat du produit, problème de fonctionnement
dtype: float64
- name: Qualité / Prix / Rapport qualité prix
dtype: string
- name: Aspect produit
dtype: string
- name: Avis général ou service ManoMano / SAV
dtype: string
splits:
- name: train
num_bytes: 27108.294392523363
num_examples: 171
- name: test
num_bytes: 6816.705607476635
num_examples: 43
download_size: 29295
dataset_size: 33925.0
---
# Dataset Card for "mm_reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rewcifer/validation_2000_cutoff_llama_first10results | ---
dataset_info:
features:
- name: text
dtype: string
- name: generated_texts
dtype: string
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 82377
num_examples: 10
download_size: 38297
dataset_size: 82377
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "validation_2000_cutoff_llama_first10results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
RoopamSadh/cancer | ---
language:
- en
--- |
andersonbcdefg/inpars_generated_query_pairs | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 727847753
num_examples: 675366
download_size: 442972737
dataset_size: 727847753
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
divergente/wikitext-ptbr-1 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- pt
license:
- cc-by-sa-3.0
- gfdl
multilinguality:
- monolingual
pretty_name: WikiTextPtBr
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
dataset_info:
- config_name: wikitext-ptbr
features:
- name: text
dtype: string
--- |
cyanelis/91396498T | ---
license: cc-by-nc-4.0
--- |
result-kand2-sdxl-wuerst-karlo/9f8a49b7 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 235
num_examples: 10
download_size: 1403
dataset_size: 235
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "9f8a49b7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Rewcifer/validation_2000_cutoff_llama_formatted | ---
dataset_info:
features:
- name: labels_and_findings
dtype: string
- name: prompts
dtype: string
- name: true_findings
dtype: string
splits:
- name: train
num_bytes: 113806806
num_examples: 14551
download_size: 26372198
dataset_size: 113806806
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "validation_2000_cutoff_llama_formatted"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_netcat420__MFANN3bv0.2 | ---
pretty_name: Evaluation run of netcat420/MFANN3bv0.2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [netcat420/MFANN3bv0.2](https://huggingface.co/netcat420/MFANN3bv0.2) 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_netcat420__MFANN3bv0.2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-04-05T22:06:13.928028](https://huggingface.co/datasets/open-llm-leaderboard/details_netcat420__MFANN3bv0.2/blob/main/results_2024-04-05T22-06-13.928028.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\": 0.5660123809430012,\n\
\ \"acc_stderr\": 0.03398433850624273,\n \"acc_norm\": 0.5666128042278201,\n\
\ \"acc_norm_stderr\": 0.034686099952925195,\n \"mc1\": 0.36107711138310894,\n\
\ \"mc1_stderr\": 0.016814312844836886,\n \"mc2\": 0.5300497483925641,\n\
\ \"mc2_stderr\": 0.015635324847578257\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5955631399317406,\n \"acc_stderr\": 0.014342036483436177,\n\
\ \"acc_norm\": 0.6177474402730375,\n \"acc_norm_stderr\": 0.014200454049979284\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5711013742282414,\n\
\ \"acc_stderr\": 0.004939073014754942,\n \"acc_norm\": 0.7634933280223063,\n\
\ \"acc_norm_stderr\": 0.0042406832810934015\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\": 0.42962962962962964,\n\
\ \"acc_stderr\": 0.04276349494376599,\n \"acc_norm\": 0.42962962962962964,\n\
\ \"acc_norm_stderr\": 0.04276349494376599\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.04065771002562603,\n\
\ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.04065771002562603\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n\
\ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6037735849056604,\n \"acc_stderr\": 0.030102793781791197,\n\
\ \"acc_norm\": 0.6037735849056604,\n \"acc_norm_stderr\": 0.030102793781791197\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.5902777777777778,\n\
\ \"acc_stderr\": 0.04112490974670788,\n \"acc_norm\": 0.5902777777777778,\n\
\ \"acc_norm_stderr\": 0.04112490974670788\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.42,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.42,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887249,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887249\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.047551296160629475,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.047551296160629475\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.68,\n \"acc_stderr\": 0.04688261722621504,\n \"acc_norm\": 0.68,\n\
\ \"acc_norm_stderr\": 0.04688261722621504\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.4765957446808511,\n \"acc_stderr\": 0.03265019475033581,\n\
\ \"acc_norm\": 0.4765957446808511,\n \"acc_norm_stderr\": 0.03265019475033581\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.34210526315789475,\n\
\ \"acc_stderr\": 0.04462917535336936,\n \"acc_norm\": 0.34210526315789475,\n\
\ \"acc_norm_stderr\": 0.04462917535336936\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.041665675771015785,\n\
\ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.041665675771015785\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.025446365634406783,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.025446365634406783\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4444444444444444,\n\
\ \"acc_stderr\": 0.044444444444444495,\n \"acc_norm\": 0.4444444444444444,\n\
\ \"acc_norm_stderr\": 0.044444444444444495\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7161290322580646,\n\
\ \"acc_stderr\": 0.02564938106302926,\n \"acc_norm\": 0.7161290322580646,\n\
\ \"acc_norm_stderr\": 0.02564938106302926\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.57,\n \"acc_stderr\": 0.04975698519562428,\n \"acc_norm\"\
: 0.57,\n \"acc_norm_stderr\": 0.04975698519562428\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6666666666666666,\n \"acc_stderr\": 0.036810508691615486,\n\
\ \"acc_norm\": 0.6666666666666666,\n \"acc_norm_stderr\": 0.036810508691615486\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7070707070707071,\n \"acc_stderr\": 0.03242497958178815,\n \"\
acc_norm\": 0.7070707070707071,\n \"acc_norm_stderr\": 0.03242497958178815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7772020725388601,\n \"acc_stderr\": 0.03003114797764154,\n\
\ \"acc_norm\": 0.7772020725388601,\n \"acc_norm_stderr\": 0.03003114797764154\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5846153846153846,\n \"acc_stderr\": 0.02498535492310233,\n \
\ \"acc_norm\": 0.5846153846153846,\n \"acc_norm_stderr\": 0.02498535492310233\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131133,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131133\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6218487394957983,\n \"acc_stderr\": 0.031499305777849054,\n\
\ \"acc_norm\": 0.6218487394957983,\n \"acc_norm_stderr\": 0.031499305777849054\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849929,\n \"\
acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849929\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7981651376146789,\n \"acc_stderr\": 0.017208579357787586,\n \"\
acc_norm\": 0.7981651376146789,\n \"acc_norm_stderr\": 0.017208579357787586\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5185185185185185,\n \"acc_stderr\": 0.03407632093854051,\n \"\
acc_norm\": 0.5185185185185185,\n \"acc_norm_stderr\": 0.03407632093854051\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.6274509803921569,\n \"acc_stderr\": 0.03393388584958405,\n \"\
acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.03393388584958405\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7172995780590717,\n \"acc_stderr\": 0.02931281415395592,\n \
\ \"acc_norm\": 0.7172995780590717,\n \"acc_norm_stderr\": 0.02931281415395592\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6278026905829597,\n\
\ \"acc_stderr\": 0.032443052830087304,\n \"acc_norm\": 0.6278026905829597,\n\
\ \"acc_norm_stderr\": 0.032443052830087304\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6641221374045801,\n \"acc_stderr\": 0.041423137719966634,\n\
\ \"acc_norm\": 0.6641221374045801,\n \"acc_norm_stderr\": 0.041423137719966634\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7024793388429752,\n \"acc_stderr\": 0.04173349148083499,\n \"\
acc_norm\": 0.7024793388429752,\n \"acc_norm_stderr\": 0.04173349148083499\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.042365112580946315,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.042365112580946315\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\
\ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8076923076923077,\n\
\ \"acc_stderr\": 0.02581923325648371,\n \"acc_norm\": 0.8076923076923077,\n\
\ \"acc_norm_stderr\": 0.02581923325648371\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.61,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.61,\n \"acc_norm_stderr\": 0.04902071300001975\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.6717752234993615,\n\
\ \"acc_stderr\": 0.01679168564019289,\n \"acc_norm\": 0.6717752234993615,\n\
\ \"acc_norm_stderr\": 0.01679168564019289\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.615606936416185,\n \"acc_stderr\": 0.026189666966272035,\n\
\ \"acc_norm\": 0.615606936416185,\n \"acc_norm_stderr\": 0.026189666966272035\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2435754189944134,\n\
\ \"acc_stderr\": 0.014355911964767865,\n \"acc_norm\": 0.2435754189944134,\n\
\ \"acc_norm_stderr\": 0.014355911964767865\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6176470588235294,\n \"acc_stderr\": 0.0278261093072837,\n\
\ \"acc_norm\": 0.6176470588235294,\n \"acc_norm_stderr\": 0.0278261093072837\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6334405144694534,\n\
\ \"acc_stderr\": 0.02736807824397164,\n \"acc_norm\": 0.6334405144694534,\n\
\ \"acc_norm_stderr\": 0.02736807824397164\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6172839506172839,\n \"acc_stderr\": 0.0270445381384026,\n\
\ \"acc_norm\": 0.6172839506172839,\n \"acc_norm_stderr\": 0.0270445381384026\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.41843971631205673,\n \"acc_stderr\": 0.02942799403941999,\n \
\ \"acc_norm\": 0.41843971631205673,\n \"acc_norm_stderr\": 0.02942799403941999\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.40352020860495436,\n\
\ \"acc_stderr\": 0.012530241301193179,\n \"acc_norm\": 0.40352020860495436,\n\
\ \"acc_norm_stderr\": 0.012530241301193179\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n \
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\"\
: 0.545751633986928,\n \"acc_stderr\": 0.020142974553795198,\n \"\
acc_norm\": 0.545751633986928,\n \"acc_norm_stderr\": 0.020142974553795198\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.046075820907199756,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.046075820907199756\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6489795918367347,\n \"acc_stderr\": 0.03055531675557364,\n\
\ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.03055531675557364\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7562189054726368,\n\
\ \"acc_stderr\": 0.03036049015401464,\n \"acc_norm\": 0.7562189054726368,\n\
\ \"acc_norm_stderr\": 0.03036049015401464\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.044084400227680794,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.044084400227680794\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.45180722891566266,\n\
\ \"acc_stderr\": 0.03874371556587953,\n \"acc_norm\": 0.45180722891566266,\n\
\ \"acc_norm_stderr\": 0.03874371556587953\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.6900584795321637,\n \"acc_stderr\": 0.035469769593931624,\n\
\ \"acc_norm\": 0.6900584795321637,\n \"acc_norm_stderr\": 0.035469769593931624\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.36107711138310894,\n\
\ \"mc1_stderr\": 0.016814312844836886,\n \"mc2\": 0.5300497483925641,\n\
\ \"mc2_stderr\": 0.015635324847578257\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7584846093133386,\n \"acc_stderr\": 0.01202898378201187\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5526914329037149,\n \
\ \"acc_stderr\": 0.013695795709089898\n }\n}\n```"
repo_url: https://huggingface.co/netcat420/MFANN3bv0.2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|arc:challenge|25_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|gsm8k|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hellaswag|10_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T22-06-13.928028.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-04-05T22-06-13.928028.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- '**/details_harness|winogrande|5_2024-04-05T22-06-13.928028.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-04-05T22-06-13.928028.parquet'
- config_name: results
data_files:
- split: 2024_04_05T22_06_13.928028
path:
- results_2024-04-05T22-06-13.928028.parquet
- split: latest
path:
- results_2024-04-05T22-06-13.928028.parquet
---
# Dataset Card for Evaluation run of netcat420/MFANN3bv0.2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [netcat420/MFANN3bv0.2](https://huggingface.co/netcat420/MFANN3bv0.2) 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_netcat420__MFANN3bv0.2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-04-05T22:06:13.928028](https://huggingface.co/datasets/open-llm-leaderboard/details_netcat420__MFANN3bv0.2/blob/main/results_2024-04-05T22-06-13.928028.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": 0.5660123809430012,
"acc_stderr": 0.03398433850624273,
"acc_norm": 0.5666128042278201,
"acc_norm_stderr": 0.034686099952925195,
"mc1": 0.36107711138310894,
"mc1_stderr": 0.016814312844836886,
"mc2": 0.5300497483925641,
"mc2_stderr": 0.015635324847578257
},
"harness|arc:challenge|25": {
"acc": 0.5955631399317406,
"acc_stderr": 0.014342036483436177,
"acc_norm": 0.6177474402730375,
"acc_norm_stderr": 0.014200454049979284
},
"harness|hellaswag|10": {
"acc": 0.5711013742282414,
"acc_stderr": 0.004939073014754942,
"acc_norm": 0.7634933280223063,
"acc_norm_stderr": 0.0042406832810934015
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.42962962962962964,
"acc_stderr": 0.04276349494376599,
"acc_norm": 0.42962962962962964,
"acc_norm_stderr": 0.04276349494376599
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5197368421052632,
"acc_stderr": 0.04065771002562603,
"acc_norm": 0.5197368421052632,
"acc_norm_stderr": 0.04065771002562603
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6037735849056604,
"acc_stderr": 0.030102793781791197,
"acc_norm": 0.6037735849056604,
"acc_norm_stderr": 0.030102793781791197
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.5902777777777778,
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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. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
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#### Annotation process
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#### Who are the annotators?
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#### 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
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## Citation [optional]
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## Glossary [optional]
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ihaflix1/mariliamandonca | ---
license: openrail
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_237 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1167986748.0
num_examples: 227589
download_size: 1194410979
dataset_size: 1167986748.0
---
# Dataset Card for "chunk_237"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Lazycuber/alpaca-en-zh-jp-merge | ---
license: apache-2.0
---
|
CyberHarem/iws_2000_girlsfrontline | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of iws_2000/IWS2000/IWS2000 (Girls' Frontline)
This is the dataset of iws_2000/IWS2000/IWS2000 (Girls' Frontline), containing 203 images and their tags.
The core tags of this character are `long_hair, hair_ornament, red_eyes, hairclip, bangs, ribbon, hair_between_eyes, hair_ribbon, breasts, bow, white_hair, sidelocks, ahoge, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 203 | 298.51 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iws_2000_girlsfrontline/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 203 | 160.95 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iws_2000_girlsfrontline/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 491 | 344.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iws_2000_girlsfrontline/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 203 | 260.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iws_2000_girlsfrontline/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 491 | 494.38 MiB | [Download](https://huggingface.co/datasets/CyberHarem/iws_2000_girlsfrontline/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/iws_2000_girlsfrontline',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 5 |  |  |  |  |  | 1girl, cleavage, day, looking_at_viewer, outdoors, solo, blue_sky, blush, collarbone, navel, beach, closed_mouth, hair_bow, ocean, white_bikini, bare_shoulders, cloud, jacket_on_shoulders, large_breasts, smile, standing |
| 1 | 8 |  |  |  |  |  | 1girl, looking_at_viewer, solo, anti-materiel_rifle, blush, official_alternate_costume, ponytail, white_shirt, black_bow, black_skirt, black_thighhighs, closed_mouth, pleated_skirt, holding_gun, smile, thighs, floating_hair, gloves, long_sleeves, night, very_long_hair, wind_lift |
| 2 | 18 |  |  |  |  |  | 1girl, solo, looking_at_viewer, military_uniform, jacket, pleated_skirt, shirt, anti-materiel_rifle, blush, white_socks, black_bow, long_sleeves, kneehighs, thighs, black_gloves, white_background, holding_gun, medal, shoes, buttons, scope, simple_background |
| 3 | 12 |  |  |  |  |  | 1girl, collared_jacket, military_uniform, solo, long_sleeves, pleated_skirt, thighs, blush, looking_at_viewer, medal, black_bow, closed_mouth, floating_hair, wind_lift, black_gloves, frilled_sleeves, simple_background, white_jacket, white_shirt, smile, white_background, cowboy_shot, cropped_jacket, double-breasted |
| 4 | 5 |  |  |  |  |  | 1girl, black_gloves, blush, collared_jacket, long_sleeves, looking_at_viewer, military_uniform, pleated_skirt, simple_background, solo, white_background, black_bow, buttons, character_name, frilled_sleeves, kneehighs, medal, very_long_hair, white_jacket, white_shirt, white_socks, chibi, closed_mouth, full_body, hair_bow, open_clothes, shoes, white_skirt |
| 5 | 5 |  |  |  |  |  | 1girl, black_gloves, long_sleeves, looking_at_viewer, military_uniform, shirt, smile, solo, closed_mouth, white_jacket, blush, holding, pleated_skirt, simple_background, frills, standing, white_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | day | looking_at_viewer | outdoors | solo | blue_sky | blush | collarbone | navel | beach | closed_mouth | hair_bow | ocean | white_bikini | bare_shoulders | cloud | jacket_on_shoulders | large_breasts | smile | standing | anti-materiel_rifle | official_alternate_costume | ponytail | white_shirt | black_bow | black_skirt | black_thighhighs | pleated_skirt | holding_gun | thighs | floating_hair | gloves | long_sleeves | night | very_long_hair | wind_lift | military_uniform | jacket | shirt | white_socks | kneehighs | black_gloves | white_background | medal | shoes | buttons | scope | simple_background | collared_jacket | frilled_sleeves | white_jacket | cowboy_shot | cropped_jacket | double-breasted | character_name | chibi | full_body | open_clothes | white_skirt | holding | frills |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:------|:--------------------|:-----------|:-------|:-----------|:--------|:-------------|:--------|:--------|:---------------|:-----------|:--------|:---------------|:-----------------|:--------|:----------------------|:----------------|:--------|:-----------|:----------------------|:-----------------------------|:-----------|:--------------|:------------|:--------------|:-------------------|:----------------|:--------------|:---------|:----------------|:---------|:---------------|:--------|:-----------------|:------------|:-------------------|:---------|:--------|:--------------|:------------|:---------------|:-------------------|:--------|:--------|:----------|:--------|:--------------------|:------------------|:------------------|:---------------|:--------------|:-----------------|:------------------|:-----------------|:--------|:------------|:---------------|:--------------|:----------|:---------|
| 0 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 8 |  |  |  |  |  | X | | | X | | X | | X | | | | X | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 18 |  |  |  |  |  | X | | | X | | X | | X | | | | | | | | | | | | | | X | | | | X | | | X | X | X | | | X | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | | | X | | X | | X | | | | X | | | | | | | | X | | | | | X | X | | | X | | X | X | | X | | | X | X | | | | | X | X | X | | | | X | X | X | X | X | X | X | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | | X | | X | | X | | | | X | X | | | | | | | | | | | | X | X | | | X | | | | | X | | X | | X | | | X | X | X | X | X | X | X | | X | X | X | X | | | | X | X | X | X | X | | |
| 5 | 5 |  |  |  |  |  | X | | | X | | X | | X | | | | X | | | | | | | | X | X | | | | | | | | X | | | | | X | | | | X | | X | | | X | X | | | | | X | | | X | | | | | | | | | X | X |
|
cookecd1/AMOC_QA | ---
dataset_info:
features:
- name: input_data
dtype: string
- name: label
dtype: int32
- name: label_level_1
dtype: int32
- name: label_level_2
dtype: int32
- name: text
dtype: string
splits:
- name: train
num_bytes: 233955
num_examples: 170
download_size: 141535
dataset_size: 233955
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
yuntian-deng/iclr-decisions-processed-full | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': reject
'1': accept
'2': withdrawn
'3': desk_rejected
splits:
- name: test
num_bytes: 6919068
num_examples: 4955
- name: train
num_bytes: 13374300
num_examples: 10454
- name: validation
num_bytes: 1488217
num_examples: 1162
download_size: 11707258
dataset_size: 21781585
---
# Dataset Card for "iclr-decisions-processed-full"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mariakmurphy55/testingdatasetcards | ---
license: cc0-1.0
language:
- en
pretty_name: linregdata
size_categories:
- n<1K
---
# Dataset Card for Testingdatasetcards
Very Simple Multiple Linear Regression Dataset
## Dataset Details
### Dataset Description
<!-- This is a very simple multiple linear regression dataset for beginners.
This dataset has only three columns and twenty rows.
There are only two independent variables and one dependent variable. The independent variables are 'age' and 'experience'.
The dependent variable is 'income'. -->
- **Curated by:** HUSSAIN NASIR KHAN (Kaggle)
- **Shared by [optional]:** Maria Murphy
- **Language(s) (NLP):** English
- **License:** CC0: Public Domain
## Uses
Intended for practice with linear regression.
## Dataset Structure
Contains three columns (age, experience, income) and twenty observations. |
Kamyar-zeinalipour/tr_quiz_simple | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5373756
num_examples: 3723
- name: test
num_bytes: 442976
num_examples: 300
download_size: 2668064
dataset_size: 5816732
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
autoevaluate/autoeval-eval-futin__feed-sen_en-395337-2175269958 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-1b7
metrics: []
dataset_name: futin/feed
dataset_config: sen_en
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-1b7
* Dataset: futin/feed
* Config: sen_en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
Mauregato/affectnet_short | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': anger
'1': surprise
'2': contempt
'3': happy
'4': neutral
'5': fear
'6': sad
'7': disgust
splits:
- name: train
num_bytes: 432233297.875
num_examples: 23233
- name: val
num_bytes: 108197028.875
num_examples: 5809
download_size: 540092363
dataset_size: 540430326.75
---
# Dataset Card for "affectnet_short"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
MicPie/unpredictable_full | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: UnpredicTable-full
size_categories:
- 100K<n<1M
source_datasets: []
task_categories:
- multiple-choice
- question-answering
- zero-shot-classification
- text2text-generation
- table-question-answering
- text-generation
- text-classification
- tabular-classification
task_ids:
- multiple-choice-qa
- extractive-qa
- open-domain-qa
- closed-domain-qa
- closed-book-qa
- open-book-qa
- language-modeling
- multi-class-classification
- natural-language-inference
- topic-classification
- multi-label-classification
- tabular-multi-class-classification
- tabular-multi-label-classification
---
# Dataset Card for "UnpredicTable-full" - Dataset of Few-shot Tasks from Tables
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://ethanperez.net/unpredictable
- **Repository:** https://github.com/JunShern/few-shot-adaptation
- **Paper:** Few-shot Adaptation Works with UnpredicTable Data
- **Point of Contact:** junshern@nyu.edu, perez@nyu.edu
### Dataset Summary
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance.
There are several dataset versions available:
* [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full): Starting from the initial WTC corpus of 50M tables, we apply our tables-to-tasks procedure to produce our resulting dataset, [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full), which comprises 413,299 tasks from 23,744 unique websites.
* [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique): This is the same as [UnpredicTable-full](https://huggingface.co/datasets/MicPie/unpredictable_full) but filtered to have a maximum of one task per website. [UnpredicTable-unique](https://huggingface.co/datasets/MicPie/unpredictable_unique) contains exactly 23,744 tasks from 23,744 websites.
* [UnpredicTable-5k](https://huggingface.co/datasets/MicPie/unpredictable_5k): This dataset contains 5k random tables from the full dataset.
* UnpredicTable data subsets based on a manual human quality rating (please see our publication for details of the ratings):
* [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low)
* [UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium)
* [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high)
* UnpredicTable data subsets based on the website of origin:
* [UnpredicTable-baseball-fantasysports-yahoo-com](https://huggingface.co/datasets/MicPie/unpredictable_baseball-fantasysports-yahoo-com)
* [UnpredicTable-bulbapedia-bulbagarden-net](https://huggingface.co/datasets/MicPie/unpredictable_bulbapedia-bulbagarden-net)
* [UnpredicTable-cappex-com](https://huggingface.co/datasets/MicPie/unpredictable_cappex-com)
* [UnpredicTable-cram-com](https://huggingface.co/datasets/MicPie/unpredictable_cram-com)
* [UnpredicTable-dividend-com](https://huggingface.co/datasets/MicPie/unpredictable_dividend-com)
* [UnpredicTable-dummies-com](https://huggingface.co/datasets/MicPie/unpredictable_dummies-com)
* [UnpredicTable-en-wikipedia-org](https://huggingface.co/datasets/MicPie/unpredictable_en-wikipedia-org)
* [UnpredicTable-ensembl-org](https://huggingface.co/datasets/MicPie/unpredictable_ensembl-org)
* [UnpredicTable-gamefaqs-com](https://huggingface.co/datasets/MicPie/unpredictable_gamefaqs-com)
* [UnpredicTable-mgoblog-com](https://huggingface.co/datasets/MicPie/unpredictable_mgoblog-com)
* [UnpredicTable-mmo-champion-com](https://huggingface.co/datasets/MicPie/unpredictable_mmo-champion-com)
* [UnpredicTable-msdn-microsoft-com](https://huggingface.co/datasets/MicPie/unpredictable_msdn-microsoft-com)
* [UnpredicTable-phonearena-com](https://huggingface.co/datasets/MicPie/unpredictable_phonearena-com)
* [UnpredicTable-sittercity-com](https://huggingface.co/datasets/MicPie/unpredictable_sittercity-com)
* [UnpredicTable-sporcle-com](https://huggingface.co/datasets/MicPie/unpredictable_sporcle-com)
* [UnpredicTable-studystack-com](https://huggingface.co/datasets/MicPie/unpredictable_studystack-com)
* [UnpredicTable-support-google-com](https://huggingface.co/datasets/MicPie/unpredictable_support-google-com)
* [UnpredicTable-w3-org](https://huggingface.co/datasets/MicPie/unpredictable_w3-org)
* [UnpredicTable-wiki-openmoko-org](https://huggingface.co/datasets/MicPie/unpredictable_wiki-openmoko-org)
* [UnpredicTable-wkdu-org](https://huggingface.co/datasets/MicPie/unpredictable_wkdu-org)
* UnpredicTable data subsets based on clustering (for the clustering details please see our publication):
* [UnpredicTable-cluster00](https://huggingface.co/datasets/MicPie/unpredictable_cluster00)
* [UnpredicTable-cluster01](https://huggingface.co/datasets/MicPie/unpredictable_cluster01)
* [UnpredicTable-cluster02](https://huggingface.co/datasets/MicPie/unpredictable_cluster02)
* [UnpredicTable-cluster03](https://huggingface.co/datasets/MicPie/unpredictable_cluster03)
* [UnpredicTable-cluster04](https://huggingface.co/datasets/MicPie/unpredictable_cluster04)
* [UnpredicTable-cluster05](https://huggingface.co/datasets/MicPie/unpredictable_cluster05)
* [UnpredicTable-cluster06](https://huggingface.co/datasets/MicPie/unpredictable_cluster06)
* [UnpredicTable-cluster07](https://huggingface.co/datasets/MicPie/unpredictable_cluster07)
* [UnpredicTable-cluster08](https://huggingface.co/datasets/MicPie/unpredictable_cluster08)
* [UnpredicTable-cluster09](https://huggingface.co/datasets/MicPie/unpredictable_cluster09)
* [UnpredicTable-cluster10](https://huggingface.co/datasets/MicPie/unpredictable_cluster10)
* [UnpredicTable-cluster11](https://huggingface.co/datasets/MicPie/unpredictable_cluster11)
* [UnpredicTable-cluster12](https://huggingface.co/datasets/MicPie/unpredictable_cluster12)
* [UnpredicTable-cluster13](https://huggingface.co/datasets/MicPie/unpredictable_cluster13)
* [UnpredicTable-cluster14](https://huggingface.co/datasets/MicPie/unpredictable_cluster14)
* [UnpredicTable-cluster15](https://huggingface.co/datasets/MicPie/unpredictable_cluster15)
* [UnpredicTable-cluster16](https://huggingface.co/datasets/MicPie/unpredictable_cluster16)
* [UnpredicTable-cluster17](https://huggingface.co/datasets/MicPie/unpredictable_cluster17)
* [UnpredicTable-cluster18](https://huggingface.co/datasets/MicPie/unpredictable_cluster18)
* [UnpredicTable-cluster19](https://huggingface.co/datasets/MicPie/unpredictable_cluster19)
* [UnpredicTable-cluster20](https://huggingface.co/datasets/MicPie/unpredictable_cluster20)
* [UnpredicTable-cluster21](https://huggingface.co/datasets/MicPie/unpredictable_cluster21)
* [UnpredicTable-cluster22](https://huggingface.co/datasets/MicPie/unpredictable_cluster22)
* [UnpredicTable-cluster23](https://huggingface.co/datasets/MicPie/unpredictable_cluster23)
* [UnpredicTable-cluster24](https://huggingface.co/datasets/MicPie/unpredictable_cluster24)
* [UnpredicTable-cluster25](https://huggingface.co/datasets/MicPie/unpredictable_cluster25)
* [UnpredicTable-cluster26](https://huggingface.co/datasets/MicPie/unpredictable_cluster26)
* [UnpredicTable-cluster27](https://huggingface.co/datasets/MicPie/unpredictable_cluster27)
* [UnpredicTable-cluster28](https://huggingface.co/datasets/MicPie/unpredictable_cluster28)
* [UnpredicTable-cluster29](https://huggingface.co/datasets/MicPie/unpredictable_cluster29)
* [UnpredicTable-cluster-noise](https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise)
### Supported Tasks and Leaderboards
Since the tables come from the web, the distribution of tasks and topics is very broad. The shape of our dataset is very wide, i.e., we have 1000's of tasks, while each task has only a few examples, compared to most current NLP datasets which are very deep, i.e., 10s of tasks with many examples. This implies that our dataset covers a broad range of potential tasks, e.g., multiple-choice, question-answering, table-question-answering, text-classification, etc.
The intended use of this dataset is to improve few-shot performance by fine-tuning/pre-training on our dataset.
### Languages
English
## Dataset Structure
### Data Instances
Each task is represented as a jsonline file and consists of several few-shot examples. Each example is a dictionary containing a field 'task', which identifies the task, followed by an 'input', 'options', and 'output' field. The 'input' field contains several column elements of the same row in the table, while the 'output' field is a target which represents an individual column of the same row. Each task contains several such examples which can be concatenated as a few-shot task. In the case of multiple choice classification, the 'options' field contains the possible classes that a model needs to choose from.
There are also additional meta-data fields such as 'pageTitle', 'title', 'outputColName', 'url', 'wdcFile'.
### Data Fields
'task': task identifier
'input': column elements of a specific row in the table.
'options': for multiple choice classification, it provides the options to choose from.
'output': target column element of the same row as input.
'pageTitle': the title of the page containing the table.
'outputColName': output column name
'url': url to the website containing the table
'wdcFile': WDC Web Table Corpus file
### Data Splits
The UnpredicTable datasets do not come with additional data splits.
## Dataset Creation
### Curation Rationale
Few-shot training on multi-task datasets has been demonstrated to improve language models' few-shot learning (FSL) performance on new tasks, but it is unclear which training tasks lead to effective downstream task adaptation. Few-shot learning datasets are typically produced with expensive human curation, limiting the scale and diversity of the training tasks available to study. As an alternative source of few-shot data, we automatically extract 413,299 tasks from diverse internet tables. We provide this as a research resource to investigate the relationship between training data and few-shot learning.
### Source Data
#### Initial Data Collection and Normalization
We use internet tables from the English-language Relational Subset of the WDC Web Table Corpus 2015 (WTC). The WTC dataset tables were extracted from the July 2015 Common Crawl web corpus (http://webdatacommons.org/webtables/2015/EnglishStatistics.html). The dataset contains 50,820,165 tables from 323,160 web domains. We then convert the tables into few-shot learning tasks. Please see our publication for more details on the data collection and conversion pipeline.
#### Who are the source language producers?
The dataset is extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/).
### Annotations
#### Annotation process
Manual annotation was only carried out for the [UnpredicTable-rated-low](https://huggingface.co/datasets/MicPie/unpredictable_rated-low),
[UnpredicTable-rated-medium](https://huggingface.co/datasets/MicPie/unpredictable_rated-medium), and [UnpredicTable-rated-high](https://huggingface.co/datasets/MicPie/unpredictable_rated-high) data subsets to rate task quality. Detailed instructions of the annotation instructions can be found in our publication.
#### Who are the annotators?
Annotations were carried out by a lab assistant.
### Personal and Sensitive Information
The data was extracted from [WDC Web Table Corpora](http://webdatacommons.org/webtables/), which in turn extracted tables from the [Common Crawl](https://commoncrawl.org/). We did not filter the data in any way. Thus any user identities or otherwise sensitive information (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history, etc.) might be contained in our dataset.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is intended for use as a research resource to investigate the relationship between training data and few-shot learning. As such, it contains high- and low-quality data, as well as diverse content that may be untruthful or inappropriate. Without careful investigation, it should not be used for training models that will be deployed for use in decision-critical or user-facing situations.
### Discussion of Biases
Since our dataset contains tables that are scraped from the web, it will also contain many toxic, racist, sexist, and otherwise harmful biases and texts. We have not run any analysis on the biases prevalent in our datasets. Neither have we explicitly filtered the content. This implies that a model trained on our dataset may potentially reflect harmful biases and toxic text that exist in our dataset.
### Other Known Limitations
No additional known limitations.
## Additional Information
### Dataset Curators
Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer, Ethan Perez
### Licensing Information
Apache 2.0
### Citation Information
```
@misc{chan2022few,
author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
title = {Few-shot Adaptation Works with UnpredicTable Data},
publisher={arXiv},
year = {2022},
url = {https://arxiv.org/abs/2208.01009}
}
```
|
SUSHMITH/med_images | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': aspirin
'1': benedryl
'2': dolo
'3': paracetmol
'4': zincovit
splits:
- name: train
num_bytes: 291504.35
num_examples: 17
- name: test
num_bytes: 35188.65
num_examples: 3
download_size: 329516
dataset_size: 326693.0
---
# Dataset Card for "med_images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Snehu001/Build_your_own_GenerativeAi22105124012 | ---
license: apache-2.0
---
|
Aratako/Rosebleu-1on1-Dialogues-RP | ---
license: apache-2.0
task_categories:
- text-generation
language:
- ja
tags:
- not-for-all-audiences
- nsfw
size_categories:
- n<1K
---
# Rosebleu-1on1-Dialogues-RP
[@matsuxr](https://huggingface.co/matsuxr)さんが公開している[Rosebleuデータセット](https://gitlab.com/open_contents_datasets/Rosebleu)を加工した[Aratako/Rosebleu-1on1-Dialogues](https://huggingface.co/datasets/Aratako/Rosebleu-1on1-Dialogues)を元に、キャラクターや作品の設定などを付け加えたうえで、ロールプレイ的な文脈になるように加工したデータセットです。
LLMのファインチューニングにおけるロールプレイングタスクの学習を想定しています。
OpenAI APIのように`role`と`content`のペアの形式となっており、`tokenizer.apply_chat_template()`によって簡単に各モデルのチャットテンプレートのデータセットへと変換可能です。
## データセットの詳細
各キャラの設定や各作品の世界観・あらすじなどをWikipediaやニコニコ大百科からまとめ、ロールプレイ向けにシステムメッセージへと埋め込んでいます。
現在、以下の2パターンのデータセットを用意してあります。主に地の文の処理方法が異なります。
1. Rosebleu-1on1-Dialogues-RP-v1.jsonl
地の文を必ずuser側に入れるようにし、LLMの出力となるassistant側はセリフのみとなるようにしています。このデータで学習されたモデルは状況描写などを行わずセリフのみを出力するようになるかもしれません。
3. Rosebleu-1on1-Dialogues-RP-v2.jsonl
地の文をそれぞれの作品の主人公側に入れるようにしています。これは、地の文の多くが主人公視点で書かれていたためです。主人公がassistant側となる場合、assistant側に地の文が入ります。なお、主人公を含まない2キャラの対話の場合は地の文を省いています。なお、こちらは一部最初のuserロールの発話が空白となっているものがあります。mistralのチャットテンプレートを想定しておりその場合はこのままでも問題ないですが、他のテンプレートで利用する場合は加工してください。（最初の1ターンを削除するなど）
## 制約
本来ロールプレイのためのデータとして存在すべきである「シーンのシチュエーション」の情報が含まれていません。同様に、キャラの設定や世界観・あらすじなども全てのシーンで同じものを使っており、そのシーンに適したものではない可能性が高いです。
また、地の文の処理もこれが最適だとは思っておらず、より良い処理の方法があると思います。
## Rosebleuデータセットについて
元データセットから概要文を引用します。
> Rosebleuブランドの代表を務められていた青猫様にご提供いただいた、 解散したRosebleuブランドのゲームタイトルのうち、権利譲渡等を行っていない10タイトルについてのシナリオから作成したデータセットです。JSONL形式になっています。主には大規模言語モデルのファインチューニング用途を想定していますが、LICENSEに違反しない用途ならばどんな用途でも問題ありません。
> https://ja.wikipedia.org/wiki/Rosebleu
## 注意
Rosebleuデータセットは成人向け美少女ゲームのシナリオから作成されており、本データセット中にもセクシャルな描写を含むテキストが存在します。
## ライセンス
元のデータセットはapache-2.0ライセンスで配布されています。以下、引用です。
> 「学習用データセットに加工したものは、自由に配布頂いてかまいません。 利用目的について営利・非営利の制限は不要です。」という内容でお預かりしたので、APACHE LICENSE, VERSION 2.0とします。(C)Rosebleu
本データセットも元データセットと同様にapache-2.0ライセンスの元公開いたします。 |
open-llm-leaderboard/details_augtoma__qCammel-70x | ---
pretty_name: Evaluation run of augtoma/qCammel-70x
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [augtoma/qCammel-70x](https://huggingface.co/augtoma/qCammel-70x) on the [Open\
\ LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_augtoma__qCammel-70x\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-24T00:38:03.634221](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70x/blob/main/results_2023-09-24T00-38-03.634221.json)(note\
\ that their might be results for other tasks in the repos if successive evals didn't\
\ cover the same tasks. You find each in the results and the \"latest\" split for\
\ each eval):\n\n```python\n{\n \"all\": {\n \"em\": 0.033766778523489936,\n\
\ \"em_stderr\": 0.001849802869119515,\n \"f1\": 0.10340918624161041,\n\
\ \"f1_stderr\": 0.0022106009828094797,\n \"acc\": 0.5700654570173166,\n\
\ \"acc_stderr\": 0.011407494958111332\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.033766778523489936,\n \"em_stderr\": 0.001849802869119515,\n\
\ \"f1\": 0.10340918624161041,\n \"f1_stderr\": 0.0022106009828094797\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.2971948445792267,\n \
\ \"acc_stderr\": 0.012588685966624186\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598479\n\
\ }\n}\n```"
repo_url: https://huggingface.co/augtoma/qCammel-70x
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|arc:challenge|25_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_24T00_38_03.634221
path:
- '**/details_harness|drop|3_2023-09-24T00-38-03.634221.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-24T00-38-03.634221.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_24T00_38_03.634221
path:
- '**/details_harness|gsm8k|5_2023-09-24T00-38-03.634221.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-24T00-38-03.634221.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hellaswag|10_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T05:27:12.496393.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T05:27:12.496393.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-18T05:27:12.496393.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_24T00_38_03.634221
path:
- '**/details_harness|winogrande|5_2023-09-24T00-38-03.634221.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-24T00-38-03.634221.parquet'
- config_name: results
data_files:
- split: 2023_08_18T05_27_12.496393
path:
- results_2023-08-18T05:27:12.496393.parquet
- split: 2023_09_24T00_38_03.634221
path:
- results_2023-09-24T00-38-03.634221.parquet
- split: latest
path:
- results_2023-09-24T00-38-03.634221.parquet
---
# Dataset Card for Evaluation run of augtoma/qCammel-70x
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/augtoma/qCammel-70x
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [augtoma/qCammel-70x](https://huggingface.co/augtoma/qCammel-70x) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_augtoma__qCammel-70x",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-24T00:38:03.634221](https://huggingface.co/datasets/open-llm-leaderboard/details_augtoma__qCammel-70x/blob/main/results_2023-09-24T00-38-03.634221.json)(note that their might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):
```python
{
"all": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797,
"acc": 0.5700654570173166,
"acc_stderr": 0.011407494958111332
},
"harness|drop|3": {
"em": 0.033766778523489936,
"em_stderr": 0.001849802869119515,
"f1": 0.10340918624161041,
"f1_stderr": 0.0022106009828094797
},
"harness|gsm8k|5": {
"acc": 0.2971948445792267,
"acc_stderr": 0.012588685966624186
},
"harness|winogrande|5": {
"acc": 0.8429360694554064,
"acc_stderr": 0.010226303949598479
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
mask-distilled-onesec-cv12-each-chunk-uniq/chunk_36 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 484676928.0
num_examples: 95184
download_size: 494822851
dataset_size: 484676928.0
---
# Dataset Card for "chunk_36"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
arieg/cluster03_medium_10 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '000534'
'1': 000821
'2': '001102'
'3': 005381
'4': 006802
'5': 008345
'6': 008357
'7': 011682
'8': '011776'
'9': 014586
'10': 016994
'11': 020369
'12': '023353'
'13': '026600'
'14': 032338
'15': '036146'
'16': 046928
'17': 048440
'18': 048465
'19': 048931
'20': '050752'
'21': 052389
'22': '052647'
'23': '056523'
'24': 057820
'25': 061492
'26': '062005'
'27': 064840
'28': 066649
'29': '067365'
'30': 067638
'31': 073169
'32': 075936
'33': 084484
'34': 086262
'35': 087192
'36': 087430
'37': 087431
'38': 088486
'39': 090804
'40': 091459
'41': 097373
'42': 097847
'43': '100976'
'44': '104724'
'45': '106937'
'46': '112196'
'47': '114242'
'48': '114942'
'49': '115473'
'50': '116098'
'51': '116237'
'52': '116467'
'53': '116489'
'54': '119896'
'55': '122647'
'56': '122911'
'57': '122936'
'58': '125238'
'59': '125622'
'60': '125825'
'61': '126229'
'62': '126230'
'63': '126283'
'64': '126410'
'65': '127349'
'66': '127498'
'67': '127648'
'68': '142671'
'69': '145609'
'70': '148610'
'71': '153956'
splits:
- name: train
num_bytes: 35812864.0
num_examples: 720
download_size: 33578199
dataset_size: 35812864.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
chucklechamp26/voices | ---
license: mit
---
|
CyberHarem/kazusa_bluearchive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kazusa/杏山カズサ/和纱 (Blue Archive)
This is the dataset of kazusa/杏山カズサ/和纱 (Blue Archive), containing 500 images and their tags.
The core tags of this character are `black_hair, cat_ears, animal_ears, colored_inner_hair, multicolored_hair, pink_hair, short_hair, halo, two-tone_hair, extra_ears, red_eyes, hair_ornament, hairclip, pink_halo, breasts, pink_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kazusa_bluearchive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 847.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kazusa_bluearchive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1362 | 1.76 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kazusa_bluearchive/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/kazusa_bluearchive',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 23 |  |  |  |  |  | 1girl, blush, solo_focus, 1boy, hetero, open_mouth, penis, nipples, looking_at_viewer, navel, completely_nude, collarbone, pov, pussy, bar_censor, black_choker, sex, sweat, vaginal, large_breasts, female_pubic_hair, heart |
| 1 | 37 |  |  |  |  |  | 1girl, black_choker, black_jacket, hooded_jacket, long_sleeves, looking_at_viewer, school_uniform, solo, pleated_skirt, white_skirt, pink_neckerchief, black_pantyhose, blush, green_sailor_collar, simple_background, white_background, hood_down, collarbone, closed_mouth, sitting, black_hoodie |
| 2 | 5 |  |  |  |  |  | 1girl, black_choker, black_jacket, black_pantyhose, green_sailor_collar, hooded_jacket, legs, long_sleeves, looking_at_viewer, no_shoes, pink_neckerchief, simple_background, sitting, soles, solo, toes, white_background, blush, full_body, hood_down, pleated_skirt, school_uniform, white_skirt, black_hoodie, closed_mouth, foot_focus, knees_up, miniskirt |
| 3 | 12 |  |  |  |  |  | 1girl, black_choker, black_jacket, blush, green_sailor_collar, hooded_jacket, looking_at_viewer, school_uniform, upper_body, long_sleeves, pink_neckerchief, solo, hoodie, closed_mouth, simple_background, white_background, collarbone |
| 4 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, short_sleeves, solo, white_shirt, blush, jacket_around_waist, pleated_skirt, black_choker, black_jacket, white_skirt, bracelet, thigh_strap, alternate_costume, electric_guitar, open_mouth, simple_background, white_background, black_mask, collarbone, holding_microphone, mouth_mask, smile, tongue_out |
| 5 | 6 |  |  |  |  |  | 1girl, alternate_costume, black_choker, blush, collarbone, navel, solo, stomach, thighs, black_bikini, looking_at_viewer, cleavage, cowboy_shot, large_breasts, simple_background, bare_shoulders, closed_mouth, medium_breasts, white_background |
| 6 | 9 |  |  |  |  |  | 1girl, alternate_costume, bare_shoulders, detached_collar, looking_at_viewer, solo, black_leotard, black_pantyhose, playboy_bunny, strapless_leotard, blush, bowtie, medium_breasts, simple_background, wrist_cuffs, closed_mouth, fake_animal_ears, rabbit_ears, white_background, cleavage, covered_navel, large_breasts, sitting |
| 7 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, frills, black_dress, enmaided, maid_apron, maid_headdress, medium_breasts, white_apron, black_thighhighs, cleavage, gloves, puffy_short_sleeves, black_choker, cat_tail, closed_mouth, garter_straps, sitting |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | solo_focus | 1boy | hetero | open_mouth | penis | nipples | looking_at_viewer | navel | completely_nude | collarbone | pov | pussy | bar_censor | black_choker | sex | sweat | vaginal | large_breasts | female_pubic_hair | heart | black_jacket | hooded_jacket | long_sleeves | school_uniform | solo | pleated_skirt | white_skirt | pink_neckerchief | black_pantyhose | green_sailor_collar | simple_background | white_background | hood_down | closed_mouth | sitting | black_hoodie | legs | no_shoes | soles | toes | full_body | foot_focus | knees_up | miniskirt | upper_body | hoodie | short_sleeves | white_shirt | jacket_around_waist | bracelet | thigh_strap | alternate_costume | electric_guitar | black_mask | holding_microphone | mouth_mask | smile | tongue_out | stomach | thighs | black_bikini | cleavage | cowboy_shot | bare_shoulders | medium_breasts | detached_collar | black_leotard | playboy_bunny | strapless_leotard | bowtie | wrist_cuffs | fake_animal_ears | rabbit_ears | covered_navel | frills | black_dress | enmaided | maid_apron | maid_headdress | white_apron | black_thighhighs | gloves | puffy_short_sleeves | cat_tail | garter_straps |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:-------------|:-------|:---------|:-------------|:--------|:----------|:--------------------|:--------|:------------------|:-------------|:------|:--------|:-------------|:---------------|:------|:--------|:----------|:----------------|:--------------------|:--------|:---------------|:----------------|:---------------|:-----------------|:-------|:----------------|:--------------|:-------------------|:------------------|:----------------------|:--------------------|:-------------------|:------------|:---------------|:----------|:---------------|:-------|:-----------|:--------|:-------|:------------|:-------------|:-----------|:------------|:-------------|:---------|:----------------|:--------------|:----------------------|:-----------|:--------------|:--------------------|:------------------|:-------------|:---------------------|:-------------|:--------|:-------------|:----------|:---------|:---------------|:-----------|:--------------|:-----------------|:-----------------|:------------------|:----------------|:----------------|:--------------------|:---------|:--------------|:-------------------|:--------------|:----------------|:---------|:--------------|:-----------|:-------------|:-----------------|:--------------|:-------------------|:---------|:----------------------|:-----------|:----------------|
| 0 | 23 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 37 |  |  |  |  |  | X | X | | | | | | | X | | | X | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 5 |  |  |  |  |  | X | X | | | | | | | X | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | X | | | | | | | X | | | X | | | | X | | | | | | | X | X | X | X | X | | | X | | X | X | X | | X | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 10 |  |  |  |  |  | X | X | | | | X | | | X | | | X | | | | X | | | | | | | X | | | | X | X | X | | | | X | X | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | | | | | | | X | X | | X | | | | X | | | | X | | | | | | | X | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | X | X | | | | | | | X | | | | | | | | | | | X | | | | | | | X | | | | X | | X | X | | X | X | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | | | | | | | X | | | | | | | X | | | | | | | | | | | X | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_TheBloke__WizardLM-7B-uncensored-GPTQ | ---
pretty_name: Evaluation run of TheBloke/WizardLM-7B-uncensored-GPTQ
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TheBloke/WizardLM-7B-uncensored-GPTQ](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the 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__WizardLM-7B-uncensored-GPTQ\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-02T12:59:15.195874](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-7B-uncensored-GPTQ/blob/main/results_2023-12-02T12-59-15.195874.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\": 0.0,\n \"\
acc_stderr\": 0.0\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \
\ \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- '**/details_harness|drop|3_2023-10-21T21-04-26.590858.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T21-04-26.590858.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- '**/details_harness|gsm8k|5_2023-10-21T21-04-26.590858.parquet'
- split: 2023_12_02T12_59_15.195874
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-59-15.195874.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-02T12-59-15.195874.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- '**/details_harness|winogrande|5_2023-10-21T21-04-26.590858.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T21-04-26.590858.parquet'
- config_name: results
data_files:
- split: 2023_10_21T21_04_26.590858
path:
- results_2023-10-21T21-04-26.590858.parquet
- split: 2023_12_02T12_59_15.195874
path:
- results_2023-12-02T12-59-15.195874.parquet
- split: latest
path:
- results_2023-12-02T12-59-15.195874.parquet
---
# Dataset Card for Evaluation run of TheBloke/WizardLM-7B-uncensored-GPTQ
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [TheBloke/WizardLM-7B-uncensored-GPTQ](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the 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__WizardLM-7B-uncensored-GPTQ",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-02T12:59:15.195874](https://huggingface.co/datasets/open-llm-leaderboard/details_TheBloke__WizardLM-7B-uncensored-GPTQ/blob/main/results_2023-12-02T12-59-15.195874.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": 0.0,
"acc_stderr": 0.0
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
taeminlee/Ko-StrategyQA | ---
language:
- ko
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- Ko-StrategyQA
task_categories:
- text-retrieval
task_ids:
- document-retrieval
config_names:
- corpus
tags:
- text-retrieval
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 236940
num_examples: 4377
- name: dev
num_bytes: 61724
num_examples: 1145
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 7021046
num_examples: 9251
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 244634
num_examples: 2833
configs:
- config_name: default
data_files:
- split: train
path: qrels/train.jsonl
- split: dev
path: qrels/dev.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
---
# Ko-StrategyQA
This dataset represents a conversion of the [Ko-StrategyQA dataset](https://huggingface.co/datasets/NomaDamas/Ko-StrategyQA) into the [BeIR](https://github.com/beir-cellar/beir) format, making it compatible for use with [mteb](https://github.com/embeddings-benchmark/mteb).
The original dataset was designed for multi-hop QA, so we processed the data accordingly. First, we grouped the evidence documents tagged by annotators into sets, and excluded unit questions containing 'no_evidence' or 'operation'. |
funtimes/descr | ---
license: cc
---
|
TypicaAI/pii-masking-60k_fr | ---
dataset_info:
features:
- name: masked_text
dtype: string
- name: unmasked_text
dtype: string
- name: privacy_mask
dtype: string
- name: span_labels
dtype: string
- name: bio_labels
sequence: string
- name: tokenised_text
sequence: string
splits:
- name: train
num_bytes: 105030283
num_examples: 61918
download_size: 31820221
dataset_size: 105030283
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
task_categories:
- token-classification
language:
- fr
tags:
- PII
- Privacy
- NER
pretty_name: typica.ai French PII dataset
size_categories:
- 10K<n<100K
---
# typica.ai - PII French dataset
<!-- Provide a quick summary of the dataset. -->
This PII French dataset TypicaAI/pii-masking-60k_fr is based on the World's largest open-source privacy dataset: ai4privacy/pii-masking-200k. The original dataset ai4privacy/pii-masking-200k was filtered out, using a BERT-based language classifier, to keep only French rows.
For more information, please refer to the dataset [ai4privacy/pii-masking-200k](https://huggingface.co/datasets/ai4privacy/pii-masking-200k).
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
If you use our work, please cite: Hicham Assoudi, Ph.D. (2024). typica.ai - PII French dataset. https://typica.ai/
## Dataset Card Authors
Hicham Assoudi, Ph.D.
## Dataset Card Contact
For any questions, comments you can contact me at assoudi@typica.ai |
rjcarne/rc-custom-copilot | ---
dataset_info:
features:
- name: repo_id
dtype: string
- name: file_path
dtype: string
- name: content
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 15269268
num_examples: 554
download_size: 4948949
dataset_size: 15269268
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sankovic/shozz | ---
license: openrail
---
|
youngdicey/sample | ---
license: openrail
---
|
open-llm-leaderboard/details_CorticalStack__mistral-7b-distilabel-truthy-dpo | ---
pretty_name: Evaluation run of CorticalStack/mistral-7b-distilabel-truthy-dpo
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [CorticalStack/mistral-7b-distilabel-truthy-dpo](https://huggingface.co/CorticalStack/mistral-7b-distilabel-truthy-dpo)\
\ 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_CorticalStack__mistral-7b-distilabel-truthy-dpo\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-06T22:39:34.229467](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-distilabel-truthy-dpo/blob/main/results_2024-03-06T22-39-34.229467.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\": 0.6381129080176873,\n\
\ \"acc_stderr\": 0.032168777003792545,\n \"acc_norm\": 0.6445873604303822,\n\
\ \"acc_norm_stderr\": 0.03281588417629927,\n \"mc1\": 0.2876376988984088,\n\
\ \"mc1_stderr\": 0.01584631510139481,\n \"mc2\": 0.45119649594650557,\n\
\ \"mc2_stderr\": 0.01429361604820163\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.560580204778157,\n \"acc_stderr\": 0.014503747823580122,\n\
\ \"acc_norm\": 0.6092150170648464,\n \"acc_norm_stderr\": 0.01425856388051378\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6346345349531965,\n\
\ \"acc_stderr\": 0.004805483767055347,\n \"acc_norm\": 0.836387173869747,\n\
\ \"acc_norm_stderr\": 0.003691678495767968\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.04461960433384741,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.04461960433384741\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6222222222222222,\n\
\ \"acc_stderr\": 0.04188307537595853,\n \"acc_norm\": 0.6222222222222222,\n\
\ \"acc_norm_stderr\": 0.04188307537595853\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.03878139888797611,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.03878139888797611\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.58,\n\
\ \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\": 0.58,\n \
\ \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6867924528301886,\n \"acc_stderr\": 0.028544793319055326,\n\
\ \"acc_norm\": 0.6867924528301886,\n \"acc_norm_stderr\": 0.028544793319055326\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|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-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\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\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.5914893617021276,\n \"acc_stderr\": 0.032134180267015755,\n\
\ \"acc_norm\": 0.5914893617021276,\n \"acc_norm_stderr\": 0.032134180267015755\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3783068783068783,\n \"acc_stderr\": 0.02497695405315525,\n \"\
acc_norm\": 0.3783068783068783,\n \"acc_norm_stderr\": 0.02497695405315525\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.40476190476190477,\n\
\ \"acc_stderr\": 0.04390259265377563,\n \"acc_norm\": 0.40476190476190477,\n\
\ \"acc_norm_stderr\": 0.04390259265377563\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7645161290322581,\n\
\ \"acc_stderr\": 0.024137632429337714,\n \"acc_norm\": 0.7645161290322581,\n\
\ \"acc_norm_stderr\": 0.024137632429337714\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.68,\n \"acc_stderr\": 0.04688261722621505,\n \"acc_norm\"\
: 0.68,\n \"acc_norm_stderr\": 0.04688261722621505\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586808,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586808\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8704663212435233,\n \"acc_stderr\": 0.02423353229775873,\n\
\ \"acc_norm\": 0.8704663212435233,\n \"acc_norm_stderr\": 0.02423353229775873\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6538461538461539,\n \"acc_stderr\": 0.024121125416941197,\n\
\ \"acc_norm\": 0.6538461538461539,\n \"acc_norm_stderr\": 0.024121125416941197\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.35185185185185186,\n \"acc_stderr\": 0.029116617606083008,\n \
\ \"acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.029116617606083008\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135363,\n\
\ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135363\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.304635761589404,\n \"acc_stderr\": 0.03757949922943343,\n \"acc_norm\"\
: 0.304635761589404,\n \"acc_norm_stderr\": 0.03757949922943343\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.8201834862385321,\n\
\ \"acc_stderr\": 0.016465345467391545,\n \"acc_norm\": 0.8201834862385321,\n\
\ \"acc_norm_stderr\": 0.016465345467391545\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.5740740740740741,\n \"acc_stderr\": 0.03372343271653062,\n\
\ \"acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.03372343271653062\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7721518987341772,\n \"acc_stderr\": 0.027303484599069432,\n \
\ \"acc_norm\": 0.7721518987341772,\n \"acc_norm_stderr\": 0.027303484599069432\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.030636591348699803,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.030636591348699803\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.034981493854624714,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.034981493854624714\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7592592592592593,\n\
\ \"acc_stderr\": 0.04133119440243838,\n \"acc_norm\": 0.7592592592592593,\n\
\ \"acc_norm_stderr\": 0.04133119440243838\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7914110429447853,\n \"acc_stderr\": 0.031921934489347235,\n\
\ \"acc_norm\": 0.7914110429447853,\n \"acc_norm_stderr\": 0.031921934489347235\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.49107142857142855,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.49107142857142855,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8349514563106796,\n \"acc_stderr\": 0.036756688322331886,\n\
\ \"acc_norm\": 0.8349514563106796,\n \"acc_norm_stderr\": 0.036756688322331886\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.02158649400128139,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.02158649400128139\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8122605363984674,\n\
\ \"acc_stderr\": 0.013964393769899126,\n \"acc_norm\": 0.8122605363984674,\n\
\ \"acc_norm_stderr\": 0.013964393769899126\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\
\ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3229050279329609,\n\
\ \"acc_stderr\": 0.015638440380241484,\n \"acc_norm\": 0.3229050279329609,\n\
\ \"acc_norm_stderr\": 0.015638440380241484\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.02463004897982478,\n\
\ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.02463004897982478\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.026236965881153262,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.026236965881153262\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.45045632333767927,\n\
\ \"acc_stderr\": 0.012707390438502346,\n \"acc_norm\": 0.45045632333767927,\n\
\ \"acc_norm_stderr\": 0.012707390438502346\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.02824568739146292,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.02824568739146292\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6879084967320261,\n \"acc_stderr\": 0.018745011201277657,\n \
\ \"acc_norm\": 0.6879084967320261,\n \"acc_norm_stderr\": 0.018745011201277657\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8109452736318408,\n\
\ \"acc_stderr\": 0.02768691358801302,\n \"acc_norm\": 0.8109452736318408,\n\
\ \"acc_norm_stderr\": 0.02768691358801302\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.038695433234721015,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.038695433234721015\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2876376988984088,\n\
\ \"mc1_stderr\": 0.01584631510139481,\n \"mc2\": 0.45119649594650557,\n\
\ \"mc2_stderr\": 0.01429361604820163\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7837411207576953,\n \"acc_stderr\": 0.011570614861409347\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.36087945413191813,\n \
\ \"acc_stderr\": 0.013228626753925138\n }\n}\n```"
repo_url: https://huggingface.co/CorticalStack/mistral-7b-distilabel-truthy-dpo
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|arc:challenge|25_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|gsm8k|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hellaswag|10_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-06T22-39-34.229467.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-06T22-39-34.229467.parquet'
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data_files:
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path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-06T22-39-34.229467.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- '**/details_harness|winogrande|5_2024-03-06T22-39-34.229467.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-06T22-39-34.229467.parquet'
- config_name: results
data_files:
- split: 2024_03_06T22_39_34.229467
path:
- results_2024-03-06T22-39-34.229467.parquet
- split: latest
path:
- results_2024-03-06T22-39-34.229467.parquet
---
# Dataset Card for Evaluation run of CorticalStack/mistral-7b-distilabel-truthy-dpo
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [CorticalStack/mistral-7b-distilabel-truthy-dpo](https://huggingface.co/CorticalStack/mistral-7b-distilabel-truthy-dpo) 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_CorticalStack__mistral-7b-distilabel-truthy-dpo",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-06T22:39:34.229467](https://huggingface.co/datasets/open-llm-leaderboard/details_CorticalStack__mistral-7b-distilabel-truthy-dpo/blob/main/results_2024-03-06T22-39-34.229467.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": 0.6381129080176873,
"acc_stderr": 0.032168777003792545,
"acc_norm": 0.6445873604303822,
"acc_norm_stderr": 0.03281588417629927,
"mc1": 0.2876376988984088,
"mc1_stderr": 0.01584631510139481,
"mc2": 0.45119649594650557,
"mc2_stderr": 0.01429361604820163
},
"harness|arc:challenge|25": {
"acc": 0.560580204778157,
"acc_stderr": 0.014503747823580122,
"acc_norm": 0.6092150170648464,
"acc_norm_stderr": 0.01425856388051378
},
"harness|hellaswag|10": {
"acc": 0.6346345349531965,
"acc_stderr": 0.004805483767055347,
"acc_norm": 0.836387173869747,
"acc_norm_stderr": 0.003691678495767968
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.27,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.27,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6222222222222222,
"acc_stderr": 0.04188307537595853,
"acc_norm": 0.6222222222222222,
"acc_norm_stderr": 0.04188307537595853
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.03878139888797611,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.03878139888797611
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6867924528301886,
"acc_stderr": 0.028544793319055326,
"acc_norm": 0.6867924528301886,
"acc_norm_stderr": 0.028544793319055326
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.53,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.53,
"acc_norm_stderr": 0.05016135580465919
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"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": 0.38235294117647056,
"acc_stderr": 0.04835503696107223,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5914893617021276,
"acc_stderr": 0.032134180267015755,
"acc_norm": 0.5914893617021276,
"acc_norm_stderr": 0.032134180267015755
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3783068783068783,
"acc_stderr": 0.02497695405315525,
"acc_norm": 0.3783068783068783,
"acc_norm_stderr": 0.02497695405315525
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.40476190476190477,
"acc_stderr": 0.04390259265377563,
"acc_norm": 0.40476190476190477,
"acc_norm_stderr": 0.04390259265377563
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7645161290322581,
"acc_stderr": 0.024137632429337714,
"acc_norm": 0.7645161290322581,
"acc_norm_stderr": 0.024137632429337714
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7757575757575758,
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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] |
ademax/legal_document_vi | ---
dataset_info:
features:
- name: subject
dtype: string
- name: meta
struct:
- name: effective_date
dtype: string
- name: issuing_agency
dtype: string
- name: promulgation_date
dtype: string
- name: sign_number
dtype: string
- name: signer
dtype: string
- name: type
dtype: string
- name: url
dtype: string
- name: text
dtype: string
- name: metadata_coQuanBanHanh
dtype: string
- name: metadata_coQuanBanHanh_conf
dtype: bool
- name: metadata_soHieu
dtype: string
- name: metadata_soHieu_conf
dtype: bool
- name: metadata_loaiVanBan
dtype: string
- name: metadata_loaiVanBan_conf
dtype: float64
- name: metadata_ngayBanHanh
dtype: string
- name: metadata_ngayBanHanh_conf
dtype: float64
- name: metadata_trichYeu
dtype: string
- name: metadata_trichYeu_conf
dtype: float64
- name: metadata_nguoiKy
dtype: string
- name: metadata_nguoiKy_conf
dtype: bool
splits:
- name: train
num_bytes: 7768076795
num_examples: 424062
download_size: 2688089919
dataset_size: 7768076795
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "legal_document_vi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bharadwajkg/planogram-sample-sd-data3 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 15268718.0
num_examples: 20
download_size: 14760935
dataset_size: 15268718.0
---
# Dataset Card for "planogram-sample-sd-data3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LambdaTests/VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_15_1000 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: response
dtype: string
splits:
- name: train
num_bytes: 971
num_examples: 32
download_size: 2157
dataset_size: 971
---
# Dataset Card for "VQAv2Validation_ViT_H_14_A_T_C_Q_benchmarks_partition_global_15_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
duxx/orca-math-word-problems-tr | ---
language:
- tr
tags:
- math
task_categories:
- question-answering
size_categories:
- 100K<n<1M
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 122988878
num_examples: 126588
download_size: 48867861
dataset_size: 122988878
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
enriqueaf/molinillo_pimienta | ---
license: gpl-3.0
---
|
kaleemWaheed/twitter_dataset_1713128788 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 29589
num_examples: 79
download_size: 17359
dataset_size: 29589
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
tahrirchi/uz-books | ---
configs:
- config_name: default
data_files:
- split: original
path: data/original-*
- split: lat
path: data/lat-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: original
num_bytes: 19244856855
num_examples: 39712
- name: lat
num_bytes: 13705512346
num_examples: 39712
download_size: 16984559355
dataset_size: 32950369201
annotations_creators:
- no-annotation
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
multilinguality:
- monolingual
language:
- uz
size_categories:
- 10M<n<100M
pretty_name: UzBooks
license: apache-2.0
tags:
- uz
- books
---
# Dataset Card for BookCorpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://tahrirchi.uz/grammatika-tekshiruvi](https://tahrirchi.uz/grammatika-tekshiruvi)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 16.98 GB
- **Size of the generated dataset:** 32.95 GB
- **Total amount of disk used:** 49.93 GB
### Dataset Summary
In an effort to democratize research on low-resource languages, we release UzBooks dataset, a cleaned book corpus consisting of nearly 40000 books in Uzbek Language divided into two branches: "original" and "lat," representing the OCRed (Latin and Cyrillic) and fully Latin versions of the texts, respectively.
Please refer to our [blogpost](https://tahrirchi.uz/grammatika-tekshiruvi) and paper (Coming soon!) for further details.
To load and use dataset, run this script:
```python
from datasets import load_dataset
uz_books=load_dataset("tahrirchi/uz-books")
```
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 16.98 GB
- **Size of the generated dataset:** 32.95 GB
- **Total amount of disk used:** 49.93 GB
An example of 'train' looks as follows.
```
{
"text": "Hamsa\nAlisher Navoiy ..."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `text`: a `string` feature that contains text of the books.
### Data Splits
| name | |
|-----------------|--------:|
| original | 39712 |
| lat | 39712 |
## Dataset Creation
The books have been crawled from various internet sources and preprocessed using Optical Character Recognition techniques in [Tesseract OCR Engine](https://github.com/tesseract-ocr/tesseract). The latin version is created by converting the original dataset with highly curated scripts in order to put more emphasis on the research and development of the field.
## Citation
Please cite this model using the following format:
```
@online{Mamasaidov2023UzBooks,
author = {Mukhammadsaid Mamasaidov and Abror Shopulatov},
title = {UzBooks dataset},
year = {2023},
url = {https://huggingface.co/datasets/tahrirchi/uz-books},
note = {Accessed: 2023-10-28}, % change this date
urldate = {2023-10-28} % change this date
}
```
## Gratitude
We are thankful to these awesome organizations and people for helping to make it happen:
- [Ilya Gusev](https://github.com/IlyaGusev/): for advise throughout the process
- [David Dale](https://daviddale.ru): for advise throughout the process
## Contacts
We believe that this work will enable and inspire all enthusiasts around the world to open the hidden beauty of low-resource languages, in particular Uzbek.
For further development and issues about the dataset, please use m.mamasaidov@tahrirchi.uz or a.shopolatov@tahrirchi.uz to contact. |
AdapterOcean/augmentatio-standardized_cluster_2 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 73895191
num_examples: 7335
download_size: 20782283
dataset_size: 73895191
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "augmentatio-standardized_cluster_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rajveer43/QnAMedicDaataset | ---
task_categories:
- question-answering
tags:
- medical
size_categories:
- 10K<n<100K
--- |
insly/language | ---
license: unknown
language:
- et
- en
pretty_name: insly
--- |
muchkanensys/PNG_Model_Grid_Script | ---
license: unlicense
---
PNGからプロンプトとシードをとってきてグリッド作成 |
isaacrehg/poetry-instructions | ---
dataset_info:
features:
- name: conversation
dtype: string
splits:
- name: train
num_bytes: 87758119
num_examples: 1322
- name: validation
num_bytes: 7731418
num_examples: 111
- name: test
num_bytes: 27041394
num_examples: 331
download_size: 63044464
dataset_size: 122530931
---
# Dataset Card for "poetry-instructions"
A dataset of user-assistant dialogue instructions for guided poetry creation.
Poems used were taken from [merve/poetry](https://huggingface.co/datasets/merve/poetry) and [matthh/gutenberg-poetry-corpus](https://huggingface.co/datasets/matthh/gutenberg-poetry-corpus).
The dataset contains dialogues in the following formats:
- Poetry Completion:
```
User: Can you continue this poem for me? <poem_start>
Assistant: Sure, a continuation for this poem could be: <poem end>
```
- Create poem in style of (?):
```
User: Can you write a poem for me in the style of <author>?
Assistant: Sure, here's a poem in the style of <author>: <poem>
```
- Creat poem about (?):
```
User: Can you write me a poem about <keywords (extracted using keyphrase model)>?
Assistant: Sure, here's a poem about <keywords>: <poem>
```
- Create poem about (?) in the style of (?):
```
User: Can you write me a poem about <keywords> in the style of <author>?
Assistant: Sure, here's a poem about <keywords> in the style of <author>: <poem>
```
|
korexyz/unsplash-people-v4 | ---
dataset_info:
features:
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1138123.0
num_examples: 4500
download_size: 307210
dataset_size: 1138123.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
sordonia/ultrachat-templated-ia-flat | ---
dataset_info:
features:
- name: source
dtype: string
- name: target
dtype: string
- name: task_source
dtype: string
- name: task_name
dtype: string
- name: split
dtype: string
splits:
- name: train
num_bytes: 742393824
num_examples: 320000
download_size: 418519900
dataset_size: 742393824
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ultrachat-templated-ia-flat"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
StofEzz/dataset_c_voice0.2 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 48000
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 72228883.0
num_examples: 2000
- name: test
num_bytes: 4218655.0
num_examples: 100
- name: validation
num_bytes: 3852928.0
num_examples: 100
download_size: 67578358
dataset_size: 80300466.0
---
# Dataset Card for "dataset_c_voice0.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
taesiri/GameplayCaptions-Gemini-pro-vision | ---
dataset_info:
features:
- name: id
dtype: string
- name: game_name
dtype: string
- name: youtube_video_id
dtype: string
- name: category
dtype: string
- name: file_path
dtype: string
- name: gemini_caption
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 75482354852.111
num_examples: 70673
download_size: 75763143507
dataset_size: 75482354852.111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BangumiBase/fairytail | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Fairy Tail
This is the image base of bangumi Fairy Tail, we detected 270 characters, 33650 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:----------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|
| 0 | 1894 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 83 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 62 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 49 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 36 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 11 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 76 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 162 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 4062 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 52 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 210 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 441 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 481 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 2387 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 108 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 200 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 137 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 481 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 81 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 320 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 225 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 42 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 81 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 167 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 99 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 112 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 81 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 27 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 108 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 717 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 221 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 61 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 37 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 20 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 86 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 55 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 27 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 80 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 50 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 643 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 352 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 35 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 94 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 50 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 50 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 29 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 62 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 21 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 26 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 30 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 253 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 39 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 126 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 127 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 106 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 34 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 46 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 109 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 72 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 45 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 25 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 59 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 53 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 56 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 149 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 311 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 64 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 58 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 53 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 21 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 27 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 57 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 44 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 26 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 8 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 29 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 37 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 379 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 188 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 1209 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 413 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 14 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 19 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 34 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 56 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 20 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 27 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 16 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 28 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 29 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 52 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 30 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 29 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 21 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 36 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 81 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 30 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 85 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 34 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 80 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 96 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 27 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 57 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 33 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 38 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 401 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 35 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 27 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 42 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| 109 | 27 | [Download](109/dataset.zip) |  |  |  |  |  |  |  |  |
| 110 | 24 | [Download](110/dataset.zip) |  |  |  |  |  |  |  |  |
| 111 | 51 | [Download](111/dataset.zip) |  |  |  |  |  |  |  |  |
| 112 | 33 | [Download](112/dataset.zip) |  |  |  |  |  |  |  |  |
| 113 | 15 | [Download](113/dataset.zip) |  |  |  |  |  |  |  |  |
| 114 | 43 | [Download](114/dataset.zip) |  |  |  |  |  |  |  |  |
| 115 | 64 | [Download](115/dataset.zip) |  |  |  |  |  |  |  |  |
| 116 | 171 | [Download](116/dataset.zip) |  |  |  |  |  |  |  |  |
| 117 | 358 | [Download](117/dataset.zip) |  |  |  |  |  |  |  |  |
| 118 | 99 | [Download](118/dataset.zip) |  |  |  |  |  |  |  |  |
| 119 | 15 | [Download](119/dataset.zip) |  |  |  |  |  |  |  |  |
| 120 | 29 | [Download](120/dataset.zip) |  |  |  |  |  |  |  |  |
| 121 | 33 | [Download](121/dataset.zip) |  |  |  |  |  |  |  |  |
| 122 | 18 | [Download](122/dataset.zip) |  |  |  |  |  |  |  |  |
| 123 | 31 | [Download](123/dataset.zip) |  |  |  |  |  |  |  |  |
| 124 | 16 | [Download](124/dataset.zip) |  |  |  |  |  |  |  |  |
| 125 | 30 | [Download](125/dataset.zip) |  |  |  |  |  |  |  |  |
| 126 | 68 | [Download](126/dataset.zip) |  |  |  |  |  |  |  |  |
| 127 | 75 | [Download](127/dataset.zip) |  |  |  |  |  |  |  |  |
| 128 | 107 | [Download](128/dataset.zip) |  |  |  |  |  |  |  |  |
| 129 | 27 | [Download](129/dataset.zip) |  |  |  |  |  |  |  |  |
| 130 | 39 | [Download](130/dataset.zip) |  |  |  |  |  |  |  |  |
| 131 | 16 | [Download](131/dataset.zip) |  |  |  |  |  |  |  |  |
| 132 | 29 | [Download](132/dataset.zip) |  |  |  |  |  |  |  |  |
| 133 | 527 | [Download](133/dataset.zip) |  |  |  |  |  |  |  |  |
| 134 | 27 | [Download](134/dataset.zip) |  |  |  |  |  |  |  |  |
| 135 | 42 | [Download](135/dataset.zip) |  |  |  |  |  |  |  |  |
| 136 | 57 | [Download](136/dataset.zip) |  |  |  |  |  |  |  |  |
| 137 | 37 | [Download](137/dataset.zip) |  |  |  |  |  |  |  |  |
| 138 | 66 | [Download](138/dataset.zip) |  |  |  |  |  |  |  |  |
| 139 | 2383 | [Download](139/dataset.zip) |  |  |  |  |  |  |  |  |
| 140 | 192 | [Download](140/dataset.zip) |  |  |  |  |  |  |  |  |
| 141 | 826 | [Download](141/dataset.zip) |  |  |  |  |  |  |  |  |
| 142 | 104 | [Download](142/dataset.zip) |  |  |  |  |  |  |  |  |
| 143 | 108 | [Download](143/dataset.zip) |  |  |  |  |  |  |  |  |
| 144 | 67 | [Download](144/dataset.zip) |  |  |  |  |  |  |  |  |
| 145 | 111 | [Download](145/dataset.zip) |  |  |  |  |  |  |  |  |
| 146 | 140 | [Download](146/dataset.zip) |  |  |  |  |  |  |  |  |
| 147 | 59 | [Download](147/dataset.zip) |  |  |  |  |  |  |  |  |
| 148 | 20 | [Download](148/dataset.zip) |  |  |  |  |  |  |  |  |
| 149 | 33 | [Download](149/dataset.zip) |  |  |  |  |  |  |  |  |
| 150 | 38 | [Download](150/dataset.zip) |  |  |  |  |  |  |  |  |
| 151 | 15 | [Download](151/dataset.zip) |  |  |  |  |  |  |  |  |
| 152 | 231 | [Download](152/dataset.zip) |  |  |  |  |  |  |  |  |
| 153 | 20 | [Download](153/dataset.zip) |  |  |  |  |  |  |  |  |
| 154 | 16 | [Download](154/dataset.zip) |  |  |  |  |  |  |  |  |
| 155 | 23 | [Download](155/dataset.zip) |  |  |  |  |  |  |  |  |
| 156 | 16 | [Download](156/dataset.zip) |  |  |  |  |  |  |  |  |
| 157 | 52 | [Download](157/dataset.zip) |  |  |  |  |  |  |  |  |
| 158 | 83 | [Download](158/dataset.zip) |  |  |  |  |  |  |  |  |
| 159 | 41 | [Download](159/dataset.zip) |  |  |  |  |  |  |  |  |
| 160 | 36 | [Download](160/dataset.zip) |  |  |  |  |  |  |  |  |
| 161 | 12 | [Download](161/dataset.zip) |  |  |  |  |  |  |  |  |
| 162 | 108 | [Download](162/dataset.zip) |  |  |  |  |  |  |  |  |
| 163 | 39 | [Download](163/dataset.zip) |  |  |  |  |  |  |  |  |
| 164 | 76 | [Download](164/dataset.zip) |  |  |  |  |  |  |  |  |
| 165 | 19 | [Download](165/dataset.zip) |  |  |  |  |  |  |  |  |
| 166 | 17 | [Download](166/dataset.zip) |  |  |  |  |  |  |  |  |
| 167 | 79 | [Download](167/dataset.zip) |  |  |  |  |  |  |  |  |
| 168 | 115 | [Download](168/dataset.zip) |  |  |  |  |  |  |  |  |
| 169 | 51 | [Download](169/dataset.zip) |  |  |  |  |  |  |  |  |
| 170 | 27 | [Download](170/dataset.zip) |  |  |  |  |  |  |  |  |
| 171 | 30 | [Download](171/dataset.zip) |  |  |  |  |  |  |  |  |
| 172 | 58 | [Download](172/dataset.zip) |  |  |  |  |  |  |  |  |
| 173 | 40 | [Download](173/dataset.zip) |  |  |  |  |  |  |  |  |
| 174 | 28 | [Download](174/dataset.zip) |  |  |  |  |  |  |  |  |
| 175 | 35 | [Download](175/dataset.zip) |  |  |  |  |  |  |  |  |
| 176 | 37 | [Download](176/dataset.zip) |  |  |  |  |  |  |  |  |
| 177 | 24 | [Download](177/dataset.zip) |  |  |  |  |  |  |  |  |
| 178 | 17 | [Download](178/dataset.zip) |  |  |  |  |  |  |  |  |
| 179 | 39 | [Download](179/dataset.zip) |  |  |  |  |  |  |  |  |
| 180 | 16 | [Download](180/dataset.zip) |  |  |  |  |  |  |  |  |
| 181 | 70 | [Download](181/dataset.zip) |  |  |  |  |  |  |  |  |
| 182 | 38 | [Download](182/dataset.zip) |  |  |  |  |  |  |  |  |
| 183 | 427 | [Download](183/dataset.zip) |  |  |  |  |  |  |  |  |
| 184 | 80 | [Download](184/dataset.zip) |  |  |  |  |  |  |  |  |
| 185 | 27 | [Download](185/dataset.zip) |  |  |  |  |  |  |  |  |
| 186 | 68 | [Download](186/dataset.zip) |  |  |  |  |  |  |  |  |
| 187 | 23 | [Download](187/dataset.zip) |  |  |  |  |  |  |  |  |
| 188 | 29 | [Download](188/dataset.zip) |  |  |  |  |  |  |  |  |
| 189 | 13 | [Download](189/dataset.zip) |  |  |  |  |  |  |  |  |
| 190 | 13 | [Download](190/dataset.zip) |  |  |  |  |  |  |  |  |
| 191 | 107 | [Download](191/dataset.zip) |  |  |  |  |  |  |  |  |
| 192 | 21 | [Download](192/dataset.zip) |  |  |  |  |  |  |  |  |
| 193 | 21 | [Download](193/dataset.zip) |  |  |  |  |  |  |  |  |
| 194 | 27 | [Download](194/dataset.zip) |  |  |  |  |  |  |  |  |
| 195 | 29 | [Download](195/dataset.zip) |  |  |  |  |  |  |  |  |
| 196 | 20 | [Download](196/dataset.zip) |  |  |  |  |  |  |  |  |
| 197 | 33 | [Download](197/dataset.zip) |  |  |  |  |  |  |  |  |
| 198 | 45 | [Download](198/dataset.zip) |  |  |  |  |  |  |  |  |
| 199 | 63 | [Download](199/dataset.zip) |  |  |  |  |  |  |  |  |
| 200 | 20 | [Download](200/dataset.zip) |  |  |  |  |  |  |  |  |
| 201 | 26 | [Download](201/dataset.zip) |  |  |  |  |  |  |  |  |
| 202 | 26 | [Download](202/dataset.zip) |  |  |  |  |  |  |  |  |
| 203 | 234 | [Download](203/dataset.zip) |  |  |  |  |  |  |  |  |
| 204 | 313 | [Download](204/dataset.zip) |  |  |  |  |  |  |  |  |
| 205 | 14 | [Download](205/dataset.zip) |  |  |  |  |  |  |  |  |
| 206 | 591 | [Download](206/dataset.zip) |  |  |  |  |  |  |  |  |
| 207 | 37 | [Download](207/dataset.zip) |  |  |  |  |  |  |  |  |
| 208 | 70 | [Download](208/dataset.zip) |  |  |  |  |  |  |  |  |
| 209 | 36 | [Download](209/dataset.zip) |  |  |  |  |  |  |  |  |
| 210 | 22 | [Download](210/dataset.zip) |  |  |  |  |  |  |  |  |
| 211 | 17 | [Download](211/dataset.zip) |  |  |  |  |  |  |  |  |
| 212 | 190 | [Download](212/dataset.zip) |  |  |  |  |  |  |  |  |
| 213 | 40 | [Download](213/dataset.zip) |  |  |  |  |  |  |  |  |
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| 267 | 6 | [Download](267/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 268 | 8 | [Download](268/dataset.zip) |  |  |  |  |  |  |  |  |
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AlexWolski/ShapeNet-SDF-Uniform | ---
annotations_creators:
- no-annotation
pretty_name: ShapeNet SDF Uniform
size_categories:
- 1K<n<10K
tags:
- Artificial Intelligence
- Machine Learning
- Computational Geometry
---
# ShapeNet SDF Sample Dataset
This is a subset of the [ShapeNet SDF Dataset](https://ls7-data.cs.tu-dortmund.de/shape_net/ShapeNet_SDF.tar.gz) provided by the [ShapeGan Project](https://github.com/marian42/shapegan).<br>
Only Uniform SDF samples are included.
### Contents
The dataset contains 8,320 data samples. Each data sample contains 200,000 uniformly distributed points and their corresponding SDF values.<br>
The dataset contains three shape classes:
* Airplanes (2156 samples)
* Chairs (4189 samples)
* Sofas (1975 samples) |
Seanxh/twitter_dataset_1713208062 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 151537
num_examples: 355
download_size: 56038
dataset_size: 151537
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Illuminatus-27/processed_bert_dataset | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: special_tokens_mask
sequence: int8
splits:
- name: test
num_bytes: 15688800
num_examples: 4358
- name: train
num_bytes: 132184800
num_examples: 36718
- name: validation
num_bytes: 13536000
num_examples: 3760
download_size: 6557867
dataset_size: 161409600
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
DGurgurov/nepali_sa | ---
license: mit
---
## Sentiment Analysis Data for the Nepali Language
**Dataset Description:**
This dataset contains a sentiment analysis dataset from Singh et al. (2020).
**Data Structure:**
The data was used for the project on [injecting external commonsense knowledge into multilingual Large Language Models](https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs).
**Citation:**
```bibtex
@INPROCEEDINGS{9381292,
author={Singh, Oyesh Mann and Timilsina, Sandesh and Bal, Bal Krishna and Joshi, Anupam},
booktitle={2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
title={Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts},
year={2020},
volume={},
number={},
pages={301-308},
doi={10.1109/ASONAM49781.2020.9381292}
}
``` |
Glac1er/June | ---
license: unknown
---
|
louisbrulenaudet/code-aviation-civile | ---
license: apache-2.0
language:
- fr
multilinguality:
- monolingual
tags:
- finetuning
- legal
- french law
- droit français
- Code de l'aviation civile
source_datasets:
- original
pretty_name: Code de l'aviation civile
task_categories:
- text-generation
- table-question-answering
- summarization
- text-retrieval
- question-answering
- text-classification
size_categories:
- 1K<n<10K
---
# Code de l'aviation civile, non-instruct (2024-04-15)
This project focuses on fine-tuning pre-trained language models to create efficient and accurate models for legal practice.
Fine-tuning is the process of adapting a pre-trained model to perform specific tasks or cater to particular domains. It involves adjusting the model's parameters through a further round of training on task-specific or domain-specific data. While conventional fine-tuning strategies involve supervised learning with labeled data, instruction-based fine-tuning introduces a more structured and interpretable approach.
Instruction-based fine-tuning leverages the power of human-provided instructions to guide the model's behavior. These instructions can be in the form of text prompts, prompts with explicit task descriptions, or a combination of both. This approach allows for a more controlled and context-aware interaction with the LLM, making it adaptable to a multitude of specialized tasks.
Instruction-based fine-tuning significantly enhances the performance of LLMs in the following ways:
- Task-Specific Adaptation: LLMs, when fine-tuned with specific instructions, exhibit remarkable adaptability to diverse tasks. They can switch seamlessly between translation, summarization, and question-answering, guided by the provided instructions.
- Reduced Ambiguity: Traditional LLMs might generate ambiguous or contextually inappropriate responses. Instruction-based fine-tuning allows for a clearer and more context-aware generation, reducing the likelihood of nonsensical outputs.
- Efficient Knowledge Transfer: Instructions can encapsulate domain-specific knowledge, enabling LLMs to benefit from expert guidance. This knowledge transfer is particularly valuable in fields like tax practice, law, medicine, and more.
- Interpretability: Instruction-based fine-tuning also makes LLM behavior more interpretable. Since the instructions are human-readable, it becomes easier to understand and control model outputs.
- Adaptive Behavior: LLMs, post instruction-based fine-tuning, exhibit adaptive behavior that is responsive to both explicit task descriptions and implicit cues within the provided text.
## Concurrent reading of the LegalKit
To use all the legal data published on LegalKit, you can use this code snippet:
```python
# -*- coding: utf-8 -*-
import concurrent.futures
import os
import datasets
from tqdm.notebook import tqdm
def dataset_loader(
name:str,
streaming:bool=True
) -> datasets.Dataset:
"""
Helper function to load a single dataset in parallel.
Parameters
----------
name : str
Name of the dataset to be loaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
dataset : datasets.Dataset
Loaded dataset object.
Raises
------
Exception
If an error occurs during dataset loading.
"""
try:
return datasets.load_dataset(
name,
split="train",
streaming=streaming
)
except Exception as exc:
logging.error(f"Error loading dataset {name}: {exc}")
return None
def load_datasets(
req:list,
streaming:bool=True
) -> list:
"""
Downloads datasets specified in a list and creates a list of loaded datasets.
Parameters
----------
req : list
A list containing the names of datasets to be downloaded.
streaming : bool, optional
Determines if datasets are streamed. Default is True.
Returns
-------
datasets_list : list
A list containing loaded datasets as per the requested names provided in 'req'.
Raises
------
Exception
If an error occurs during dataset loading or processing.
Examples
--------
>>> datasets = load_datasets(["dataset1", "dataset2"], streaming=False)
"""
datasets_list = []
with concurrent.futures.ThreadPoolExecutor() as executor:
future_to_dataset = {executor.submit(dataset_loader, name): name for name in req}
for future in tqdm(concurrent.futures.as_completed(future_to_dataset), total=len(req)):
name = future_to_dataset[future]
try:
dataset = future.result()
if dataset:
datasets_list.append(dataset)
except Exception as exc:
logging.error(f"Error processing dataset {name}: {exc}")
return datasets_list
req = [
"louisbrulenaudet/code-artisanat",
"louisbrulenaudet/code-action-sociale-familles",
# ...
]
datasets_list = load_datasets(
req=req,
streaming=True
)
dataset = datasets.concatenate_datasets(
datasets_list
)
```
## Dataset generation
This JSON file is a list of dictionaries, each dictionary contains the following fields:
- `instruction`: `string`, presenting the instruction linked to the element.
- `input`: `string`, signifying the input details for the element.
- `output`: `string`, indicating the output information for the element.
- `start`: `string`, the date of entry into force of the article.
- `expiration`: `string`, the date of expiration of the article.
- `num`: `string`, the id of the article.
We used the following list of instructions for generating the dataset:
```python
instructions = [
"Compose l'intégralité de l'article sous forme écrite.",
"Écris la totalité du contenu de l'article.",
"Formule la totalité du texte présent dans l'article.",
"Produis l'intégralité de l'article en écriture.",
"Développe l'article dans son ensemble par écrit.",
"Génère l'ensemble du texte contenu dans l'article.",
"Formule le contenu intégral de l'article en entier.",
"Rédige la totalité du texte de l'article en entier.",
"Compose l'intégralité du contenu textuel de l'article.",
"Rédige l'ensemble du texte qui constitue l'article.",
"Formule l'article entier dans son contenu écrit.",
"Composez l'intégralité de l'article sous forme écrite.",
"Écrivez la totalité du contenu de l'article.",
"Formulez la totalité du texte présent dans l'article.",
"Développez l'article dans son ensemble par écrit.",
"Générez l'ensemble du texte contenu dans l'article.",
"Formulez le contenu intégral de l'article en entier.",
"Rédigez la totalité du texte de l'article en entier.",
"Composez l'intégralité du contenu textuel de l'article.",
"Écrivez l'article dans son intégralité en termes de texte.",
"Rédigez l'ensemble du texte qui constitue l'article.",
"Formulez l'article entier dans son contenu écrit.",
"Composer l'intégralité de l'article sous forme écrite.",
"Écrire la totalité du contenu de l'article.",
"Formuler la totalité du texte présent dans l'article.",
"Produire l'intégralité de l'article en écriture.",
"Développer l'article dans son ensemble par écrit.",
"Générer l'ensemble du texte contenu dans l'article.",
"Formuler le contenu intégral de l'article en entier.",
"Rédiger la totalité du texte de l'article en entier.",
"Composer l'intégralité du contenu textuel de l'article.",
"Rédiger l'ensemble du texte qui constitue l'article.",
"Formuler l'article entier dans son contenu écrit.",
"Quelles sont les dispositions de l'article ?",
"Quelles dispositions sont incluses dans l'article ?",
"Quelles sont les dispositions énoncées dans l'article ?",
"Quel est le texte intégral de l'article ?",
"Quelle est la lettre de l'article ?"
]
```
## Feedback
If you have any feedback, please reach out at [louisbrulenaudet@icloud.com](mailto:louisbrulenaudet@icloud.com). |
a686d380/h-corpus-2023 | ---
viewer: false
language:
- zh
---
经过清洗和去重过的H小说
共205,028篇文章，解压后17.0 GB
仅用于科学研究！ |
metaboulie/Tidied-PII-Detection-Kaggle-7k | ---
license: apache-2.0
task_categories:
- text-generation
- token-classification
language:
- en
size_categories:
- 1K<n<10K
---
# Dataset Card for Dataset Name
This dataset is a modified version of the training set of the Kaggle Competition PII Data Detection.
## Dataset Details
The PII data for each text is extracted into 'pii_data' field, and thinking tools are extracted into 'thinking_tools' field.
I create this dataset to instruct tuning LLMs and generate more data to training Token Classifiers.
|
lengoctuong/codeparrot-ds-mapped_ids | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 8618263476
num_examples: 16702061
- name: valid
num_bytes: 48072624
num_examples: 93164
download_size: 3804670316
dataset_size: 8666336100
---
# Dataset Card for "codeparrot-ds-mapped_ids"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nampdn-ai/tiny-webtext | ---
license: mit
viewer: true
task_categories:
- text-generation
language:
- en
pretty_name: Tiny WebText
size_categories:
- 1M<n<10M
source_datasets:
- tiiuae/falcon-refinedweb
---
# Tiny WebText
The Tiny WebText dataset is designed to help models learn about perception on web text while neutralizing the bias of the source text using critical thinking methods. By providing a rich and diverse set of texts, I aim to improve the ability of models to understand and analyze information in a more objective and unbiased manner.
This dataset can be used to train and evaluate natural language processing and machine learning models, with the goal of improving their perception and critical thinking skills. It is a valuable resource for researchers and developers, especially those working in the fields of machine learning and data engineering.
The dataset is augmented using subset of [Falcon-RefinedWeb](https://arxiv.org/abs/2306.01116), which provides additional augmented text using [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct) model to improve the quality and diversity of the texts.
I welcome any feedback or contributions. Thank you for your interest in my work! |
Nexdata/Wake-up_Words_Speech_Data_by_Microphone | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for Nexdata/Wake-up_Words_Speech_Data_by_Microphone
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.nexdata.ai/datasets/1076?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
More than 1,000 recorders read the specified wake-up words, covering slow, normal, and fast three speeds. Audios are recorded in the professional recording studio using the microphone.
For more details, please refer to the link: https://www.nexdata.ai/datasets/1076?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
Chinese Mandarin
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing
### Citation Information
[More Information Needed]
### Contributions
|
perlthoughts/big-brain-4k | ---
license: apache-2.0
---
code
```python
# used when training samples do not include a system prompt.
DEFAULT_SYSTEM_PROMPT = "Below is an instruction that describes a task. Write a response that appropriately completes the request."
# if any of these words are in the system or prompt, the item will be skipped.
BAD_WORDS = [
"english", "translate", "russian", "chinese", "japanese", "spanish", "persian", "french", "german", "italian", "korean",
"arabic", "hindi", "portuguese", "turkish", "vietnamese", "indonesian", "thai", "polish", "dutch", "greek", "czech",
"romanian", "swedish", "danish", "finnish", "hungarian", "norwegian", "slovak", "slovenian", "lithuanian", "latvian",
"estonian", "bulgarian", "serbian", "ukrainian", "belarusian", "croatian", "bosnian", "macedonian", "albanian", "icelandic",
"irish", "welsh", "scottish", "latin", "esperanto", "hebrew", "yiddish", "afrikaans", "swahili", "zulu", "xhosa", "sotho",
"sesotho", "somali", "hausa", "igbo", "yoruba", "malay", "tagalog", "hawaiian", "maori", "mongolian", "tamil", "telugu",
"kannada", "gujarati", "marathi", "punjabi", "nepali", "sinhala", "khmer", "lao", "burmese", "tibetan", "georgian",
"azerbaijani", "kurdish", "armenian", "kazakh", "uzbek", "tajik", "kirghiz", "turkmen", "tatar", "bashkir", "chechen",
"chuvash", "ossetian", "moldavian", "moldovan", "language model", " AI ", "openai", "gpt", "gpt-2", "gpt-3", "gpt2", "gpt3", "gpt4",
"gpt-4", "illegal", "harmful", "cannot provide", "yourself or others", "harm to yourself", "cannot suggest", "morals", "ethical",
"cannot answer", "can't answer", "don't know", "no answer", "no response", "i can't", "not enough information", "insufficient",
"it is not possible", "not answerable", "unfortunately", "can't answer", "am not sure", "davinci-0", "ada-0", "babbage-0", "curie-0",
]
# if any of these words are not in the system or prompt, the item will be skipped.
GOOD_WORDS = [
"solve", "calculate", "math", "equation", "formula", "logic", "algebra", "geometry", "riddle", "puzzle", "proof", "theorem",
"problem", "theory", "finance", "economics", "chemistry", "biology", "physics", "science", "history", "geography",
"philosophy", "psychology", "sociology", "computer", "programming", "technology", "engineering", "medicine", "health",
"code", "program", "health", "medical", "doctor", "nurse", "hospital", "disease", "bacteria", "symptom", "cancer",
"diagnosis", "treatment", "procedure", "medicine", "infection", "survival", "therapy", "psychological", "psychiatry",
"summarize", "summarized", "find the", "result", "title", "author", "abstract", "conclusion", "research", "upon a time",
"to whom it may", "subject:", "title:", "from:", "date:", "invoice", "recipe", "life pro tip", "tweet", "a story", "a poem",
"short story", "article", "essay",
]
TOTAL_ITEMS = 100000
# all datasets used and the percentage/ratio of each from the total.
DATASETS = {
"meta-math/MetaMathQA": {
"ratio": 0.3, "set": "train",
"system": DEFAULT_SYSTEM_PROMPT, "prompt": "query", "output": "response",
},
"allenai/ultrafeedback_binarized_cleaned": {
"ratio": 0.3, "set": "train_sft",
"system": DEFAULT_SYSTEM_PROMPT, "prompt": "prompt", "output": "get_assistant(chosen)",
},
"Open-Orca/OpenOrca": {
"ratio": 0.4, "set": "train",
"system": "system_prompt", "prompt": "question", "output": "response",
},
}
MAX_CHAR_LENGTH = 4096
``` |
ibranze/araproje_hellaswag_en_conf_llama_worstscore_reversed | ---
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
splits:
- name: validation
num_bytes: 149738.0
num_examples: 250
download_size: 81104
dataset_size: 149738.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_hellaswag_en_conf_llama_worstscore_reversed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B-DPO | ---
pretty_name: Evaluation run of cloudyu/Yi-34Bx2-MoE-60B-DPO
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [cloudyu/Yi-34Bx2-MoE-60B-DPO](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B-DPO)\
\ 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_cloudyu__Yi-34Bx2-MoE-60B-DPO\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-23T09:26:46.662482](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B-DPO/blob/main/results_2024-01-23T09-26-46.662482.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\": 0.7706672409381974,\n\
\ \"acc_stderr\": 0.027896926086644222,\n \"acc_norm\": 0.7738601958843111,\n\
\ \"acc_norm_stderr\": 0.028438404294113005,\n \"mc1\": 0.4883720930232558,\n\
\ \"mc1_stderr\": 0.017498767175740088,\n \"mc2\": 0.6624336903360023,\n\
\ \"mc2_stderr\": 0.0145357390643212\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6749146757679181,\n \"acc_stderr\": 0.013688147309729124,\n\
\ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266129\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6546504680342561,\n\
\ \"acc_stderr\": 0.004745103543901293,\n \"acc_norm\": 0.8510256920932086,\n\
\ \"acc_norm_stderr\": 0.0035533545281323554\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.7333333333333333,\n\
\ \"acc_stderr\": 0.038201699145179055,\n \"acc_norm\": 0.7333333333333333,\n\
\ \"acc_norm_stderr\": 0.038201699145179055\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.875,\n \"acc_stderr\": 0.026913523521537846,\n \
\ \"acc_norm\": 0.875,\n \"acc_norm_stderr\": 0.026913523521537846\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\": 0.02461829819586651,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.02461829819586651\n },\n\
\ \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.875,\n \
\ \"acc_stderr\": 0.02765610492929436,\n \"acc_norm\": 0.875,\n \
\ \"acc_norm_stderr\": 0.02765610492929436\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.67,\n \"acc_stderr\": 0.04725815626252606,\n \"acc_norm\": 0.67,\n\
\ \"acc_norm_stderr\": 0.04725815626252606\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.7630057803468208,\n\
\ \"acc_stderr\": 0.03242414757483098,\n \"acc_norm\": 0.7630057803468208,\n\
\ \"acc_norm_stderr\": 0.03242414757483098\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.6274509803921569,\n \"acc_stderr\": 0.048108401480826346,\n\
\ \"acc_norm\": 0.6274509803921569,\n \"acc_norm_stderr\": 0.048108401480826346\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.81,\n \"acc_stderr\": 0.039427724440366234,\n \"acc_norm\": 0.81,\n\
\ \"acc_norm_stderr\": 0.039427724440366234\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.7957446808510639,\n \"acc_stderr\": 0.026355158413349414,\n\
\ \"acc_norm\": 0.7957446808510639,\n \"acc_norm_stderr\": 0.026355158413349414\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.6140350877192983,\n\
\ \"acc_stderr\": 0.04579639422070434,\n \"acc_norm\": 0.6140350877192983,\n\
\ \"acc_norm_stderr\": 0.04579639422070434\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.7586206896551724,\n \"acc_stderr\": 0.03565998174135302,\n\
\ \"acc_norm\": 0.7586206896551724,\n \"acc_norm_stderr\": 0.03565998174135302\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.7275132275132276,\n \"acc_stderr\": 0.022930973071633363,\n \"\
acc_norm\": 0.7275132275132276,\n \"acc_norm_stderr\": 0.022930973071633363\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.5714285714285714,\n\
\ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.5714285714285714,\n\
\ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.56,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.56,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.9032258064516129,\n\
\ \"acc_stderr\": 0.016818943416345197,\n \"acc_norm\": 0.9032258064516129,\n\
\ \"acc_norm_stderr\": 0.016818943416345197\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.625615763546798,\n \"acc_stderr\": 0.03405155380561952,\n\
\ \"acc_norm\": 0.625615763546798,\n \"acc_norm_stderr\": 0.03405155380561952\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.82,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\"\
: 0.82,\n \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8666666666666667,\n \"acc_stderr\": 0.026544435312706463,\n\
\ \"acc_norm\": 0.8666666666666667,\n \"acc_norm_stderr\": 0.026544435312706463\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.9292929292929293,\n \"acc_stderr\": 0.018263105420199505,\n \"\
acc_norm\": 0.9292929292929293,\n \"acc_norm_stderr\": 0.018263105420199505\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9689119170984456,\n \"acc_stderr\": 0.012525310625527033,\n\
\ \"acc_norm\": 0.9689119170984456,\n \"acc_norm_stderr\": 0.012525310625527033\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.8205128205128205,\n \"acc_stderr\": 0.0194573907876818,\n \
\ \"acc_norm\": 0.8205128205128205,\n \"acc_norm_stderr\": 0.0194573907876818\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.44074074074074077,\n \"acc_stderr\": 0.030270671157284067,\n \
\ \"acc_norm\": 0.44074074074074077,\n \"acc_norm_stderr\": 0.030270671157284067\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.8529411764705882,\n \"acc_stderr\": 0.023005459446673947,\n\
\ \"acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.023005459446673947\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.5099337748344371,\n \"acc_stderr\": 0.04081677107248437,\n \"\
acc_norm\": 0.5099337748344371,\n \"acc_norm_stderr\": 0.04081677107248437\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.9174311926605505,\n \"acc_stderr\": 0.011800361363016576,\n \"\
acc_norm\": 0.9174311926605505,\n \"acc_norm_stderr\": 0.011800361363016576\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.6712962962962963,\n \"acc_stderr\": 0.032036140846700596,\n \"\
acc_norm\": 0.6712962962962963,\n \"acc_norm_stderr\": 0.032036140846700596\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.9215686274509803,\n \"acc_stderr\": 0.018869514646658935,\n \"\
acc_norm\": 0.9215686274509803,\n \"acc_norm_stderr\": 0.018869514646658935\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8987341772151899,\n \"acc_stderr\": 0.019637720526065522,\n \
\ \"acc_norm\": 0.8987341772151899,\n \"acc_norm_stderr\": 0.019637720526065522\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7937219730941704,\n\
\ \"acc_stderr\": 0.02715715047956382,\n \"acc_norm\": 0.7937219730941704,\n\
\ \"acc_norm_stderr\": 0.02715715047956382\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.9083969465648855,\n \"acc_stderr\": 0.025300035578642962,\n\
\ \"acc_norm\": 0.9083969465648855,\n \"acc_norm_stderr\": 0.025300035578642962\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\": 0.8773006134969326,\n \"acc_stderr\": 0.025777328426978927,\n\
\ \"acc_norm\": 0.8773006134969326,\n \"acc_norm_stderr\": 0.025777328426978927\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.6339285714285714,\n\
\ \"acc_stderr\": 0.04572372358737431,\n \"acc_norm\": 0.6339285714285714,\n\
\ \"acc_norm_stderr\": 0.04572372358737431\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.912621359223301,\n \"acc_stderr\": 0.027960689125970654,\n\
\ \"acc_norm\": 0.912621359223301,\n \"acc_norm_stderr\": 0.027960689125970654\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9444444444444444,\n\
\ \"acc_stderr\": 0.015006312806446912,\n \"acc_norm\": 0.9444444444444444,\n\
\ \"acc_norm_stderr\": 0.015006312806446912\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776348,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776348\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.9080459770114943,\n\
\ \"acc_stderr\": 0.010333225570778518,\n \"acc_norm\": 0.9080459770114943,\n\
\ \"acc_norm_stderr\": 0.010333225570778518\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.8294797687861272,\n \"acc_stderr\": 0.020247961569303728,\n\
\ \"acc_norm\": 0.8294797687861272,\n \"acc_norm_stderr\": 0.020247961569303728\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.8111731843575419,\n\
\ \"acc_stderr\": 0.013089403869745457,\n \"acc_norm\": 0.8111731843575419,\n\
\ \"acc_norm_stderr\": 0.013089403869745457\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.8594771241830066,\n \"acc_stderr\": 0.019899435463539946,\n\
\ \"acc_norm\": 0.8594771241830066,\n \"acc_norm_stderr\": 0.019899435463539946\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.8167202572347267,\n\
\ \"acc_stderr\": 0.02197419884826582,\n \"acc_norm\": 0.8167202572347267,\n\
\ \"acc_norm_stderr\": 0.02197419884826582\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.8765432098765432,\n \"acc_stderr\": 0.01830386880689179,\n\
\ \"acc_norm\": 0.8765432098765432,\n \"acc_norm_stderr\": 0.01830386880689179\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.6382978723404256,\n \"acc_stderr\": 0.028663820147199485,\n \
\ \"acc_norm\": 0.6382978723404256,\n \"acc_norm_stderr\": 0.028663820147199485\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.6023468057366362,\n\
\ \"acc_stderr\": 0.012499840347460642,\n \"acc_norm\": 0.6023468057366362,\n\
\ \"acc_norm_stderr\": 0.012499840347460642\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.8272058823529411,\n \"acc_stderr\": 0.022966067585581774,\n\
\ \"acc_norm\": 0.8272058823529411,\n \"acc_norm_stderr\": 0.022966067585581774\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.8251633986928104,\n \"acc_stderr\": 0.015366167064780641,\n \
\ \"acc_norm\": 0.8251633986928104,\n \"acc_norm_stderr\": 0.015366167064780641\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7181818181818181,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.7181818181818181,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\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\": 0.900497512437811,\n\
\ \"acc_stderr\": 0.021166216304659393,\n \"acc_norm\": 0.900497512437811,\n\
\ \"acc_norm_stderr\": 0.021166216304659393\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.92,\n \"acc_stderr\": 0.0272659924344291,\n \
\ \"acc_norm\": 0.92,\n \"acc_norm_stderr\": 0.0272659924344291\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8830409356725146,\n \"acc_stderr\": 0.02464806896136616,\n\
\ \"acc_norm\": 0.8830409356725146,\n \"acc_norm_stderr\": 0.02464806896136616\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4883720930232558,\n\
\ \"mc1_stderr\": 0.017498767175740088,\n \"mc2\": 0.6624336903360023,\n\
\ \"mc2_stderr\": 0.0145357390643212\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8476716653512234,\n \"acc_stderr\": 0.010099208246065588\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.7391963608794542,\n \
\ \"acc_stderr\": 0.01209425241733274\n }\n}\n```"
repo_url: https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B-DPO
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|arc:challenge|25_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|gsm8k|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hellaswag|10_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T09-26-46.662482.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-23T09-26-46.662482.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- '**/details_harness|winogrande|5_2024-01-23T09-26-46.662482.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-23T09-26-46.662482.parquet'
- config_name: results
data_files:
- split: 2024_01_23T09_26_46.662482
path:
- results_2024-01-23T09-26-46.662482.parquet
- split: latest
path:
- results_2024-01-23T09-26-46.662482.parquet
---
# Dataset Card for Evaluation run of cloudyu/Yi-34Bx2-MoE-60B-DPO
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [cloudyu/Yi-34Bx2-MoE-60B-DPO](https://huggingface.co/cloudyu/Yi-34Bx2-MoE-60B-DPO) 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_cloudyu__Yi-34Bx2-MoE-60B-DPO",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-23T09:26:46.662482](https://huggingface.co/datasets/open-llm-leaderboard/details_cloudyu__Yi-34Bx2-MoE-60B-DPO/blob/main/results_2024-01-23T09-26-46.662482.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": 0.7706672409381974,
"acc_stderr": 0.027896926086644222,
"acc_norm": 0.7738601958843111,
"acc_norm_stderr": 0.028438404294113005,
"mc1": 0.4883720930232558,
"mc1_stderr": 0.017498767175740088,
"mc2": 0.6624336903360023,
"mc2_stderr": 0.0145357390643212
},
"harness|arc:challenge|25": {
"acc": 0.6749146757679181,
"acc_stderr": 0.013688147309729124,
"acc_norm": 0.712457337883959,
"acc_norm_stderr": 0.013226719056266129
},
"harness|hellaswag|10": {
"acc": 0.6546504680342561,
"acc_stderr": 0.004745103543901293,
"acc_norm": 0.8510256920932086,
"acc_norm_stderr": 0.0035533545281323554
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.7333333333333333,
"acc_stderr": 0.038201699145179055,
"acc_norm": 0.7333333333333333,
"acc_norm_stderr": 0.038201699145179055
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.875,
"acc_stderr": 0.026913523521537846,
"acc_norm": 0.875,
"acc_norm_stderr": 0.026913523521537846
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.8,
"acc_stderr": 0.02461829819586651,
"acc_norm": 0.8,
"acc_norm_stderr": 0.02461829819586651
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.875,
"acc_stderr": 0.02765610492929436,
"acc_norm": 0.875,
"acc_norm_stderr": 0.02765610492929436
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.44,
"acc_stderr": 0.0498887651569859,
"acc_norm": 0.44,
"acc_norm_stderr": 0.0498887651569859
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.7630057803468208,
"acc_stderr": 0.03242414757483098,
"acc_norm": 0.7630057803468208,
"acc_norm_stderr": 0.03242414757483098
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.6274509803921569,
"acc_stderr": 0.048108401480826346,
"acc_norm": 0.6274509803921569,
"acc_norm_stderr": 0.048108401480826346
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.81,
"acc_stderr": 0.039427724440366234,
"acc_norm": 0.81,
"acc_norm_stderr": 0.039427724440366234
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.7957446808510639,
"acc_stderr": 0.026355158413349414,
"acc_norm": 0.7957446808510639,
"acc_norm_stderr": 0.026355158413349414
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.6140350877192983,
"acc_stderr": 0.04579639422070434,
"acc_norm": 0.6140350877192983,
"acc_norm_stderr": 0.04579639422070434
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.7586206896551724,
"acc_stderr": 0.03565998174135302,
"acc_norm": 0.7586206896551724,
"acc_norm_stderr": 0.03565998174135302
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.7275132275132276,
"acc_stderr": 0.022930973071633363,
"acc_norm": 0.7275132275132276,
"acc_norm_stderr": 0.022930973071633363
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.5714285714285714,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.5714285714285714,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.9032258064516129,
"acc_stderr": 0.016818943416345197,
"acc_norm": 0.9032258064516129,
"acc_norm_stderr": 0.016818943416345197
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.625615763546798,
"acc_stderr": 0.03405155380561952,
"acc_norm": 0.625615763546798,
"acc_norm_stderr": 0.03405155380561952
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.82,
"acc_stderr": 0.03861229196653694,
"acc_norm": 0.82,
"acc_norm_stderr": 0.03861229196653694
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8666666666666667,
"acc_stderr": 0.026544435312706463,
"acc_norm": 0.8666666666666667,
"acc_norm_stderr": 0.026544435312706463
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.9292929292929293,
"acc_stderr": 0.018263105420199505,
"acc_norm": 0.9292929292929293,
"acc_norm_stderr": 0.018263105420199505
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9689119170984456,
"acc_stderr": 0.012525310625527033,
"acc_norm": 0.9689119170984456,
"acc_norm_stderr": 0.012525310625527033
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.8205128205128205,
"acc_stderr": 0.0194573907876818,
"acc_norm": 0.8205128205128205,
"acc_norm_stderr": 0.0194573907876818
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.44074074074074077,
"acc_stderr": 0.030270671157284067,
"acc_norm": 0.44074074074074077,
"acc_norm_stderr": 0.030270671157284067
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.8529411764705882,
"acc_stderr": 0.023005459446673947,
"acc_norm": 0.8529411764705882,
"acc_norm_stderr": 0.023005459446673947
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.5099337748344371,
"acc_stderr": 0.04081677107248437,
"acc_norm": 0.5099337748344371,
"acc_norm_stderr": 0.04081677107248437
},
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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. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### 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
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[More Information Needed]
### Recommendations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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DavidLanz/BTC_USDT_ohlcv_202403 | ---
license: apache-2.0
---
|
liuyanchen1015/MULTI_VALUE_stsb_regularized_reflexives | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: score
dtype: float64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 738
num_examples: 3
- name: test
num_bytes: 661
num_examples: 4
- name: train
num_bytes: 4151
num_examples: 23
download_size: 12856
dataset_size: 5550
---
# Dataset Card for "MULTI_VALUE_stsb_regularized_reflexives"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
saillab/taco-datasets | ---
language:
- en
- ne
- sn
- mai
- fa
- hi
- af
- sq
- am
- ar
- hy
- as
- ay
- az
- bm
- eu
- be
- bn
- bh
- bs
- bg
- ca
- ceb
- ny
- zh
- co
- hr
- cs
- da
- dv
- dog
- nl
- eo
- et
- ee
- tl
- fi
- fr
- fy
- gl
- ka
- de
- el
- gn
- gu
- ht
- ha
- haw
- he
- hmn
- hu
- is
- ig
- ilo
- id
- ga
- it
- ja
- jv
- kn
- kk
- km
- rw
- kok
- ko
- kri
- ku
- ky
- lo
- la
- lv
- ln
- lt
- lg
- lb
- mk
- ml
- mt
- mi
- mr
- mni
- ms
- mg
- mt
- my
- 'no'
- or
- om
- ps
- pl
- pt
- pa
- ro
- ru
- sm
- gd
- sr
- st
- tn
- sd
- si
- sk
- sl
- so
- es
- su
- sw
- sv
- tg
- ta
- tt
- te
- th
- ti
- to
- tr
- tk
- tw
- uk
- ur
- ug
- uz
- vi
- cy
- xh
- yi
- yo
- zu
pretty_name: t
size_categories:
- 100K<n<1M
---
This repo consists of the datasets used for the TaCo paper. There are four datasets:
* Multilingual Alpaca-52K GPT-4 dataset
* Multilingual Dolly-15K GPT-4 dataset
* TaCo dataset
* Multilingual Vicuna Benchmark dataset
We translated the first three datasets using Google Cloud Translation.
The TaCo dataset is created by using the TaCo approach as described in our paper, combining the Alpaca-52K and Dolly-15K datasets.
If you would like to create the TaCo dataset for a specific language, you can simply follow the method as mentioned in the paper and use the above translated datasets.
Link to the Paper: [Arxiv](https://arxiv.org/abs/2311.10797)
```
{
"instruction": "instruction in xx",
"input": "input in xx",
"output": "Instruction in English: instruction in en ,
Response in English: response in en ,
Response in xx: response in xx "
}
```
**Model Weights**
We have released all of our model adapters in the HuggingFace.
* [Taco Nepali-33B 🌮](https://huggingface.co/saillab/taco-nepali-33b)
* [Taco Sanskrit-33B 🌮](https://huggingface.co/saillab/taco-sanskrit-33b)
* [Taco Maithili-33B 🌮](https://huggingface.co/saillab/taco-maithili-33b)
* [Taco Persian-33B 🌮](https://huggingface.co/saillab/taco-persian-33b)
**Citation**
```
@article{upadhayay2023taco,
title={TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes},
author={Upadhayay, Bibek and Behzadan, Vahid},
journal={arXiv preprint arXiv:2311.10797},
year={2023}
}
```
**Copyright and Intended Use**
This dataset has been released under CC BY-NC, intended for academic and research purposes only. Please review the licenses and terms and conditions of Alpaca-52K, Dolly-15K, and Google Cloud Translation before using this dataset for any purpose other than research. |
mdacampora/tax-convos-sample | ---
dataset_info:
features:
- name: customer
struct:
- name: employee_id
dtype: string
- name: name
dtype: string
- name: social_security_number
dtype: string
- name: problem
dtype: string
- name: transcript
sequence: string
splits:
- name: train
num_bytes: 2443
num_examples: 4
download_size: 8150
dataset_size: 2443
---
# Dataset Card for "tax-convos-sample"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/squad_qa_wrong_title_v5_full_recite_ans_sent_first_permute_rerun | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: correct_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 8175993.493769262
num_examples: 4778
- name: validation
num_bytes: 422069
num_examples: 300
download_size: 1635153
dataset_size: 8598062.493769262
---
# Dataset Card for "squad_qa_wrong_title_v5_full_recite_ans_sent_first_permute_rerun"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
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