datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
zongxiao/github-issues-colab | ---
dataset_info:
features:
- name: url
dtype: string
- name: repository_url
dtype: string
- name: labels_url
dtype: string
- name: comments_url
dtype: string
- name: events_url
dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: user
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: labels
list:
- name: id
dtype: int64
- name: node_id
dtype: string
- name: url
dtype: string
- name: name
dtype: string
- name: color
dtype: string
- name: default
dtype: bool
- name: description
dtype: string
- name: state
dtype: string
- name: locked
dtype: bool
- name: assignee
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: assignees
list:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: milestone
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: labels_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
- name: description
dtype: string
- name: creator
struct:
- name: login
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: avatar_url
dtype: string
- name: gravatar_id
dtype: string
- name: url
dtype: string
- name: html_url
dtype: string
- name: followers_url
dtype: string
- name: following_url
dtype: string
- name: gists_url
dtype: string
- name: starred_url
dtype: string
- name: subscriptions_url
dtype: string
- name: organizations_url
dtype: string
- name: repos_url
dtype: string
- name: events_url
dtype: string
- name: received_events_url
dtype: string
- name: type
dtype: string
- name: site_admin
dtype: bool
- name: open_issues
dtype: int64
- name: closed_issues
dtype: int64
- name: state
dtype: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: due_on
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: comments
sequence: string
- name: created_at
dtype: timestamp[s]
- name: updated_at
dtype: timestamp[s]
- name: closed_at
dtype: timestamp[s]
- name: author_association
dtype: string
- name: active_lock_reason
dtype: 'null'
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: body
dtype: string
- name: reactions
struct:
- name: url
dtype: string
- name: total_count
dtype: int64
- name: '+1'
dtype: int64
- name: '-1'
dtype: int64
- name: laugh
dtype: int64
- name: hooray
dtype: int64
- name: confused
dtype: int64
- name: heart
dtype: int64
- name: rocket
dtype: int64
- name: eyes
dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 13478052
num_examples: 3624
download_size: 3952655
dataset_size: 13478052
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "github-issues-colab"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Anusha64/aeon4 | ---
license: mit
---
|
projecte-aina/catalanqa | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: catalanqa
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
## 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 Card for CatalanQA
## Dataset Description
- **Homepage:** https://github.com/projecte-aina
- **Point of Contact:** langtech@bsc.es
### Dataset Summary
This dataset can be used to build extractive-QA and Language Models. It is an aggregation and balancing of 2 previous datasets: [VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad) and [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad).
Splits have been balanced by kind of question, and unlike other datasets like [SQuAD](http://arxiv.org/abs/1606.05250), it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times.
This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.
### Supported Tasks and Leaderboards
Extractive-QA, Language Model.
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
```
{
"title": "Els 521 policies espanyols amb més mala nota a les oposicions seran enviats a Catalunya",
"paragraphs": [
{
"context": "El Ministeri d'Interior espanyol enviarà a Catalunya els 521 policies espanyols que han obtingut més mala nota a les oposicions. Segons que explica El País, hi havia mig miler de places vacants que s'havien de cobrir, però els agents amb més bones puntuacions han elegit destinacions diferents. En total van aprovar les oposicions 2.600 aspirants. D'aquests, en seran destinats al Principat 521 dels 560 amb més mala nota. Per l'altra banda, entre els 500 agents amb més bona nota, només 8 han triat Catalunya. Fonts de la policia espanyola que esmenta el diari ho atribueixen al procés d'independència, al Primer d'Octubre i a la 'situació social' que se'n deriva.",
"qas": [
{
"question": "Quants policies enviaran a Catalunya?",
"id": "0.5961700408283691",
"answers": [
{
"text": "521",
"answer_start": 57
}
]
}
]
}
]
},
```
### Data Fields
Follows [(Rajpurkar, Pranav et al., 2016)](http://arxiv.org/abs/1606.05250) for SQuAD v1 datasets:
- `id` (str): Unique ID assigned to the question.
- `title` (str): Title of the article.
- `context` (str): Article text.
- `question` (str): Question.
- `answers` (list): Answer to the question, containing:
- `text` (str): Span text answering to the question.
- `answer_start` Starting offset of the span text answering to the question.
### Data Splits
- train.json: 17135 question/answer pairs
- dev.json: 2157 question/answer pairs
- test.json: 2135 question/answer pairs
## Dataset Creation
### Curation Rationale
We created this corpus to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
- [VilaWeb](https://www.vilaweb.cat/) and [Catalan Wikipedia](https://ca.wikipedia.org).
#### Initial Data Collection and Normalization
This dataset is a balanced aggregation from [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad) and [VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad) datasets.
#### Who are the source language producers?
Volunteers from [Catalan Wikipedia](https://ca.wikipedia.org) and professional journalists from [VilaWeb](https://www.vilaweb.cat/).
### Annotations
#### Annotation process
We did an aggregation and balancing from [ViquiQuAD](https://huggingface.co/datasets/projecte-aina/viquiquad) and [VilaQuAD](https://huggingface.co/datasets/projecte-aina/vilaquad) datasets.
To annotate those datasets, we commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQuAD 1.0 [(Rajpurkar, Pranav et al., 2016)](http://arxiv.org/abs/1606.05250).
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
#### Who are the annotators?
Annotation was commissioned by a specialized company that hired a team of native language speakers.
### Personal and Sensitive Information
No personal or sensitive information is included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this corpus contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.
### Contributions
[N/A] |
CyberHarem/nelson_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of nelson/ネルソン (Kantai Collection)
This is the dataset of nelson/ネルソン (Kantai Collection), containing 275 images and their tags.
The core tags of this character are `blonde_hair, long_hair, blue_eyes, breasts, large_breasts, headgear`, 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 | 275 | 256.23 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nelson_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 275 | 173.86 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nelson_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 590 | 344.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nelson_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 275 | 238.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nelson_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 590 | 445.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/nelson_kantaicollection/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/nelson_kantaicollection',
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 | 9 |  |  |  |  |  | 1girl, fake_animal_ears, playboy_bunny, rabbit_ears, solo, wrist_cuffs, black_pantyhose, alternate_costume, black_leotard, bowtie, detached_collar, cleavage, black_footwear, covered_navel, cowboy_shot, fishnet_pantyhose, full_body, high_heels, looking_at_viewer, simple_background, standing |
| 1 | 9 |  |  |  |  |  | 1girl, cowboy_shot, long_sleeves, military_uniform, red_ascot, red_rose, solo, looking_at_viewer, pencil_skirt, white_background, simple_background, smile, one-hour_drawing_challenge, open_mouth, twitter_username |
| 2 | 9 |  |  |  |  |  | 1girl, long_sleeves, military_uniform, red_ascot, red_rose, smile, solo, looking_at_viewer, simple_background, upper_body, white_background, skirt |
| 3 | 6 |  |  |  |  |  | 1girl, cleavage, cropped_jacket, grey_jacket, midriff, navel, official_alternate_costume, race_queen, solo, black_hairband, choker, fingerless_gloves, looking_at_viewer, skirt, black_gloves, cowboy_shot, hand_on_hip, smile, blue_background, grey_gloves, long_sleeves |
| 4 | 5 |  |  |  |  |  | 1girl, cleavage, cropped_jacket, midriff, official_alternate_costume, race_queen, simple_background, white_background, black_hairband, looking_at_viewer, navel, solo, bandeau, cowboy_shot, upper_body, black_skirt, choker, grey_jacket, smile |
| 5 | 10 |  |  |  |  |  | 1girl, solo, cleavage, simple_background, white_background, looking_at_viewer, blush, twitter_username, collarbone, one-hour_drawing_challenge, side-tie_bikini_bottom, hair_between_eyes, navel, cowboy_shot, dated |
| 6 | 8 |  |  |  |  |  | 1girl, navel, nipples, solo, completely_nude, armpits, arms_up, looking_at_viewer, blush, female_pubic_hair, collarbone, full_body, cowboy_shot, simple_background, standing, sweat |
| 7 | 7 |  |  |  |  |  | 1girl, alternate_costume, black_hairband, long_sleeves, solo, turtleneck, cowboy_shot, looking_at_viewer, smile, white_coat, bag, blush, grey_coat, hair_between_eyes, pants, purple_sweater, red_sweater |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fake_animal_ears | playboy_bunny | rabbit_ears | solo | wrist_cuffs | black_pantyhose | alternate_costume | black_leotard | bowtie | detached_collar | cleavage | black_footwear | covered_navel | cowboy_shot | fishnet_pantyhose | full_body | high_heels | looking_at_viewer | simple_background | standing | long_sleeves | military_uniform | red_ascot | red_rose | pencil_skirt | white_background | smile | one-hour_drawing_challenge | open_mouth | twitter_username | upper_body | skirt | cropped_jacket | grey_jacket | midriff | navel | official_alternate_costume | race_queen | black_hairband | choker | fingerless_gloves | black_gloves | hand_on_hip | blue_background | grey_gloves | bandeau | black_skirt | blush | collarbone | side-tie_bikini_bottom | hair_between_eyes | dated | nipples | completely_nude | armpits | arms_up | female_pubic_hair | sweat | turtleneck | white_coat | bag | grey_coat | pants | purple_sweater | red_sweater |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------------------|:----------------|:--------------|:-------|:--------------|:------------------|:--------------------|:----------------|:---------|:------------------|:-----------|:-----------------|:----------------|:--------------|:--------------------|:------------|:-------------|:--------------------|:--------------------|:-----------|:---------------|:-------------------|:------------|:-----------|:---------------|:-------------------|:--------|:-----------------------------|:-------------|:-------------------|:-------------|:--------|:-----------------|:--------------|:----------|:--------|:-----------------------------|:-------------|:-----------------|:---------|:--------------------|:---------------|:--------------|:------------------|:--------------|:----------|:--------------|:--------|:-------------|:-------------------------|:--------------------|:--------|:----------|:------------------|:----------|:----------|:--------------------|:--------|:-------------|:-------------|:------|:------------|:--------|:-----------------|:--------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | | | | X | | | | | | | | | | X | | | | X | X | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | | | X | | | | | | | | | | | | | | X | X | | X | X | X | X | | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | |
| 5 | 10 |  |  |  |  |  | X | | | | X | | | | | | | X | | | X | | | | X | X | | | | | | | X | | X | | X | | | | | | X | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | 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 |
|
irds/neumarco_zh_train_judged | ---
pretty_name: '`neumarco/zh/train/judged`'
viewer: false
source_datasets: ['irds/neumarco_zh', 'irds/neumarco_zh_train']
task_categories:
- text-retrieval
---
# Dataset Card for `neumarco/zh/train/judged`
The `neumarco/zh/train/judged` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/neumarco#neumarco/zh/train/judged).
# Data
This dataset provides:
- `queries` (i.e., topics); count=502,939
- For `docs`, use [`irds/neumarco_zh`](https://huggingface.co/datasets/irds/neumarco_zh)
- For `qrels`, use [`irds/neumarco_zh_train`](https://huggingface.co/datasets/irds/neumarco_zh_train)
- For `docpairs`, use [`irds/neumarco_zh_train`](https://huggingface.co/datasets/irds/neumarco_zh_train)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/neumarco_zh_train_judged', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
|
mrm8488/spanish_biomedical_ds_tokenized_and_gropuped | ---
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 3601107900
num_examples: 878319
- name: test
num_bytes: 187816900
num_examples: 45809
download_size: 1807775268
dataset_size: 3788924800
---
# Dataset Card for "spanish_biomedical_ds_tokenized_and_gropuped"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
luiseduardobrito/similarity-sentences-portuguese | ---
task_categories:
- text-classification
language:
- pt
---
# similarity-sentences-portuguese (SSP)
### Dataset Summary
This dataset comprises a collection of sentences generated using Chat GPT-3, covering various general topics, originally in spanish by [jaimevera1107](https://huggingface.co/datasets/jaimevera1107/similarity-sentences-spanish).
The sentences were translated to portuguese using [seamless-m4t-medium](https://huggingface.co/facebook/seamless-m4t-medium).
### Languages
Portuguese
## Dataset Structure
### Data Fields
- Sentence 1: The first sentence to be compared.
- Sentence 2: The second sentence to be compared.
- Score: A number between 0 and 1 indicating the similarity between Sentence 1 and Sentence 2, with 1 indicating high similarity.
- Source: The source of the information, represented by its abbreviation.
## Dataset Biases
This dataset inherits the biases present in the two existing datasets and the biases inherent in a text generation model like Chat GPT-3.
|
brainer/pill_identification_graph | ---
dataset_info:
- config_name: co-graph-edges
features:
- name: source
dtype: string
- name: target
dtype: string
- name: weight
dtype: int64
splits:
- name: train
num_bytes: 3305038
num_examples: 97207
download_size: 710459
dataset_size: 3305038
- config_name: co-graph-nodes
features:
- name: id
dtype: string
splits:
- name: train
num_bytes: 33462
num_examples: 2574
download_size: 21573
dataset_size: 33462
- config_name: merge-hira-pill_identification-edges
features:
- name: source
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 1397407
num_examples: 53749
download_size: 535187
dataset_size: 1397407
- config_name: merge-hira-pill_identification-nodes
features:
- name: id
dtype: string
splits:
- name: train
num_bytes: 835118
num_examples: 64245
download_size: 510022
dataset_size: 835118
- config_name: size-graph-edges
features:
- name: source
dtype: string
- name: target
dtype: string
- name: width
dtype: float64
- name: weight
dtype: float64
splits:
- name: train
num_bytes: 3194482
num_examples: 75609
download_size: 986413
dataset_size: 3194482
- config_name: size-graph-nodes
features:
- name: id
dtype: string
splits:
- name: train
num_bytes: 327665
num_examples: 25205
download_size: 179993
dataset_size: 327665
configs:
- config_name: co-graph-edges
data_files:
- split: train
path: co-graph-edges/train-*
- config_name: co-graph-nodes
data_files:
- split: train
path: co-graph-nodes/train-*
- config_name: merge-hira-pill_identification-edges
data_files:
- split: train
path: merge-hira-pill_identification-edges/train-*
- config_name: merge-hira-pill_identification-nodes
data_files:
- split: train
path: merge-hira-pill_identification-nodes/train-*
- config_name: size-graph-edges
data_files:
- split: train
path: size-graph-edges/train-*
- config_name: size-graph-nodes
data_files:
- split: train
path: size-graph-nodes/train-*
---
|
BangumiBase/imoutosaeirebaii | ---
license: mit
tags:
- art
size_categories:
- n<1K
---
# Bangumi Image Base of Imouto Sae Ireba Ii
This is the image base of bangumi Imouto sae Ireba Ii, we detected 18 characters, 622 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 | 30 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 88 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 7 | [Download](2/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 3 | 36 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 179 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 28 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 29 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 37 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 7 | [Download](8/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 9 | 6 | [Download](9/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 10 | 8 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 10 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 7 | [Download](12/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 15 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 14 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 69 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 42 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
autoevaluate/autoeval-staging-eval-project-d3ec9b9a-b64a-40a0-baff-3af478f604df-367 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- squad_v2
eval_info:
task: extractive_question_answering
model: autoevaluate/extractive-question-answering
metrics: []
dataset_name: squad_v2
dataset_config: squad_v2
dataset_split: validation
col_mapping:
context: context
question: question
answers-text: answers.text
answers-answer_start: answers.answer_start
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Question Answering
* Model: autoevaluate/extractive-question-answering
* Dataset: squad_v2
* Config: squad_v2
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
ABC-iRobotics/oe_dataset | ---
language:
- en
license: gpl-3.0
tags:
- vision
- image segmentation
- instance segmentation
- object detection
- synthetic
- sim-to-real
annotations_creators:
- machine-generated
pretty_name: OE Dataset
size_categories:
- 1K<n<10K
task_categories:
- object-detection
- image-segmentation
- robotics
task_ids:
- instance-segmentation
- semantic-segmentation
---
# The OE Dataset!
A dataset consisting of synthetic and real images annotated with instance segmentation masks for testing sim-to-real model performance for robotic manipulation.
### Dataset Summary
The OE Dataset is a collection of synthetic and real images of 3D-printed OE logos. Each image is annotated with instance segmentation masks. The dataset explicitly marks synthetic samples based on their creation method (either photorealistic synthetic samples or domain randomized samples) to facilitate sim-to-real performance tests on different synthetic datasets.
### Supported Tasks and Leaderboards
The dataset supports tasks such as semantic segmentation, instance segmentation, object detection, image classification, and testing sim-to-real transfer.
## Dataset Structure
### Data Instances
The instances of the dataset are 1920x1080x3 images in PNG format. The annotations are 1920x1080x4 PNG images representing the instance segmentation masks, where each instance is associated with a unique color.
### Data Fields
The data fields are:
1) 'image': 1920x1080x3 PNG image
2) 'mask': 1920x1080x4 PNG image
### Data Splits
The dataset contains training and validation splits for all image collections (real images, photorealistic synthetic images, domain randomized synthetic images) to facilitate cross-domain testing.
## Dataset Creation
### Curation Rationale
The dataset was created to provide a testbed for examining the effects of fine-tuning instance segmentation models on synthetic data (using various sim-to-real approaches).
### Source Data
The data is generated using two methods:
- Real images are recorded using a robotic setup and automatically annotated using the method proposed in [[1]](https://ieeexplore.ieee.org/abstract/document/9922852)
- Synthetic samples are generated using Blender and annotated using the [Blender Annotation Tool (BAT)](https://github.com/ABC-iRobotics/blender_annotation_tool)
### Citation Information
OE Dataset:
```bibtex
@ARTICLE{10145828,
author={Károly, Artúr István and Tirczka, Sebestyén and Gao, Huijun and Rudas, Imre J. and Galambos, Péter},
journal={IEEE Transactions on Cybernetics},
title={Increasing the Robustness of Deep Learning Models for Object Segmentation: A Framework for Blending Automatically Annotated Real and Synthetic Data},
year={2023},
volume={},
number={},
pages={1-14},
doi={10.1109/TCYB.2023.3276485}}
```
Automatically annotating real images with instance segmentation masks using a robotic arm:
```bibtex
@INPROCEEDINGS{9922852,
author={Károly, Artúr I. and Károly, Ármin and Galambos, Péter},
booktitle={2022 IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC)},
title={Automatic Generation and Annotation of Object Segmentation Datasets Using Robotic Arm},
year={2022},
volume={},
number={},
pages={000063-000068},
doi={10.1109/ICCC202255925.2022.9922852}}
```
Synthetic dataset generation and annotation method:
```bibtex
@INPROCEEDINGS{9780790,
author={Károly, Artúr I. and Galambos, Péter},
booktitle={2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI)},
title={Automated Dataset Generation with Blender for Deep Learning-based Object Segmentation},
year={2022},
volume={},
number={},
pages={000329-000334},
doi={10.1109/SAMI54271.2022.9780790}}
```
Other related publications:
```bibtex
@INPROCEEDINGS{10029564,
author={Károly, Artúr I. and Tirczka, Sebestyén and Piricz, Tamás and Galambos, Péter},
booktitle={2022 IEEE 22nd International Symposium on Computational Intelligence and Informatics and 8th IEEE International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo)},
title={Robotic Manipulation of Pathological Slides Powered by Deep Learning and Classical Image Processing},
year={2022},
volume={},
number={},
pages={000387-000392},
doi={10.1109/CINTI-MACRo57952.2022.10029564}}
```
```bibtex
@Article{app13010525,
AUTHOR = {Károly, Artúr István and Galambos, Péter},
TITLE = {Task-Specific Grasp Planning for Robotic Assembly by Fine-Tuning GQCNNs on Automatically Generated Synthetic Data},
JOURNAL = {Applied Sciences},
VOLUME = {13},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {525},
URL = {https://www.mdpi.com/2076-3417/13/1/525},
ISSN = {2076-3417},
ABSTRACT = {In modern robot applications, there is often a need to manipulate previously unknown objects in an unstructured environment. The field of grasp-planning deals with the task of finding grasps for a given object that can be successfully executed with a robot. The predicted grasps can be evaluated according to certain criteria, such as analytical metrics, similarity to human-provided grasps, or the success rate of physical trials. The quality of a grasp also depends on the task which will be carried out after the grasping is completed. Current task-specific grasp planning approaches mostly use probabilistic methods, which utilize categorical task encoding. We argue that categorical task encoding may not be suitable for complex assembly tasks. This paper proposes a transfer-learning-based approach for task-specific grasp planning for robotic assembly. The proposed method is based on an automated pipeline that quickly and automatically generates a small-scale task-specific synthetic grasp dataset using Graspit! and Blender. This dataset is utilized to fine-tune pre-trained grasp quality convolutional neural networks (GQCNNs). The aim is to train GQCNNs that can predict grasps which do not result in a collision when placing the objects. Consequently, this paper focuses on the geometric feasibility of the predicted grasps and does not consider the dynamic effects. The fine-tuned GQCNNs are evaluated using the Moveit! Task Constructor motion planning framework, which enables the automated inspection of whether the motion planning for a task is feasible given a predicted grasp and, if not, which part of the task is responsible for the failure. Our results suggest that fine-tuning GQCNN models can result in superior grasp-planning performance (0.9 success rate compared to 0.65) in the context of an assembly task. Our method can be used to rapidly attain new task-specific grasp policies for flexible robotic assembly applications.},
DOI = {10.3390/app13010525}
}
``` |
mrfakename/Code-Feedback-ShareGPT | ---
license: apache-2.0
---
From https://huggingface.co/datasets/m-a-p/Code-Feedback |
datajuicer/the-pile-uspto-refined-by-data-juicer | ---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- data-juicer
- pretraining
size_categories:
- 1M<n<10M
---
# The Pile -- USPTO (refined by Data-Juicer)
A refined version of USPTO dataset in The Pile by [Data-Juicer](https://github.com/alibaba/data-juicer). Removing some "bad" samples from the original dataset to make it higher-quality.
This dataset is usually used to pretrain a Large Language Model.
**Notice**: Here is a small subset for previewing. The whole dataset is available [here](https://dail-wlcb.oss-cn-wulanchabu.aliyuncs.com/LLM_data/our_refined_datasets/pretraining/the-pile-uspto-refine-result.jsonl) (About 18G).
## Dataset Information
- Number of samples: 4,516,283 (Keep ~46.77% from the original dataset)
## Refining Recipe
```yaml
# global parameters
project_name: 'Data-Juicer-recipes-uspto'
dataset_path: '/path/to/your/dataset' # path to your dataset directory or file
export_path: '/path/to/your/dataset.jsonl' # path to your dataset result file
np: 50 # number of subprocess to process your dataset
open_tracer: true
# process schedule
# a list of several process operators with their arguments
process:
- clean_email_mapper:
- clean_links_mapper:
- fix_unicode_mapper:
- punctuation_normalization_mapper:
- whitespace_normalization_mapper:
- alphanumeric_filter:
tokenization: false
min_ratio: 0.7 # <3sigma (0.758)
- average_line_length_filter: # for code
max_len: 2000 # >3sigma (1307)
- character_repetition_filter:
rep_len: 10
max_ratio: 0.2 # >3sigma (0.189)
- flagged_words_filter:
lang: en
tokenization: true
max_ratio: 0.0016 # 3sigma
- language_id_score_filter:
min_score: 0.6
- maximum_line_length_filter: # for code
max_len: 3061 # 3sigma
- perplexity_filter:
lang: en
max_ppl: 4000 # 3sigma
- special_characters_filter:
max_ratio: 0.3 # > 3sigma (0.274)
- text_length_filter:
max_len: 21556 # 3sigma
- words_num_filter:
lang: en
tokenization: true
min_num: 100
max_num: 6000 # 3sigma
- word_repetition_filter:
lang: en
tokenization: true
rep_len: 10
max_ratio: 0.169 # 3sigma
- document_simhash_deduplicator:
tokenization: space
window_size: 6
lowercase: true
ignore_pattern: '\p{P}'
num_blocks: 6
hamming_distance: 4
``` |
jan-hq/rag_dataset_12000_binarized | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 84953348.29083334
num_examples: 10797
- name: test
num_bytes: 9511692
num_examples: 1200
download_size: 56864331
dataset_size: 94465040.29083334
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
joey234/mmlu-professional_medicine-neg-prepend-fix | ---
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: ori_prompt
dtype: string
splits:
- name: dev
num_bytes: 13678
num_examples: 5
- name: test
num_bytes: 1083116
num_examples: 272
download_size: 26475
dataset_size: 1096794
---
# Dataset Card for "mmlu-professional_medicine-neg-prepend-fix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/takafuji_kako_idolmastercinderellagirls | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of takafuji_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls)
This is the dataset of takafuji_kako/鷹富士茄子 (THE iDOLM@STER: Cinderella Girls), containing 341 images and their tags.
The core tags of this character are `black_hair, short_hair, breasts, yellow_eyes, bangs, large_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 | 341 | 446.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 341 | 259.42 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 830 | 551.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 341 | 398.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 830 | 772.03 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takafuji_kako_idolmastercinderellagirls/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/takafuji_kako_idolmastercinderellagirls',
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 | 9 |  |  |  |  |  | 1girl, cleavage, looking_at_viewer, necklace, smile, solo, navel, open_mouth, blush, frilled_bikini, outdoors, bracelet, collarbone, day, hair_flower, medium_breasts, beach, blue_bikini, floral_print, front-tie_top, side-tie_bikini_bottom |
| 1 | 18 |  |  |  |  |  | kimono, smile, 1girl, hair_flower, looking_at_viewer, solo, blush, obi, floral_print, upper_body |
| 2 | 34 |  |  |  |  |  | 1girl, solo, smile, hair_bow, looking_at_viewer, detached_sleeves, cleavage, medium_breasts, navel, blush, midriff, bare_shoulders, open_mouth, japanese_clothes |
| 3 | 6 |  |  |  |  |  | 1girl, blush, collarbone, looking_at_viewer, navel, nipples, solo, completely_nude, brown_eyes, cowboy_shot, medium_breasts, simple_background, smile, white_background |
| 4 | 7 |  |  |  |  |  | cleavage, detached_collar, playboy_bunny, rabbit_ears, 1girl, brown_eyes, wrist_cuffs, bowtie, looking_at_viewer, rabbit_tail, smile, solo, open_mouth, strapless_leotard, black_leotard, black_pantyhose, cowboy_shot, fake_animal_ears, fishnets, white_background |
| 5 | 6 |  |  |  |  |  | 1boy, 1girl, blush, hetero, solo_focus, nipples, smile, breast_grab, collarbone, grabbing, looking_at_viewer, nude, penis, pov, censored, cum_on_body, male_pubic_hair, on_back, open_mouth, paizuri, sweat |
| 6 | 5 |  |  |  |  |  | 1girl, onsen, solo, blush, looking_at_viewer, naked_towel, smile, collarbone, full_moon, water, closed_mouth, medium_breasts, night_sky, nipples, ponytail, snow, wet |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | cleavage | looking_at_viewer | necklace | smile | solo | navel | open_mouth | blush | frilled_bikini | outdoors | bracelet | collarbone | day | hair_flower | medium_breasts | beach | blue_bikini | floral_print | front-tie_top | side-tie_bikini_bottom | kimono | obi | upper_body | hair_bow | detached_sleeves | midriff | bare_shoulders | japanese_clothes | nipples | completely_nude | brown_eyes | cowboy_shot | simple_background | white_background | detached_collar | playboy_bunny | rabbit_ears | wrist_cuffs | bowtie | rabbit_tail | strapless_leotard | black_leotard | black_pantyhose | fake_animal_ears | fishnets | 1boy | hetero | solo_focus | breast_grab | grabbing | nude | penis | pov | censored | cum_on_body | male_pubic_hair | on_back | paizuri | sweat | onsen | naked_towel | full_moon | water | closed_mouth | night_sky | ponytail | snow | wet |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:--------------------|:-----------|:--------|:-------|:--------|:-------------|:--------|:-----------------|:-----------|:-----------|:-------------|:------|:--------------|:-----------------|:--------|:--------------|:---------------|:----------------|:-------------------------|:---------|:------|:-------------|:-----------|:-------------------|:----------|:-----------------|:-------------------|:----------|:------------------|:-------------|:--------------|:--------------------|:-------------------|:------------------|:----------------|:--------------|:--------------|:---------|:--------------|:--------------------|:----------------|:------------------|:-------------------|:-----------|:-------|:---------|:-------------|:--------------|:-----------|:-------|:--------|:------|:-----------|:--------------|:------------------|:----------|:----------|:--------|:--------|:--------------|:------------|:--------|:---------------|:------------|:-----------|:-------|:------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 18 |  |  |  |  |  | X | | X | | X | X | | | X | | | | | | X | | | | X | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 34 |  |  |  |  |  | X | X | X | | X | X | X | X | X | | | | | | | X | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | | X | X | X | | X | | | | X | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | 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 | X | X | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | X | | X | X | | | X | | | | X | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
mask-distilled-one-sec-cv12/chunk_92 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1262316984
num_examples: 247902
download_size: 1287803838
dataset_size: 1262316984
---
# Dataset Card for "chunk_92"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-project-e1907042-7494834 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- clinc_oos
eval_info:
task: multi_class_classification
model: calcworks/distilbert-base-uncased-distilled-clinc
metrics: []
dataset_name: clinc_oos
dataset_config: small
dataset_split: test
col_mapping:
text: text
target: intent
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Multi-class Text Classification
* Model: calcworks/distilbert-base-uncased-distilled-clinc
* Dataset: clinc_oos
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@lewtun](https://huggingface.co/lewtun) for evaluating this model. |
nannullna/laion-subset | ---
license: mit
task_categories:
- text-to-image
language:
- en
size_categories:
- 10K<n<100K
--- |
irds/beir_nfcorpus | ---
pretty_name: '`beir/nfcorpus`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `beir/nfcorpus`
The `beir/nfcorpus` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/beir#beir/nfcorpus).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=3,633
- `queries` (i.e., topics); count=3,237
This dataset is used by: [`beir_nfcorpus_dev`](https://huggingface.co/datasets/irds/beir_nfcorpus_dev), [`beir_nfcorpus_test`](https://huggingface.co/datasets/irds/beir_nfcorpus_test), [`beir_nfcorpus_train`](https://huggingface.co/datasets/irds/beir_nfcorpus_train)
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/beir_nfcorpus', 'docs')
for record in docs:
record # {'doc_id': ..., 'text': ..., 'title': ..., 'url': ...}
queries = load_dataset('irds/beir_nfcorpus', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ..., 'url': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{Boteva2016Nfcorpus,
title="A Full-Text Learning to Rank Dataset for Medical Information Retrieval",
author = "Vera Boteva and Demian Gholipour and Artem Sokolov and Stefan Riezler",
booktitle = "Proceedings of the European Conference on Information Retrieval ({ECIR})",
location = "Padova, Italy",
publisher = "Springer",
year = 2016
}
@article{Thakur2021Beir,
title = "BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models",
author = "Thakur, Nandan and Reimers, Nils and Rücklé, Andreas and Srivastava, Abhishek and Gurevych, Iryna",
journal= "arXiv preprint arXiv:2104.08663",
month = "4",
year = "2021",
url = "https://arxiv.org/abs/2104.08663",
}
```
|
4eJIoBek/ru-libinpoc-11k | ---
license: mit
task_categories:
- text-generation
size_categories:
- 10K<n<100K
---
11,5k russian books in txt format, divided by genres
11,5 тыщ книг русской литературы. датасет сделан из древнющего диска "lib in poc" |
open-llm-leaderboard/details_KoboldAI__OPT-2.7B-Nerys-v2 | ---
pretty_name: Evaluation run of KoboldAI/OPT-2.7B-Nerys-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [KoboldAI/OPT-2.7B-Nerys-v2](https://huggingface.co/KoboldAI/OPT-2.7B-Nerys-v2)\
\ 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_KoboldAI__OPT-2.7B-Nerys-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T17:38:49.546880](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__OPT-2.7B-Nerys-v2/blob/main/results_2023-10-15T17-38-49.546880.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\": 0.0008389261744966443,\n\
\ \"em_stderr\": 0.0002964962989801233,\n \"f1\": 0.04603607382550343,\n\
\ \"f1_stderr\": 0.0011567494331612429,\n \"acc\": 0.31169783140345136,\n\
\ \"acc_stderr\": 0.007576909482467849\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0008389261744966443,\n \"em_stderr\": 0.0002964962989801233,\n\
\ \"f1\": 0.04603607382550343,\n \"f1_stderr\": 0.0011567494331612429\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.003032600454890068,\n \
\ \"acc_stderr\": 0.0015145735612245438\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6203630623520127,\n \"acc_stderr\": 0.013639245403711154\n\
\ }\n}\n```"
repo_url: https://huggingface.co/KoboldAI/OPT-2.7B-Nerys-v2
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_07_19T17_09_58.471604
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T17_38_49.546880
path:
- '**/details_harness|drop|3_2023-10-15T17-38-49.546880.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T17-38-49.546880.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T17_38_49.546880
path:
- '**/details_harness|gsm8k|5_2023-10-15T17-38-49.546880.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T17-38-49.546880.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:09:58.471604.parquet'
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- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:09:58.471604.parquet'
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- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:09:58.471604.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:09:58.471604.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:09:58.471604.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:09:58.471604.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T17_38_49.546880
path:
- '**/details_harness|winogrande|5_2023-10-15T17-38-49.546880.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T17-38-49.546880.parquet'
- config_name: results
data_files:
- split: 2023_07_19T17_09_58.471604
path:
- results_2023-07-19T17:09:58.471604.parquet
- split: 2023_10_15T17_38_49.546880
path:
- results_2023-10-15T17-38-49.546880.parquet
- split: latest
path:
- results_2023-10-15T17-38-49.546880.parquet
---
# Dataset Card for Evaluation run of KoboldAI/OPT-2.7B-Nerys-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/KoboldAI/OPT-2.7B-Nerys-v2
- **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 [KoboldAI/OPT-2.7B-Nerys-v2](https://huggingface.co/KoboldAI/OPT-2.7B-Nerys-v2) 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_KoboldAI__OPT-2.7B-Nerys-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T17:38:49.546880](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__OPT-2.7B-Nerys-v2/blob/main/results_2023-10-15T17-38-49.546880.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": 0.0008389261744966443,
"em_stderr": 0.0002964962989801233,
"f1": 0.04603607382550343,
"f1_stderr": 0.0011567494331612429,
"acc": 0.31169783140345136,
"acc_stderr": 0.007576909482467849
},
"harness|drop|3": {
"em": 0.0008389261744966443,
"em_stderr": 0.0002964962989801233,
"f1": 0.04603607382550343,
"f1_stderr": 0.0011567494331612429
},
"harness|gsm8k|5": {
"acc": 0.003032600454890068,
"acc_stderr": 0.0015145735612245438
},
"harness|winogrande|5": {
"acc": 0.6203630623520127,
"acc_stderr": 0.013639245403711154
}
}
```
### 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] |
DILAB-HYU/SimKoR | ---
license: cc-by-4.0
---
# SimKoR
We provide korean sentence text similarity pair dataset using sentiment analysis corpus from [bab2min/corpus](https://github.com/bab2min/corpus).
This data crawling korean review from naver shopping website. we reconstruct subset of dataset to make our dataset.
## Dataset description
The original dataset description can be found at the link [[here]](https://github.com/bab2min/corpus/tree/master/sentiment).
In korean Contrastive Learning, There are few suitable validation dataset (only KorNLI). To create contrastive learning validation dataset, we changed original sentiment analysis dataset to sentence text similar dataset. Our simkor dataset was created by grouping pair of sentence. Each score [0,1,2,4,5] means how far the meaning is between sentences.
## Data Distribution
Our dataset class consist of text similarity score [0, 1,2,4,5]. each score consists of data of the same size.
<table>
<tr><th>Score</th><th>train</th><th>valid</th><th>test</th></tr>
<tr><th>5</th><th>4,000</th><th>1,000</th><th>1,000</th></tr>
<tr><th>4</th><th>4,000</th><th>1,000</th><th>1,000</th></tr>
<tr><th>2</th><th>4,000</th><th>1,000</th><th>1,000</th></tr>
<tr><th>1</th><th>4,000</th><th>1,000</th><th>1,000</th></tr>
<tr><th>0</th><th>4,000</th><th>1,000</th><th>1,000</th></tr>
<tr><th>All</th><th>20,000</th><th>5,000</th><th>5,000</th></tr>
</table>
## Example
```
text1 text2 label
고속충전이 안됨ㅠㅠ 집에매연냄새없앨려했는데 그냥창문여는게더 공기가좋네요 5
적당히 맵고 괜찮네요 어제 시킨게 벌써 왔어요 ㅎㅎ 배송빠르고 품질양호합니다 4
다 괜찮은데 배송이 10일이나 걸린게 많이 아쉽네요. 선반 설치하고 나니 주방 베란다 완전 다시 태어났어요~ 2
가격 싸지만 쿠션이 약해 무릎 아파요~ 반품하려구요~ 튼튼하고 빨래도 많이 걸 수 있고 잘쓰고 있어요 1
각인이 찌그저져있고 엉성합니다. 처음 해보는 방탈출이었는데 너무 재미있었어요. 0
```
## Contributors
The main contributors of the work are :
- [Jaemin Kim](https://github.com/kimfunn)\*
- [Yohan Na](https://github.com/nayohan)\*
- [Kangmin Kim](https://github.com/Gangsss)
- [Sangrak Lee](https://github.com/PangRAK)
\*: Equal Contribution
Hanyang University Data Intelligence Lab[(DILAB)](http://dilab.hanyang.ac.kr/) providing support ❤️
## Github
- **Repository :** [SimKoR](https://github.com/nayohan/SimKoR)
## License
<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a>This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>. |
litagin/moe-speech-metadata | ---
license: other
extra_gated_fields:
Your twitter (X) account or discord accout name: text
I want to use this dataset for: text
viewer: false
---
[LICENSE](https://huggingface.co/spaces/litagin/moe-speech-license)
- Extra data for [MoeSpeech ver 0.3](https://huggingface.co/datasets/litagin/moe-speech)
- Currently transcriptions (by faster whisper large-v3 int8) only, and not manually modified so contain some error.
- You need my permission to access this dataset. I may not grant access to individuals I do not know. |
sheik21/leo-voz | ---
license: openrail
---
|
open-llm-leaderboard/details_yeen214__test_llama2_ko_7b | ---
pretty_name: Evaluation run of yeen214/test_llama2_ko_7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeen214/test_llama2_ko_7b](https://huggingface.co/yeen214/test_llama2_ko_7b)\
\ 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_yeen214__test_llama2_ko_7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-24T10:13:17.932483](https://huggingface.co/datasets/open-llm-leaderboard/details_yeen214__test_llama2_ko_7b/blob/main/results_2023-10-24T10-13-17.932483.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\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 2.3070469798657714e-05,\n \"f1_stderr\"\
: 9.018273500539545e-06,\n \"acc\": 0.24191002367797948,\n \"acc_stderr\"\
: 0.007022563065489298\n },\n \"harness|drop|3\": {\n \"em\": 0.0,\n\
\ \"em_stderr\": 0.0,\n \"f1\": 2.3070469798657714e-05,\n \"\
f1_stderr\": 9.018273500539545e-06\n },\n \"harness|gsm8k|5\": {\n \
\ \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.48382004735595896,\n \"acc_stderr\": 0.014045126130978596\n\
\ }\n}\n```"
repo_url: https://huggingface.co/yeen214/test_llama2_ko_7b
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|arc:challenge|25_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_24T10_13_17.932483
path:
- '**/details_harness|drop|3_2023-10-24T10-13-17.932483.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-24T10-13-17.932483.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_24T10_13_17.932483
path:
- '**/details_harness|gsm8k|5_2023-10-24T10-13-17.932483.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-24T10-13-17.932483.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hellaswag|10_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-48-16.505628.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T06-48-16.505628.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-10-04T06-48-16.505628.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_24T10_13_17.932483
path:
- '**/details_harness|winogrande|5_2023-10-24T10-13-17.932483.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-24T10-13-17.932483.parquet'
- config_name: results
data_files:
- split: 2023_10_04T06_48_16.505628
path:
- results_2023-10-04T06-48-16.505628.parquet
- split: 2023_10_24T10_13_17.932483
path:
- results_2023-10-24T10-13-17.932483.parquet
- split: latest
path:
- results_2023-10-24T10-13-17.932483.parquet
---
# Dataset Card for Evaluation run of yeen214/test_llama2_ko_7b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeen214/test_llama2_ko_7b
- **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 [yeen214/test_llama2_ko_7b](https://huggingface.co/yeen214/test_llama2_ko_7b) 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_yeen214__test_llama2_ko_7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-24T10:13:17.932483](https://huggingface.co/datasets/open-llm-leaderboard/details_yeen214__test_llama2_ko_7b/blob/main/results_2023-10-24T10-13-17.932483.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": 0.0,
"em_stderr": 0.0,
"f1": 2.3070469798657714e-05,
"f1_stderr": 9.018273500539545e-06,
"acc": 0.24191002367797948,
"acc_stderr": 0.007022563065489298
},
"harness|drop|3": {
"em": 0.0,
"em_stderr": 0.0,
"f1": 2.3070469798657714e-05,
"f1_stderr": 9.018273500539545e-06
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.48382004735595896,
"acc_stderr": 0.014045126130978596
}
}
```
### 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] |
tyzhu/random25eof_find_passage_train10000_eval100_rare | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 2111428
num_examples: 20100
- name: validation
num_bytes: 11904
num_examples: 100
download_size: 707669
dataset_size: 2123332
---
# Dataset Card for "random25eof_find_passage_train10000_eval100_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/cleopatra_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of cleopatra/クレオパトラ/克娄巴特拉 (Fate/Grand Order)
This is the dataset of cleopatra/クレオパトラ/克娄巴特拉 (Fate/Grand Order), containing 231 images and their tags.
The core tags of this character are `long_hair, hairband, breasts, green_eyes, green_hair, earrings, very_long_hair, hoop_earrings, blunt_bangs, large_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 | 231 | 334.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cleopatra_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 231 | 293.21 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cleopatra_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 538 | 536.87 MiB | [Download](https://huggingface.co/datasets/CyberHarem/cleopatra_fgo/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/cleopatra_fgo',
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 | 7 |  |  |  |  |  | 1girl, blush, large_areolae, looking_at_viewer, nipples, solo, jewelry, nude, collarbone, sweat, huge_breasts, smile, gigantic_breasts, thighs, tongue_out |
| 1 | 5 |  |  |  |  |  | 1girl, bracelet, collarbone, looking_at_viewer, navel, nipples, nude, smile, thighs, armlet, blush, choker, huge_breasts, large_areolae, pussy, solo, bar_censor, necklace, ring, sweat |
| 2 | 5 |  |  |  |  |  | 1girl, huge_breasts, looking_at_viewer, navel, solo, armlet, black_bikini, gigantic_breasts, large_areolae, nipples, thighs, topless, alternate_breast_size, bikini_bottom_only, blush, bracelet, choker, hand_on_own_hip, animal_ears, aqua_hair, collarbone, eyeliner, grin, panties, side-tie_bikini_bottom, sidelocks, simple_background, thumb_ring, white_background |
| 3 | 13 |  |  |  |  |  | 1girl, solo, bracelet, looking_at_viewer, necklace, white_dress, armlet, collarbone, smile, choker, closed_mouth, bare_shoulders, cleavage, medium_breasts, sitting, thighs, thumb_ring |
| 4 | 7 |  |  |  |  |  | 1girl, black_shorts, looking_at_viewer, smile, solo, belt, pantyhose_under_shorts, short_shorts, black_footwear, knee_boots, necklace, blue_eyes, bracelet, brown_pantyhose, long_sleeves, sitting, thumb_ring |
| 5 | 8 |  |  |  |  |  | 1girl, black_shorts, long_sleeves, looking_at_viewer, short_shorts, solo, belt, cowboy_shot, necklace, smile, pantyhose_under_shorts, white_background, closed_mouth, simple_background, bracelet, thumb_ring |
| 6 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, necklace, shorts, simple_background, white_background, black_footwear, knee_boots, brown_pantyhose, high_heels |
| 7 | 5 |  |  |  |  |  | 1girl, black_footwear, black_shorts, hand_on_own_hip, knee_boots, short_shorts, smile, solo, belt, blue_hair, bracelet, full_body, looking_at_viewer, necklace, standing, pantyhose_under_shorts, simple_background, white_background, absurdly_long_hair, long_sleeves, thumb_ring |
| 8 | 5 |  |  |  |  |  | 1boy, 1girl, armlet, blush, hetero, nipples, nude, sex, sweat, huge_breasts, navel, smile, solo_focus, thighs, vaginal, cum_in_pussy, girl_on_top, lactation, penis, bar_censor, bracelet, closed_mouth, collarbone, cowgirl_position, ejaculation, grabbing_another's_breast, large_areolae, looking_at_viewer, mosaic_censoring, open_mouth, sidelocks, spread_legs, white_choker |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | large_areolae | looking_at_viewer | nipples | solo | jewelry | nude | collarbone | sweat | huge_breasts | smile | gigantic_breasts | thighs | tongue_out | bracelet | navel | armlet | choker | pussy | bar_censor | necklace | ring | black_bikini | topless | alternate_breast_size | bikini_bottom_only | hand_on_own_hip | animal_ears | aqua_hair | eyeliner | grin | panties | side-tie_bikini_bottom | sidelocks | simple_background | thumb_ring | white_background | white_dress | closed_mouth | bare_shoulders | cleavage | medium_breasts | sitting | black_shorts | belt | pantyhose_under_shorts | short_shorts | black_footwear | knee_boots | blue_eyes | brown_pantyhose | long_sleeves | cowboy_shot | shorts | high_heels | blue_hair | full_body | standing | absurdly_long_hair | 1boy | hetero | sex | solo_focus | vaginal | cum_in_pussy | girl_on_top | lactation | penis | cowgirl_position | ejaculation | grabbing_another's_breast | mosaic_censoring | open_mouth | spread_legs | white_choker |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------------|:--------------------|:----------|:-------|:----------|:-------|:-------------|:--------|:---------------|:--------|:-------------------|:---------|:-------------|:-----------|:--------|:---------|:---------|:--------|:-------------|:-----------|:-------|:---------------|:----------|:------------------------|:---------------------|:------------------|:--------------|:------------|:-----------|:-------|:----------|:-------------------------|:------------|:--------------------|:-------------|:-------------------|:--------------|:---------------|:-----------------|:-----------|:-----------------|:----------|:---------------|:-------|:-------------------------|:---------------|:-----------------|:-------------|:------------|:------------------|:---------------|:--------------|:---------|:-------------|:------------|:------------|:-----------|:---------------------|:-------|:---------|:------|:-------------|:----------|:---------------|:--------------|:------------|:--------|:-------------------|:--------------|:----------------------------|:-------------------|:-------------|:--------------|:---------------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | 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 | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 13 |  |  |  |  |  | X | | | X | | X | | | X | | | X | | X | | X | | X | X | | | X | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 7 |  |  |  |  |  | X | | | X | | X | | | | | | X | | | | X | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 8 |  |  |  |  |  | X | | | X | | X | | | | | | X | | | | X | | | | | | X | | | | | | | | | | | | | | X | X | X | | X | | | | | X | X | X | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | X | | X | | | | | | X | | | | | | | | | | X | | | | | | | | | | | | | | X | | X | | | | | | | | | | | X | X | | X | | | X | X | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | | | X | | X | | | | | | X | | | | X | | | | | | X | | | | | | X | | | | | | | | X | X | X | | | | | | | X | X | X | X | X | X | | | X | | | | X | X | X | X | | | | | | | | | | | | | | | | |
| 8 | 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 | X | X | X | X | X |
|
CyberHarem/kousaka_reina_soundeuphonium | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Kousaka Reina/高坂麗奈 (Sound! Euphonium)
This is the dataset of Kousaka Reina/高坂麗奈 (Sound! Euphonium), containing 403 images and their tags.
The core tags of this character are `black_hair, long_hair, purple_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 | 403 | 271.59 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_reina_soundeuphonium/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 403 | 271.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_reina_soundeuphonium/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 780 | 466.85 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kousaka_reina_soundeuphonium/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/kousaka_reina_soundeuphonium',
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 | 6 |  |  |  |  |  | 1girl, blue_sailor_collar, blush, closed_mouth, kitauji_high_school_uniform, pink_neckerchief, serafuku, solo, white_shirt, upper_body, profile, short_sleeves, from_side |
| 1 | 6 |  |  |  |  |  | 1girl, kitauji_high_school_uniform, red_neckerchief, serafuku, solo, trumpet, playing_instrument, ponytail, holding_instrument, upper_body |
| 2 | 34 |  |  |  |  |  | 1girl, kitauji_high_school_uniform, serafuku, solo, red_neckerchief, brown_shirt, closed_mouth, white_sailor_collar, blush, upper_body, looking_at_viewer, long_sleeves, smile |
| 3 | 6 |  |  |  |  |  | 1girl, blush, closed_mouth, portrait, solo, blurry, kitauji_high_school_uniform, looking_at_viewer |
| 4 | 5 |  |  |  |  |  | 1girl, blush, close-up, closed_mouth, solo, looking_at_viewer, portrait |
| 5 | 7 |  |  |  |  |  | 1girl, brown_skirt, kitauji_high_school_uniform, pleated_skirt, red_neckerchief, solo, long_sleeves, sailor_collar, school_bag, brown_shirt, blush, brown_serafuku, standing |
| 6 | 8 |  |  |  |  |  | 2girls, blurry, blush, kitauji_high_school_uniform, serafuku, brown_hair, solo_focus, instrument, looking_at_another, sailor_collar, closed_mouth, neckerchief |
| 7 | 7 |  |  |  |  |  | 2girls, blush, looking_at_another, yuri, brown_hair, profile, close-up, closed_mouth, kitauji_high_school_uniform, blurry_background, from_side, short_hair |
| 8 | 14 |  |  |  |  |  | blue_kimono, yukata, blush, ponytail, 1girl, obi, solo, closed_mouth, hair_ornament |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_sailor_collar | blush | closed_mouth | kitauji_high_school_uniform | pink_neckerchief | serafuku | solo | white_shirt | upper_body | profile | short_sleeves | from_side | red_neckerchief | trumpet | playing_instrument | ponytail | holding_instrument | brown_shirt | white_sailor_collar | looking_at_viewer | long_sleeves | smile | portrait | blurry | close-up | brown_skirt | pleated_skirt | sailor_collar | school_bag | brown_serafuku | standing | 2girls | brown_hair | solo_focus | instrument | looking_at_another | neckerchief | yuri | blurry_background | short_hair | blue_kimono | yukata | obi | hair_ornament |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------------|:--------|:---------------|:------------------------------|:-------------------|:-----------|:-------|:--------------|:-------------|:----------|:----------------|:------------|:------------------|:----------|:---------------------|:-----------|:---------------------|:--------------|:----------------------|:--------------------|:---------------|:--------|:-----------|:---------|:-----------|:--------------|:----------------|:----------------|:-------------|:-----------------|:-----------|:---------|:-------------|:-------------|:-------------|:---------------------|:--------------|:-------|:--------------------|:-------------|:--------------|:---------|:------|:----------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | | | | X | | X | X | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 34 |  |  |  |  |  | X | | X | X | X | | X | X | | X | | | | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | X | X | | | X | | | | | | | | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | X | | | | X | | | | | | | | | | | | | X | | | X | | X | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | | X | | X | | | X | | | | | | X | | | | | X | | | X | | | | | X | X | X | X | X | X | | | | | | | | | | | | | |
| 6 | 8 |  |  |  |  |  | | | 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 | | | | |
| 8 | 14 |  |  |  |  |  | X | | X | X | | | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X |
|
Jing24/new_sorted_generate_sub_4 | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: conf
dtype: float32
splits:
- name: train
num_bytes: 42695739
num_examples: 46640
download_size: 7861758
dataset_size: 42695739
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
bubl-ai/williams_family_tree | ---
license: mit
---
The dataset was created using the code available at [bubl-ai's GitHub repository](https://github.com/bubl-ai/llamaindex-project/blob/main/builders/family_tree_synthetic_data/williams_family.py).
This synthetic dataset is about a fictional family, designed by us through the implementation of custom [Person and Family classes](https://github.com/bubl-ai/llamaindex-project/blob/main/bubls/bubls/synthetic_data/family_tree.py).
The dataset is organized into two distinct folders:
- Biographies: Within this folder, you'll find biographies generated using ChatGPT. These narratives are intricately woven based on our predefined family structure, created through the utilization of the Person and Family classes described above.
- Test Questions: In the 'test_questions' folder, we curate pairs of questions and answers derived from the biographies. This compilation serves as a valuable test dataset, enabling the evaluation of various Retrieval-Augmented Generative (RAG) configurations in future analyses.
|
CyberHarem/wicke_pokemon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of wicke/ビッケ (Pokémon)
This is the dataset of wicke/ビッケ (Pokémon), containing 500 images and their tags.
The core tags of this character are `breasts, glasses, purple_hair, green_eyes, pink-framed_eyewear, short_hair, big_hair, large_breasts, huge_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 | 500 | 446.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wicke_pokemon/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 270.17 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wicke_pokemon/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1127 | 537.60 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wicke_pokemon/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 402.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wicke_pokemon/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1127 | 734.92 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wicke_pokemon/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/wicke_pokemon',
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 | 9 |  |  |  |  |  | 1girl, navel, nipples, solo, nude, pussy, looking_at_viewer, smile, simple_background, white_background |
| 1 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, blush, smile, artist_name, cleavage, black_bikini, collarbone, mature_female, open_mouth |
| 2 | 14 |  |  |  |  |  | 1girl, pink_sweater, ribbed_sweater, simple_background, smile, solo, turtleneck_sweater, white_background, closed_mouth, long_sleeves, looking_at_viewer, upper_body, white_coat, blush, capelet |
| 3 | 5 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, pink_sweater, ribbed_sweater, simple_background, smile, solo, turtleneck_sweater, white_background, capelet, open_mouth |
| 4 | 16 |  |  |  |  |  | full_body, long_sleeves, pink_sweater, ribbed_sweater, turtleneck_sweater, capelet, looking_at_viewer, standing, thigh_boots, 1girl, smile, solo, white_skirt, white_footwear, closed_mouth, high_heel_boots, simple_background, hand_on_hip, white_background, white_coat |
| 5 | 6 |  |  |  |  |  | 1boy, 1girl, hetero, penis, ribbed_sweater, solo_focus, turtleneck_sweater, blush, ejaculation, mosaic_censoring, pink_sweater, sweater_lift, cum_on_breasts, nipples, paizuri, heart, open_mouth, simple_background, smile, tongue_out |
| 6 | 9 |  |  |  |  |  | 1boy, 1girl, hetero, penis, uncensored, sex_from_behind, open_mouth, ribbed_sweater, solo_focus, thighhighs, anal, artist_name, ass, blush, testicles, cum_in_pussy, high_heel_boots, pink_sweater, pokephilia, turtleneck_sweater, vaginal |
| 7 | 7 |  |  |  |  |  | 1girl, outdoors, blue_sky, day, ocean, solo, beach, cloud, navel, smile, collarbone, looking_at_viewer, one_eye_closed, pink_bikini, thighs |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | nipples | solo | nude | pussy | looking_at_viewer | smile | simple_background | white_background | blush | artist_name | cleavage | black_bikini | collarbone | mature_female | open_mouth | pink_sweater | ribbed_sweater | turtleneck_sweater | closed_mouth | long_sleeves | upper_body | white_coat | capelet | full_body | standing | thigh_boots | white_skirt | white_footwear | high_heel_boots | hand_on_hip | 1boy | hetero | penis | solo_focus | ejaculation | mosaic_censoring | sweater_lift | cum_on_breasts | paizuri | heart | tongue_out | uncensored | sex_from_behind | thighhighs | anal | ass | testicles | cum_in_pussy | pokephilia | vaginal | outdoors | blue_sky | day | ocean | beach | cloud | one_eye_closed | pink_bikini | thighs |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------|:-------|:-------|:--------|:--------------------|:--------|:--------------------|:-------------------|:--------|:--------------|:-----------|:---------------|:-------------|:----------------|:-------------|:---------------|:-----------------|:---------------------|:---------------|:---------------|:-------------|:-------------|:----------|:------------|:-----------|:--------------|:--------------|:-----------------|:------------------|:--------------|:-------|:---------|:--------|:-------------|:--------------|:-------------------|:---------------|:-----------------|:----------|:--------|:-------------|:-------------|:------------------|:-------------|:-------|:------|:------------|:---------------|:-------------|:----------|:-----------|:-----------|:------|:--------|:--------|:--------|:-----------------|:--------------|:---------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | | | X | X | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | | | X | | | X | X | X | X | X | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | | X | | | X | X | X | X | | | | | | | X | X | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 16 |  |  |  |  |  | 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 | X | | | | | | | | | | | | | | | | | | |
| 6 | 9 |  |  |  |  |  | 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 |
|
kehuitt/Fraud_News_Reports | ---
license: apache-2.0
---
|
zh-tw-llm-dv/zh-tw-pythia-ta8000-v1-e1-tr_sg-301-c1024-sbldt2 | ---
dataset_info:
dataset_size: 53808823.79506409
download_size: 15215886
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- dtype: string
name: preview
- dtype: int64
name: length
splits:
- name: train
num_bytes: 52893234.00506409
num_examples: 6133
- name: test
num_bytes: 915589.79
num_examples: 97
---
# zh-tw-pythia-ta8000-v1-e1-tr_sg-301-c1024-sbldt2
This dataset is a part of the `zh-tw-llm` project.
* Tokenizer: `zh-tw-pythia-tokenizer-a8000-v1`
* Built with: `sharegpt`
* Rows: `train` `6133`, `test` `97`
* Max length: `1024`
* Full config:
```json
{"build_with": ["sharegpt"], "preview_length": 128, "sort_by": "length-desc", "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "use_template": "random", "rows_limit": 300000, "test_size": 100, "test_split_seed": 42}, "sharegpt_settings": {"source_dataset": "zetavg/ShareGPT-Processed", "train_on_inputs": false, "languages": [{"en": 0.4}, "zh_Hant"], "rows_limit": 8000, "test_size": 0.02, "test_split_seed": 42, "test_rows_limit": 100}}
``` |
kanishka/counterfactual_babylm_aann_indef_naan | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 581833803
num_examples: 11632617
- name: validation
num_bytes: 56120230
num_examples: 1026747
download_size: 0
dataset_size: 637954033
---
# Dataset Card for "counterfactual_babylm_aann_indef_naan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilled-from-one-sec-cv12/chunk_65 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1210289824
num_examples: 235832
download_size: 1232671233
dataset_size: 1210289824
---
# Dataset Card for "chunk_65"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tj-solergibert/SRV-Europarl-ST-processed-mt-it | ---
dataset_info:
features:
- name: source_text
dtype: string
- name: dest_text
dtype: string
- name: dest_lang
dtype: string
splits:
- name: train
num_bytes: 121979892.4315265
num_examples: 504773
- name: valid
num_bytes: 15246425.496728532
num_examples: 67701
- name: test
num_bytes: 15677401.348182635
num_examples: 70814
download_size: 118670951
dataset_size: 152903719.27643767
---
# Dataset Card for "SRV-Europarl-ST-processed-mt-it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
leffff/south-park-character-png-dataset-old | ---
license: mit
---
|
AdapterOcean/med_alpaca_standardized_cluster_77 | ---
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: 73965064
num_examples: 7666
download_size: 21628323
dataset_size: 73965064
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_77"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mhzarem76/fintuned-llm | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: output
dtype: string
- name: instruction
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 18997844
num_examples: 51942
download_size: 11986973
dataset_size: 18997844
---
# Dataset Card for "fintuned-llm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tazarov/chroma-qna | ---
language: en
license: mit
size_categories:
- n<1K
pretty_name: Chroma export of collection N/A
dataset_info:
features:
- name: id
dtype: string
- name: document
dtype: string
- name: embedding
sequence: float32
splits:
- name: train
num_bytes: 150353
num_examples: 23
download_size: 207150
dataset_size: 150353
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
x-chroma:
description: Chroma Dataset
collection: N/A
metadata: N/A
---
|
Sunbird/salt-multispeaker-ach | ---
dataset_info:
features:
- name: ids
dtype: string
- name: texts
dtype: string
- name: audios
sequence: float32
- name: audio_languages
dtype: string
- name: are_studio
dtype: bool
- name: speaker_ids
dtype: string
- name: sample_rates
dtype: int64
splits:
- name: train
num_bytes: 1789772689
num_examples: 4811
- name: dev
num_bytes: 37429616
num_examples: 101
- name: test
num_bytes: 36224375
num_examples: 96
download_size: 872954526
dataset_size: 1863426680
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
|
UB-CVML-Group/PIE_Bench_pp | ---
license: cc-by-sa-4.0
dataset_info:
- config_name: 0_random_140
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: edit_action
dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 15594175.0
num_examples: 140
download_size: 15528907
dataset_size: 15594175.0
- config_name: 1_change_object_80
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: edit_action
dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 8188672.0
num_examples: 80
download_size: 8174209
dataset_size: 8188672.0
- config_name: 2_add_object_80
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: edit_action
dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 6926151.0
num_examples: 80
download_size: 6917854
dataset_size: 6926151.0
- config_name: 3_delete_object_80
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: edit_action
dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 7513741.0
num_examples: 80
download_size: 7382006
dataset_size: 7513741.0
- config_name: 4_change_attribute_content_40
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
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dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 4125034.0
num_examples: 40
download_size: 4061909
dataset_size: 4125034.0
- config_name: 5_change_attribute_pose_40
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
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dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 4217839.0
num_examples: 40
download_size: 4148577
dataset_size: 4217839.0
- config_name: 6_change_attribute_color_40
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
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dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 4274823.0
num_examples: 40
download_size: 4263550
dataset_size: 4274823.0
- config_name: 7_change_attribute_material_40
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
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dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 4061715.0
num_examples: 40
download_size: 4005557
dataset_size: 4061715.0
- config_name: 8_change_background_80
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: edit_action
dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 8533140.0
num_examples: 80
download_size: 8422137
dataset_size: 8533140.0
- config_name: 9_change_style_80
features:
- name: image
dtype: image
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: edit_action
dtype: string
- name: aspect_mapping
dtype: string
- name: blended_words
dtype: string
- name: mask
dtype: string
splits:
- name: V1
num_bytes: 8698695.0
num_examples: 80
download_size: 8686937
dataset_size: 8698695.0
configs:
- config_name: 0_random_140
data_files:
- split: V1
path: 0_random_140/V1-*
- config_name: 1_change_object_80
data_files:
- split: V1
path: 1_change_object_80/V1-*
- config_name: 2_add_object_80
data_files:
- split: V1
path: 2_add_object_80/V1-*
- config_name: 3_delete_object_80
data_files:
- split: V1
path: 3_delete_object_80/V1-*
- config_name: 4_change_attribute_content_40
data_files:
- split: V1
path: 4_change_attribute_content_40/V1-*
- config_name: 5_change_attribute_pose_40
data_files:
- split: V1
path: 5_change_attribute_pose_40/V1-*
- config_name: 6_change_attribute_color_40
data_files:
- split: V1
path: 6_change_attribute_color_40/V1-*
- config_name: 7_change_attribute_material_40
data_files:
- split: V1
path: 7_change_attribute_material_40/V1-*
- config_name: 8_change_background_80
data_files:
- split: V1
path: 8_change_background_80/V1-*
- config_name: 9_change_style_80
data_files:
- split: V1
path: 9_change_style_80/V1-*
---
## What is PIE-Bench++?
PIE-Bench++ builds upon the foundation laid by the original [PIE-Bench dataset](https://cure-lab.github.io/PnPInversion) introduced by (Ju et al., 2024), designed to provide a comprehensive benchmark for multi-aspect image editing evaluation. This enhanced dataset contains 700 images and prompts across nine distinct edit categories, encompassing a wide range of manipulations:
- **Object-Level Manipulations:** Additions, removals, and modifications of objects within the image.
- **Attribute-Level Manipulations:** Changes in content, pose, color, and material of objects.
- **Image-Level Manipulations:** Adjustments to the background and overall style of the image.
While retaining the original images, the enhanced dataset features revised source prompts and editing prompts, augmented with additional metadata such as editing types and aspect mapping. This comprehensive augmentation aims to facilitate more nuanced and detailed evaluations in the domain of multi-aspect image editing.
## Data Annotation Guide
### Overview
Our dataset annotations are structured to provide comprehensive information for each image, facilitating a deeper understanding of the editing process. Each annotation consists of the following key elements:
- **Source Prompt:** The original description or caption of the image before any edits are made.
- **Target Prompt:** The description or caption of the image after the edits are applied.
- **Edit Action:** A detailed specification of the changes made to the image, including:
- The position index in the source prompt where changes occur.
- The type of edit applied (e.g., 1: change object, 2: add object, 3: remove object, 4: change attribute content, 5: change attribute pose, 6: change attribute color, 7: change attribute material, 8: change background, 9: change style).
- The operation required to achieve the desired outcome (e.g., '+' / '-' means adding/removing words at the specified position, and 'xxx' means replacing the existing words).
- **Aspect Mapping:** A mapping that connects objects undergoing editing to their respective modified attributes. This helps identify which objects are subject to editing and the specific attributes that are altered.
### Example Annotation
Here is an example annotation for an image in our dataset:
```json
{
"000000000002": {
"image_path": "0_random_140/000000000002.jpg",
"source_prompt": "a cat sitting on a wooden chair",
"target_prompt": "a [red] [dog] [with flowers in mouth] [standing] on a [metal] chair",
"edit_action": {
"red": {"position": 1, "edit_type": 6, "action": "+"},
"dog": {"position": 1, "edit_type": 1, "action": "cat"},
"with flowers in mouth": {"position": 2, "edit_type": 2, "action": "+"},
"standing": {"position": 2, "edit_type": 5, "action": "sitting"},
"metal": {"position": 5, "edit_type": 7, "action": "wooden"}
},
"aspect_mapping": {
"dog": ["red", "standing"],
"chair": ["metal"],
"flowers": []
},
"blended_words": [
"cat,dog",
"chair,chair"
],
"mask": "0 262144"
}
}
|
tr416/dataset_20231007_025908 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 762696.0
num_examples: 297
- name: test
num_bytes: 7704.0
num_examples: 3
download_size: 74279
dataset_size: 770400.0
---
# Dataset Card for "dataset_20231007_025908"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BOP-Berlin-University-Alliance/dc_elements_raw_data | ---
license: gpl-3.0
task_categories:
- text-classification
language:
- en
size_categories:
- n<1K
---
The dataset consists of the descriptions and comments about the concepts in Dublin Core ontology elements. |
hink00/sd-runpod | ---
license: wtfpl
---
|
reciprocate/dpo_ultra-capybara-code_filtered-best | ---
dataset_info:
features:
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 192320966
num_examples: 35232
download_size: 99821013
dataset_size: 192320966
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
zolak/twitter_dataset_1712998923 | ---
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: 2930263
num_examples: 7191
download_size: 1462291
dataset_size: 2930263
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Vaxxzin/Ivan | ---
license: apache-2.0
---
|
CyberHarem/chacha_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of chacha/茶々/茶茶 (Fate/Grand Order)
This is the dataset of chacha/茶々/茶茶 (Fate/Grand Order), containing 145 images and their tags.
The core tags of this character are `brown_hair, long_hair, brown_eyes, hat, hairband, black_headwear, parted_bangs`, 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 | 145 | 156.67 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chacha_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 145 | 144.18 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chacha_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 284 | 249.97 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chacha_fgo/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/chacha_fgo',
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 | 11 |  |  |  |  |  | 1girl, kikumon, looking_at_viewer, mitsudomoe_(shape), solo, black_gloves, black_dress, black_pantyhose, blush, floral_print, bow, fur-trimmed_gloves, grin, sash, white_background, simple_background, black_capelet, japanese_clothes, open_mouth, ribbon, standing |
| 1 | 8 |  |  |  |  |  | 1girl, gloves, solo, flaming_sword, katana, kikumon, looking_at_viewer, mitsudomoe_(shape), fire, holding_sword, black_capelet, black_pantyhose, dress, floral_print, fur-trimmed_capelet, sash, closed_mouth, open_mouth, standing, :d, hand_on_own_hip, pink_ribbon, very_long_hair |
| 2 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, smile, solo, closed_mouth, ribbon, kimono, simple_background, upper_body |
| 3 | 10 |  |  |  |  |  | 2girls, black_bikini, open_mouth, navel, blush, sarong, small_breasts, looking_at_viewer, :d, barefoot, sparkle, very_long_hair |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | kikumon | looking_at_viewer | mitsudomoe_(shape) | solo | black_gloves | black_dress | black_pantyhose | blush | floral_print | bow | fur-trimmed_gloves | grin | sash | white_background | simple_background | black_capelet | japanese_clothes | open_mouth | ribbon | standing | gloves | flaming_sword | katana | fire | holding_sword | dress | fur-trimmed_capelet | closed_mouth | :d | hand_on_own_hip | pink_ribbon | very_long_hair | smile | kimono | upper_body | 2girls | black_bikini | navel | sarong | small_breasts | barefoot | sparkle |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:----------|:--------------------|:---------------------|:-------|:---------------|:--------------|:------------------|:--------|:---------------|:------|:---------------------|:-------|:-------|:-------------------|:--------------------|:----------------|:-------------------|:-------------|:---------|:-----------|:---------|:----------------|:---------|:-------|:----------------|:--------|:----------------------|:---------------|:-----|:------------------|:--------------|:-----------------|:--------|:---------|:-------------|:---------|:---------------|:--------|:---------|:----------------|:-----------|:----------|
| 0 | 11 |  |  |  |  |  | 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 | X | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | X | | X | | | | | | | | | | | X | | | | X | | | | | | | | | X | | | | | X | X | X | | | | | | | |
| 3 | 10 |  |  |  |  |  | | | X | | | | | | X | | | | | | | | | | X | | | | | | | | | | | X | | | X | | | | X | X | X | X | X | X | X |
|
HiTZ/cometa | ---
license: apache-2.0
task_categories:
- token-classification
language:
- es
pretty_name: CoMeta
size_categories:
- 1K<n<10K
---
# 🪁 CoMeta
<!-- Provide a quick summary of the dataset. -->
CoMeta is a manually annotated dataset corpus for metaphor detection in Spanish consisting of 3633 sentences of texts of multiple domains. We believe that CoMeta is the largest publicly available dataset with metaphorical annotations in texts of general domain for the Spanish language.
- **Repository:** Code and dataset in tabulated format available at https://github.com/ixa-ehu/cometa
- **Paper:** [Leveraging a New Spanish Corpus for Multilingual and Cross-lingual Metaphor Detection](https://aclanthology.org/2022.conll-1.16/)
## Dataset Structure
- **tokens:** list of text split.
- **tags:** list of metaphor annotations for each token.
- 0: literal
- 1: metaphor
## 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 CoMeta, please cite our work:
```
@inproceedings{sanchez-bayona-agerri-2022-leveraging,
title = "Leveraging a New {S}panish Corpus for Multilingual and Cross-lingual Metaphor Detection",
author = "Sanchez-Bayona, Elisa and
Agerri, Rodrigo",
editor = "Fokkens, Antske and
Srikumar, Vivek",
booktitle = "Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.conll-1.16",
doi = "10.18653/v1/2022.conll-1.16",
pages = "228--240",
abstract = "The lack of wide coverage datasets annotated with everyday metaphorical expressions for languages other than English is striking. This means that most research on supervised metaphor detection has been published only for that language. In order to address this issue, this work presents the first corpus annotated with naturally occurring metaphors in Spanish large enough to develop systems to perform metaphor detection. The presented dataset, CoMeta, includes texts from various domains, namely, news, political discourse, Wikipedia and reviews. In order to label CoMeta, we apply the MIPVU method, the guidelines most commonly used to systematically annotate metaphor on real data. We use our newly created dataset to provide competitive baselines by fine-tuning several multilingual and monolingual state-of-the-art large language models. Furthermore, by leveraging the existing VUAM English data in addition to CoMeta, we present the, to the best of our knowledge, first cross-lingual experiments on supervised metaphor detection. Finally, we perform a detailed error analysis that explores the seemingly high transfer of everyday metaphor across these two languages and datasets.",
}
```
## Dataset Card Contact
{elisa.sanchez, rodrigo.agerri}@ehu.eus |
patomp/thai-mscoco-2014-captions | ---
dataset_info:
features:
- name: image
dtype: image
- name: filepath
dtype: string
- name: sentids
list: int32
- name: filename
dtype: string
- name: imgid
dtype: int32
- name: split
dtype: string
- name: sentences_tokens
list:
list: string
- name: sentences_raw
list: string
- name: sentences_sentid
list: int32
- name: cocoid
dtype: int32
- name: th_sentences_raw
sequence: string
splits:
- name: test
num_bytes: 819234726.0
num_examples: 5000
- name: validation
num_bytes: 807387321.0
num_examples: 5000
- name: train
num_bytes: 18882795327.165
num_examples: 113287
download_size: 20158273111
dataset_size: 20509417374.165
---
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("patomp/thai-mscoco-2014-captions")
dataset
```
output
```python
DatasetDict({
train: Dataset({
features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
num_rows: 113287
})
validation: Dataset({
features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
num_rows: 5000
})
test: Dataset({
features: ['image', 'filepath', 'sentids', 'filename', 'imgid', 'split', 'sentences_tokens', 'sentences_raw', 'sentences_sentid', 'cocoid', 'th_sentences_raw'],
num_rows: 5000
})
})
```
A sample
```python
dataset["validation"][0]
```
output
```python
{
"image":<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x336 at 0x7F6C5A83F430>,
"filepath":"COCO_val2014_000000184613.jpg",
"sentids":[474921,479322,479334,481560,483594],
"filename":"COCO_val2014_000000184613.jpg",
"imgid":2,
"split":"val",
"sentences_tokens":[
["a", "child","holding", "a","flowered","umbrella","and","petting","a","yak"],["a","young","man","holding","an","umbrella","next","to","a","herd","of","cattle"],
["a","young","boy","barefoot","holding","an","umbrella","touching","the","horn","of","a","cow"],
["a","young","boy","with","an","umbrella","who","is","touching","the","horn","of","a","cow"],
["a","boy","holding","an","umbrella","while","standing","next","to","livestock"]
],
"sentences_raw":[
"A child holding a flowered umbrella and petting a yak.",
"A young man holding an umbrella next to a herd of cattle.",
"a young boy barefoot holding an umbrella touching the horn of a cow",
"A young boy with an umbrella who is touching the horn of a cow.",
"A boy holding an umbrella while standing next to livestock."
],
"sentences_sentid":[474921,479322,479334,481560,483594],
"cocoid":184613,
"th_sentences_raw":[
"เด็กถือร่มที่มีดอกหนึ่งคันและลูบคลูบลํา",
"ชายหนุ่มคนหนึ่งถือร่มไว้ข้างๆ ฝูงวัว",
"เด็กหนุ่มคนหนึ่งเท้าเปล่าจับร่มจับแตรของวัว",
"เด็กชายที่มีร่มสัมผัสแตรของวัว",
"เด็กชายถือร่มในขณะที่ยืนถัดจากปศุสัตว์"
]
}
```
## Dataset Construction
The dataset contructed from translating the captions of [MS COCO 2014 dataset](https://huggingface.co/datasets/HuggingFaceM4/COCO) [1] to Thai by using [NMT](https://airesearch.in.th/releases/machine-translation-models/) provided by VISTEC-depa Thailand Artificial Intelligence Research Institute [2]. The translated of 3 splits (train, validation and test) dataset was published in the [Huggingface](https://huggingface.co/datasets/patomp/thai-mscoco-2014-captions).
## References
[1] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. 2014. Microsoft COCO: Common Objects in Context. In Computer Vision – ECCV 2014, Springer International Publishing, Cham, 740–755.
[2] English-Thai Machine Translation Models. (2020, June 23). VISTEC-depa Thailand Artificial Intelligence Research Institute. https://airesearch.in.th/releases/machine-translation-models/ |
lsmathh/pokedata | ---
task_categories:
- question-answering
language:
- en
pretty_name: p
--- |
carlicode/violence_context | ---
license: other
---
|
open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qk_rank14_v2 | ---
pretty_name: Evaluation run of xxyyy123/10k_v1_lora_qk_rank14_v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [xxyyy123/10k_v1_lora_qk_rank14_v2](https://huggingface.co/xxyyy123/10k_v1_lora_qk_rank14_v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qk_rank14_v2\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-03T15:46:18.274387](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qk_rank14_v2/blob/main/results_2023-09-03T15%3A46%3A18.274387.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\": 0.5170296348361414,\n\
\ \"acc_stderr\": 0.03493290232216538,\n \"acc_norm\": 0.5207737377982975,\n\
\ \"acc_norm_stderr\": 0.034916919339016556,\n \"mc1\": 0.3574051407588739,\n\
\ \"mc1_stderr\": 0.016776599676729398,\n \"mc2\": 0.5241397415740128,\n\
\ \"mc2_stderr\": 0.0157002252598079\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5298634812286689,\n \"acc_stderr\": 0.014585305840007105,\n\
\ \"acc_norm\": 0.5648464163822525,\n \"acc_norm_stderr\": 0.014487986197186043\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6100378410675165,\n\
\ \"acc_stderr\": 0.004867445945277159,\n \"acc_norm\": 0.7959569806811392,\n\
\ \"acc_norm_stderr\": 0.004021769582317863\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542128,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542128\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.4962962962962963,\n\
\ \"acc_stderr\": 0.04319223625811331,\n \"acc_norm\": 0.4962962962962963,\n\
\ \"acc_norm_stderr\": 0.04319223625811331\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.48026315789473684,\n \"acc_stderr\": 0.040657710025626036,\n\
\ \"acc_norm\": 0.48026315789473684,\n \"acc_norm_stderr\": 0.040657710025626036\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.47,\n\
\ \"acc_stderr\": 0.05016135580465919,\n \"acc_norm\": 0.47,\n \
\ \"acc_norm_stderr\": 0.05016135580465919\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\": 0.5486111111111112,\n\
\ \"acc_stderr\": 0.041614023984032786,\n \"acc_norm\": 0.5486111111111112,\n\
\ \"acc_norm_stderr\": 0.041614023984032786\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.4797687861271676,\n\
\ \"acc_stderr\": 0.03809342081273957,\n \"acc_norm\": 0.4797687861271676,\n\
\ \"acc_norm_stderr\": 0.03809342081273957\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929775,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929775\n\
\ },\n \"harness|hendrycksTest-computer_security|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-conceptual_physics|5\"\
: {\n \"acc\": 0.4595744680851064,\n \"acc_stderr\": 0.032579014820998356,\n\
\ \"acc_norm\": 0.4595744680851064,\n \"acc_norm_stderr\": 0.032579014820998356\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\": 0.4413793103448276,\n \"acc_stderr\": 0.04137931034482758,\n\
\ \"acc_norm\": 0.4413793103448276,\n \"acc_norm_stderr\": 0.04137931034482758\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2962962962962963,\n \"acc_stderr\": 0.023517294335963286,\n \"\
acc_norm\": 0.2962962962962963,\n \"acc_norm_stderr\": 0.023517294335963286\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.0404061017820884,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.0404061017820884\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.5709677419354838,\n\
\ \"acc_stderr\": 0.028156036538233193,\n \"acc_norm\": 0.5709677419354838,\n\
\ \"acc_norm_stderr\": 0.028156036538233193\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.3694581280788177,\n \"acc_stderr\": 0.03395970381998573,\n\
\ \"acc_norm\": 0.3694581280788177,\n \"acc_norm_stderr\": 0.03395970381998573\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \"acc_norm\"\
: 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7090909090909091,\n \"acc_stderr\": 0.03546563019624336,\n\
\ \"acc_norm\": 0.7090909090909091,\n \"acc_norm_stderr\": 0.03546563019624336\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6818181818181818,\n \"acc_stderr\": 0.033184773338453294,\n \"\
acc_norm\": 0.6818181818181818,\n \"acc_norm_stderr\": 0.033184773338453294\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.7409326424870466,\n \"acc_stderr\": 0.03161877917935413,\n\
\ \"acc_norm\": 0.7409326424870466,\n \"acc_norm_stderr\": 0.03161877917935413\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.4897435897435897,\n \"acc_stderr\": 0.025345672221942374,\n\
\ \"acc_norm\": 0.4897435897435897,\n \"acc_norm_stderr\": 0.025345672221942374\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.25555555555555554,\n \"acc_stderr\": 0.026593939101844075,\n \
\ \"acc_norm\": 0.25555555555555554,\n \"acc_norm_stderr\": 0.026593939101844075\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5084033613445378,\n \"acc_stderr\": 0.0324739027656967,\n \
\ \"acc_norm\": 0.5084033613445378,\n \"acc_norm_stderr\": 0.0324739027656967\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3443708609271523,\n \"acc_stderr\": 0.038796870240733264,\n \"\
acc_norm\": 0.3443708609271523,\n \"acc_norm_stderr\": 0.038796870240733264\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.728440366972477,\n \"acc_stderr\": 0.01906909836319144,\n \"acc_norm\"\
: 0.728440366972477,\n \"acc_norm_stderr\": 0.01906909836319144\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.375,\n\
\ \"acc_stderr\": 0.033016908987210894,\n \"acc_norm\": 0.375,\n \
\ \"acc_norm_stderr\": 0.033016908987210894\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.6862745098039216,\n \"acc_stderr\": 0.032566854844603886,\n\
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.032566854844603886\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598035,\n \
\ \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598035\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5964125560538116,\n\
\ \"acc_stderr\": 0.03292802819330314,\n \"acc_norm\": 0.5964125560538116,\n\
\ \"acc_norm_stderr\": 0.03292802819330314\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.5954198473282443,\n \"acc_stderr\": 0.043046937953806645,\n\
\ \"acc_norm\": 0.5954198473282443,\n \"acc_norm_stderr\": 0.043046937953806645\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.6528925619834711,\n \"acc_stderr\": 0.04345724570292534,\n \"\
acc_norm\": 0.6528925619834711,\n \"acc_norm_stderr\": 0.04345724570292534\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.5950920245398773,\n \"acc_stderr\": 0.03856672163548914,\n\
\ \"acc_norm\": 0.5950920245398773,\n \"acc_norm_stderr\": 0.03856672163548914\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\
\ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\
\ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7087378640776699,\n \"acc_stderr\": 0.04498676320572924,\n\
\ \"acc_norm\": 0.7087378640776699,\n \"acc_norm_stderr\": 0.04498676320572924\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.782051282051282,\n\
\ \"acc_stderr\": 0.02704685763071669,\n \"acc_norm\": 0.782051282051282,\n\
\ \"acc_norm_stderr\": 0.02704685763071669\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.57,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.57,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7088122605363985,\n\
\ \"acc_stderr\": 0.016246087069701407,\n \"acc_norm\": 0.7088122605363985,\n\
\ \"acc_norm_stderr\": 0.016246087069701407\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.5664739884393064,\n \"acc_stderr\": 0.026680134761679214,\n\
\ \"acc_norm\": 0.5664739884393064,\n \"acc_norm_stderr\": 0.026680134761679214\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2681564245810056,\n\
\ \"acc_stderr\": 0.014816119635317003,\n \"acc_norm\": 0.2681564245810056,\n\
\ \"acc_norm_stderr\": 0.014816119635317003\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.5392156862745098,\n \"acc_stderr\": 0.028541722692618874,\n\
\ \"acc_norm\": 0.5392156862745098,\n \"acc_norm_stderr\": 0.028541722692618874\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.5819935691318328,\n\
\ \"acc_stderr\": 0.028013651891995072,\n \"acc_norm\": 0.5819935691318328,\n\
\ \"acc_norm_stderr\": 0.028013651891995072\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.5555555555555556,\n \"acc_stderr\": 0.027648477877413324,\n\
\ \"acc_norm\": 0.5555555555555556,\n \"acc_norm_stderr\": 0.027648477877413324\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3723404255319149,\n \"acc_stderr\": 0.028838921471251458,\n \
\ \"acc_norm\": 0.3723404255319149,\n \"acc_norm_stderr\": 0.028838921471251458\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.38396349413298564,\n\
\ \"acc_stderr\": 0.01242158783313423,\n \"acc_norm\": 0.38396349413298564,\n\
\ \"acc_norm_stderr\": 0.01242158783313423\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4852941176470588,\n \"acc_stderr\": 0.03035969707904611,\n\
\ \"acc_norm\": 0.4852941176470588,\n \"acc_norm_stderr\": 0.03035969707904611\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.4869281045751634,\n \"acc_stderr\": 0.020220920829626912,\n \
\ \"acc_norm\": 0.4869281045751634,\n \"acc_norm_stderr\": 0.020220920829626912\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6,\n\
\ \"acc_stderr\": 0.0469237132203465,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.0469237132203465\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6081632653061224,\n \"acc_stderr\": 0.03125127591089165,\n\
\ \"acc_norm\": 0.6081632653061224,\n \"acc_norm_stderr\": 0.03125127591089165\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.6019900497512438,\n\
\ \"acc_stderr\": 0.03461199429040013,\n \"acc_norm\": 0.6019900497512438,\n\
\ \"acc_norm_stderr\": 0.03461199429040013\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \
\ \"acc_norm\": 0.76,\n \"acc_norm_stderr\": 0.042923469599092816\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.42771084337349397,\n\
\ \"acc_stderr\": 0.03851597683718534,\n \"acc_norm\": 0.42771084337349397,\n\
\ \"acc_norm_stderr\": 0.03851597683718534\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.695906432748538,\n \"acc_stderr\": 0.03528211258245229,\n\
\ \"acc_norm\": 0.695906432748538,\n \"acc_norm_stderr\": 0.03528211258245229\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3574051407588739,\n\
\ \"mc1_stderr\": 0.016776599676729398,\n \"mc2\": 0.5241397415740128,\n\
\ \"mc2_stderr\": 0.0157002252598079\n }\n}\n```"
repo_url: https://huggingface.co/xxyyy123/10k_v1_lora_qk_rank14_v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|arc:challenge|25_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hellaswag|10_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T15:46:18.274387.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T15:46:18.274387.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T15:46:18.274387.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T15:46:18.274387.parquet'
- config_name: results
data_files:
- split: 2023_09_03T15_46_18.274387
path:
- results_2023-09-03T15:46:18.274387.parquet
- split: latest
path:
- results_2023-09-03T15:46:18.274387.parquet
---
# Dataset Card for Evaluation run of xxyyy123/10k_v1_lora_qk_rank14_v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/xxyyy123/10k_v1_lora_qk_rank14_v2
- **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 [xxyyy123/10k_v1_lora_qk_rank14_v2](https://huggingface.co/xxyyy123/10k_v1_lora_qk_rank14_v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qk_rank14_v2",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-03T15:46:18.274387](https://huggingface.co/datasets/open-llm-leaderboard/details_xxyyy123__10k_v1_lora_qk_rank14_v2/blob/main/results_2023-09-03T15%3A46%3A18.274387.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": 0.5170296348361414,
"acc_stderr": 0.03493290232216538,
"acc_norm": 0.5207737377982975,
"acc_norm_stderr": 0.034916919339016556,
"mc1": 0.3574051407588739,
"mc1_stderr": 0.016776599676729398,
"mc2": 0.5241397415740128,
"mc2_stderr": 0.0157002252598079
},
"harness|arc:challenge|25": {
"acc": 0.5298634812286689,
"acc_stderr": 0.014585305840007105,
"acc_norm": 0.5648464163822525,
"acc_norm_stderr": 0.014487986197186043
},
"harness|hellaswag|10": {
"acc": 0.6100378410675165,
"acc_stderr": 0.004867445945277159,
"acc_norm": 0.7959569806811392,
"acc_norm_stderr": 0.004021769582317863
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.4962962962962963,
"acc_stderr": 0.04319223625811331,
"acc_norm": 0.4962962962962963,
"acc_norm_stderr": 0.04319223625811331
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.48026315789473684,
"acc_stderr": 0.040657710025626036,
"acc_norm": 0.48026315789473684,
"acc_norm_stderr": 0.040657710025626036
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.47,
"acc_stderr": 0.05016135580465919,
"acc_norm": 0.47,
"acc_norm_stderr": 0.05016135580465919
},
"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": 0.5486111111111112,
"acc_stderr": 0.041614023984032786,
"acc_norm": 0.5486111111111112,
"acc_norm_stderr": 0.041614023984032786
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.4797687861271676,
"acc_stderr": 0.03809342081273957,
"acc_norm": 0.4797687861271676,
"acc_norm_stderr": 0.03809342081273957
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.29411764705882354,
"acc_stderr": 0.04533838195929775,
"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929775
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.4595744680851064,
"acc_stderr": 0.032579014820998356,
"acc_norm": 0.4595744680851064,
"acc_norm_stderr": 0.032579014820998356
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.34210526315789475,
"acc_stderr": 0.04462917535336936,
"acc_norm": 0.34210526315789475,
"acc_norm_stderr": 0.04462917535336936
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.4413793103448276,
"acc_stderr": 0.04137931034482758,
"acc_norm": 0.4413793103448276,
"acc_norm_stderr": 0.04137931034482758
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2962962962962963,
"acc_stderr": 0.023517294335963286,
"acc_norm": 0.2962962962962963,
"acc_norm_stderr": 0.023517294335963286
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.0404061017820884,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.0404061017820884
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.5709677419354838,
"acc_stderr": 0.028156036538233193,
"acc_norm": 0.5709677419354838,
"acc_norm_stderr": 0.028156036538233193
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.3694581280788177,
"acc_stderr": 0.03395970381998573,
"acc_norm": 0.3694581280788177,
"acc_norm_stderr": 0.03395970381998573
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7090909090909091,
"acc_stderr": 0.03546563019624336,
"acc_norm": 0.7090909090909091,
"acc_norm_stderr": 0.03546563019624336
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.033184773338453294,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.033184773338453294
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.7409326424870466,
"acc_stderr": 0.03161877917935413,
"acc_norm": 0.7409326424870466,
"acc_norm_stderr": 0.03161877917935413
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.4897435897435897,
"acc_stderr": 0.025345672221942374,
"acc_norm": 0.4897435897435897,
"acc_norm_stderr": 0.025345672221942374
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.25555555555555554,
"acc_stderr": 0.026593939101844075,
"acc_norm": 0.25555555555555554,
"acc_norm_stderr": 0.026593939101844075
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5084033613445378,
"acc_stderr": 0.0324739027656967,
"acc_norm": 0.5084033613445378,
"acc_norm_stderr": 0.0324739027656967
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3443708609271523,
"acc_stderr": 0.038796870240733264,
"acc_norm": 0.3443708609271523,
"acc_norm_stderr": 0.038796870240733264
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.728440366972477,
"acc_stderr": 0.01906909836319144,
"acc_norm": 0.728440366972477,
"acc_norm_stderr": 0.01906909836319144
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.375,
"acc_stderr": 0.033016908987210894,
"acc_norm": 0.375,
"acc_norm_stderr": 0.033016908987210894
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.6862745098039216,
"acc_stderr": 0.032566854844603886,
"acc_norm": 0.6862745098039216,
"acc_norm_stderr": 0.032566854844603886
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7257383966244726,
"acc_stderr": 0.029041333510598035,
"acc_norm": 0.7257383966244726,
"acc_norm_stderr": 0.029041333510598035
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.5964125560538116,
"acc_stderr": 0.03292802819330314,
"acc_norm": 0.5964125560538116,
"acc_norm_stderr": 0.03292802819330314
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.5954198473282443,
"acc_stderr": 0.043046937953806645,
"acc_norm": 0.5954198473282443,
"acc_norm_stderr": 0.043046937953806645
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.6528925619834711,
"acc_stderr": 0.04345724570292534,
"acc_norm": 0.6528925619834711,
"acc_norm_stderr": 0.04345724570292534
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.6851851851851852,
"acc_stderr": 0.04489931073591312,
"acc_norm": 0.6851851851851852,
"acc_norm_stderr": 0.04489931073591312
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.5950920245398773,
"acc_stderr": 0.03856672163548914,
"acc_norm": 0.5950920245398773,
"acc_norm_stderr": 0.03856672163548914
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.39285714285714285,
"acc_stderr": 0.04635550135609976,
"acc_norm": 0.39285714285714285,
"acc_norm_stderr": 0.04635550135609976
},
"harness|hendrycksTest-management|5": {
"acc": 0.7087378640776699,
"acc_stderr": 0.04498676320572924,
"acc_norm": 0.7087378640776699,
"acc_norm_stderr": 0.04498676320572924
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.782051282051282,
"acc_stderr": 0.02704685763071669,
"acc_norm": 0.782051282051282,
"acc_norm_stderr": 0.02704685763071669
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.57,
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"acc_norm": 0.57,
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"harness|hendrycksTest-nutrition|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6081632653061224,
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"acc_norm": 0.6081632653061224,
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},
"harness|hendrycksTest-sociology|5": {
"acc": 0.6019900497512438,
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"acc_norm": 0.6019900497512438,
"acc_norm_stderr": 0.03461199429040013
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-virology|5": {
"acc": 0.42771084337349397,
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"acc_norm": 0.42771084337349397,
"acc_norm_stderr": 0.03851597683718534
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.695906432748538,
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"acc_norm": 0.695906432748538,
"acc_norm_stderr": 0.03528211258245229
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3574051407588739,
"mc1_stderr": 0.016776599676729398,
"mc2": 0.5241397415740128,
"mc2_stderr": 0.0157002252598079
}
}
```
### 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] |
qfrodicio/gesture-prediction-9-classes | ---
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: sentence
dtype: string
- name: gestures
sequence: string
splits:
- name: train
num_bytes: 360893
num_examples: 1392
- name: test
num_bytes: 96706
num_examples: 354
- name: validation
num_bytes: 120010
num_examples: 449
download_size: 173305
dataset_size: 577609
---
# Dataset Card for "gesture-prediction-9-classes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
khpbvo/TestDutchdataset | ---
license: apache-2.0
---
|
lingtrain/minor-prince | ---
dataset_info:
features:
- name: ba
dtype: string
- name: cv
dtype: string
- name: di
dtype: string
- name: krc
dtype: string
- name: kv
dtype: string
- name: mdf
dtype: string
- name: mrh
dtype: string
- name: mrj
dtype: string
- name: myv
dtype: string
- name: ru
dtype: string
- name: sah
dtype: string
- name: tt
dtype: string
splits:
- name: train
num_bytes: 1859606
num_examples: 1229
download_size: 922687
dataset_size: 1859606
---
# Dataset Card for "minor-prince"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
lemeswmv/conradovoz | ---
license: openrail
---
|
Nandikaa08/datamapping | ---
license: apache-2.0
---
|
fujiki/japanese_hh-rlhf-49k | ---
license: mit
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: index
dtype: string
splits:
- name: train
num_bytes: 34168978
num_examples: 49332
download_size: 18427777
dataset_size: 34168978
language:
- ja
---
- This is a little bit different version of [`kunishou/hh-rlhf-49k-ja`](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) without `ng_translation == 1` examples.
- Please also refer to the original dataset [`kunishou/hh-rlhf-49k-ja`](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja). |
liuyanchen1015/MULTI_VALUE_stsb_null_relcl | ---
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: 30916
num_examples: 144
- name: test
num_bytes: 15172
num_examples: 78
- name: train
num_bytes: 55050
num_examples: 223
download_size: 78454
dataset_size: 101138
---
# Dataset Card for "MULTI_VALUE_stsb_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zobairhasanmns/player-loras | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: additional_feature
dtype: string
splits:
- name: train
num_bytes: 2189297.0
num_examples: 23
download_size: 2136452
dataset_size: 2189297.0
---
# Dataset Card for "player-loras"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
discovery | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license: apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: discovery
pretty_name: Discovery
tags:
- discourse-marker-prediction
dataset_info:
- config_name: discovery
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '[no-conn]'
'1': absolutely,
'2': accordingly
'3': actually,
'4': additionally
'5': admittedly,
'6': afterward
'7': again,
'8': already,
'9': also,
'10': alternately,
'11': alternatively
'12': although,
'13': altogether,
'14': amazingly,
'15': and
'16': anyway,
'17': apparently,
'18': arguably,
'19': as_a_result,
'20': basically,
'21': because_of_that
'22': because_of_this
'23': besides,
'24': but
'25': by_comparison,
'26': by_contrast,
'27': by_doing_this,
'28': by_then
'29': certainly,
'30': clearly,
'31': coincidentally,
'32': collectively,
'33': consequently
'34': conversely
'35': curiously,
'36': currently,
'37': elsewhere,
'38': especially,
'39': essentially,
'40': eventually,
'41': evidently,
'42': finally,
'43': first,
'44': firstly,
'45': for_example
'46': for_instance
'47': fortunately,
'48': frankly,
'49': frequently,
'50': further,
'51': furthermore
'52': generally,
'53': gradually,
'54': happily,
'55': hence,
'56': here,
'57': historically,
'58': honestly,
'59': hopefully,
'60': however
'61': ideally,
'62': immediately,
'63': importantly,
'64': in_contrast,
'65': in_fact,
'66': in_other_words
'67': in_particular,
'68': in_short,
'69': in_sum,
'70': in_the_end,
'71': in_the_meantime,
'72': in_turn,
'73': incidentally,
'74': increasingly,
'75': indeed,
'76': inevitably,
'77': initially,
'78': instead,
'79': interestingly,
'80': ironically,
'81': lastly,
'82': lately,
'83': later,
'84': likewise,
'85': locally,
'86': luckily,
'87': maybe,
'88': meaning,
'89': meantime,
'90': meanwhile,
'91': moreover
'92': mostly,
'93': namely,
'94': nationally,
'95': naturally,
'96': nevertheless
'97': next,
'98': nonetheless
'99': normally,
'100': notably,
'101': now,
'102': obviously,
'103': occasionally,
'104': oddly,
'105': often,
'106': on_the_contrary,
'107': on_the_other_hand
'108': once,
'109': only,
'110': optionally,
'111': or,
'112': originally,
'113': otherwise,
'114': overall,
'115': particularly,
'116': perhaps,
'117': personally,
'118': plus,
'119': preferably,
'120': presently,
'121': presumably,
'122': previously,
'123': probably,
'124': rather,
'125': realistically,
'126': really,
'127': recently,
'128': regardless,
'129': remarkably,
'130': sadly,
'131': second,
'132': secondly,
'133': separately,
'134': seriously,
'135': significantly,
'136': similarly,
'137': simultaneously
'138': slowly,
'139': so,
'140': sometimes,
'141': soon,
'142': specifically,
'143': still,
'144': strangely,
'145': subsequently,
'146': suddenly,
'147': supposedly,
'148': surely,
'149': surprisingly,
'150': technically,
'151': thankfully,
'152': then,
'153': theoretically,
'154': thereafter,
'155': thereby,
'156': therefore
'157': third,
'158': thirdly,
'159': this,
'160': though,
'161': thus,
'162': together,
'163': traditionally,
'164': truly,
'165': truthfully,
'166': typically,
'167': ultimately,
'168': undoubtedly,
'169': unfortunately,
'170': unsurprisingly,
'171': usually,
'172': well,
'173': yet,
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 334809726
num_examples: 1566000
- name: validation
num_bytes: 18607661
num_examples: 87000
- name: test
num_bytes: 18615474
num_examples: 87000
download_size: 146233621
dataset_size: 372032861
- config_name: discoverysmall
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype:
class_label:
names:
'0': '[no-conn]'
'1': absolutely,
'2': accordingly
'3': actually,
'4': additionally
'5': admittedly,
'6': afterward
'7': again,
'8': already,
'9': also,
'10': alternately,
'11': alternatively
'12': although,
'13': altogether,
'14': amazingly,
'15': and
'16': anyway,
'17': apparently,
'18': arguably,
'19': as_a_result,
'20': basically,
'21': because_of_that
'22': because_of_this
'23': besides,
'24': but
'25': by_comparison,
'26': by_contrast,
'27': by_doing_this,
'28': by_then
'29': certainly,
'30': clearly,
'31': coincidentally,
'32': collectively,
'33': consequently
'34': conversely
'35': curiously,
'36': currently,
'37': elsewhere,
'38': especially,
'39': essentially,
'40': eventually,
'41': evidently,
'42': finally,
'43': first,
'44': firstly,
'45': for_example
'46': for_instance
'47': fortunately,
'48': frankly,
'49': frequently,
'50': further,
'51': furthermore
'52': generally,
'53': gradually,
'54': happily,
'55': hence,
'56': here,
'57': historically,
'58': honestly,
'59': hopefully,
'60': however
'61': ideally,
'62': immediately,
'63': importantly,
'64': in_contrast,
'65': in_fact,
'66': in_other_words
'67': in_particular,
'68': in_short,
'69': in_sum,
'70': in_the_end,
'71': in_the_meantime,
'72': in_turn,
'73': incidentally,
'74': increasingly,
'75': indeed,
'76': inevitably,
'77': initially,
'78': instead,
'79': interestingly,
'80': ironically,
'81': lastly,
'82': lately,
'83': later,
'84': likewise,
'85': locally,
'86': luckily,
'87': maybe,
'88': meaning,
'89': meantime,
'90': meanwhile,
'91': moreover
'92': mostly,
'93': namely,
'94': nationally,
'95': naturally,
'96': nevertheless
'97': next,
'98': nonetheless
'99': normally,
'100': notably,
'101': now,
'102': obviously,
'103': occasionally,
'104': oddly,
'105': often,
'106': on_the_contrary,
'107': on_the_other_hand
'108': once,
'109': only,
'110': optionally,
'111': or,
'112': originally,
'113': otherwise,
'114': overall,
'115': particularly,
'116': perhaps,
'117': personally,
'118': plus,
'119': preferably,
'120': presently,
'121': presumably,
'122': previously,
'123': probably,
'124': rather,
'125': realistically,
'126': really,
'127': recently,
'128': regardless,
'129': remarkably,
'130': sadly,
'131': second,
'132': secondly,
'133': separately,
'134': seriously,
'135': significantly,
'136': similarly,
'137': simultaneously
'138': slowly,
'139': so,
'140': sometimes,
'141': soon,
'142': specifically,
'143': still,
'144': strangely,
'145': subsequently,
'146': suddenly,
'147': supposedly,
'148': surely,
'149': surprisingly,
'150': technically,
'151': thankfully,
'152': then,
'153': theoretically,
'154': thereafter,
'155': thereby,
'156': therefore
'157': third,
'158': thirdly,
'159': this,
'160': though,
'161': thus,
'162': together,
'163': traditionally,
'164': truly,
'165': truthfully,
'166': typically,
'167': ultimately,
'168': undoubtedly,
'169': unfortunately,
'170': unsurprisingly,
'171': usually,
'172': well,
'173': yet,
- name: idx
dtype: int32
splits:
- name: train
num_bytes: 3355192
num_examples: 15662
- name: validation
num_bytes: 185296
num_examples: 871
- name: test
num_bytes: 187471
num_examples: 869
download_size: 146233621
dataset_size: 3727959
train-eval-index:
- config: discovery
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
- config: discoverysmall
task: text-classification
task_id: multi-class-classification
splits:
train_split: train
eval_split: validation
col_mapping:
sentence1: text1
sentence2: text2
label: target
config_names:
- discovery
- discoverysmall
---
# Dataset Card for Discovery
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/sileod/Discovery
- **Repository:** https://github.com/sileod/Discovery
- **Paper:** https://www.aclweb.org/anthology/N19-1351/
- **Leaderboard:**
- **Point of Contact:** damien.sileo at inria.fr
### Dataset Summary
Discourse marker prediction with 174 markers
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
input : sentence1, sentence2,
label: marker originally between sentence1 and sentence2
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Train/Val/Test
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Aranea english web corpus
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Self supervised (see paper)
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1351",
pages = "3477--3486",
abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
}
```
### Contributions
Thanks to [@sileod](https://github.com/sileod) for adding this dataset. |
him1411/EDGAR10-Q | ---
license: mit
tags:
- financial
- ner
- context-ner
size_categories:
- 1M<n<10M
---
# EDGAR10-Q
## Dataset Summary
EDGAR10-Q is a large financial dataset curated by scraping annual and quarterly reports of top 1500 LLCs in the world.
The dataset is designed for the task of ContextNER, which aims to generate the relevant context for entities in a sentence, where the context is a set of phrases describing the entity but not necessarily present in the sentence.
The dataset is the largest in terms of the number of sentences (1M), entities (2.8M), and average tokens per sentence (35).
You may want to check out
* Our paper: [CONTEXT-NER: Contextual Phrase Generation at Scale](https://arxiv.org/abs/2109.08079/)
* GitHub: [Click Here](https://github.com/him1411/edgar10q-dataset)
## Supported Tasks
The dataset is designed for the task of ContextNER that aims to generate the relevant context for entities in a sentence,
where the context is a set of phrases describing the entity but not necessarily present in the sentence.
## Dataset Structure
### Data Instances
The dataset includes plain text input-output pairs, where the input is a sentence with an entity and the output is the context for the entity.
An example of a train instance looks as follows:
```
{
"input": "0.6 million . The denominator also includes the dilutive effect of approximately 0.9 million, 0.6 million and 0.6 million shares of unvested restricted shares of common stock for the years ended December 31, 2019, 2018 and 2017, respectively.",
"output": "Dilutive effect of unvested restricted shares of Class A common stock"
}
```
We also publish a metadata file in the original repository to promote future research in the area. Please checkout the [main website](https://github.com/him1411/edgar10q-dataset)
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` in the form of entity plus sentence.
- `label`: a string describing the relevant context for entity in the sentence
### Data Splits
The dataset is split into train, validation, and test sets. The sizes of the splits are as follows:
| | Train | Validation | Test |
|-----------|-----------|------------|-------|
| Instances | 1,498,995 | 187,383 |187,383|
### Dataset Creation
The dataset was created by scraping annual and quarterly reports of top 1500 LLCs in the world.
### Models trained using this dataset
There are several models finetuned using this dataset. They are:
1. [EDGAR-T5-base](https://huggingface.co/him1411/EDGAR-T5-base)
2. [EDGAR-BART-Base](https://huggingface.co/him1411/EDGAR-BART-Base)
3. [EDGAR-flan-t5-base](https://huggingface.co/him1411/EDGAR-flan-t5-base)
4. [EDGAR-T5-Large](https://huggingface.co/him1411/EDGAR-T5-Large)
5. [EDGAR-Tk-Instruct-Large](https://huggingface.co/him1411/EDGAR-Tk-Instruct-Large)
6. [Instruction tuned EDGAR-Tk-Instruct-base](https://huggingface.co/him1411/EDGAR-Tk-instruct-base-inst-tune)
### Citation Information
If you use this dataset and any other related artifact, please cite the following paper:
```
@article{gupta2021context,
title={Context-NER: Contextual Phrase Generation at Scale},
author={Gupta, Himanshu and Verma, Shreyas and Kumar, Tarun and Mishra, Swaroop and Agrawal, Tamanna and Badugu, Amogh and Bhatt, Himanshu Sharad},
journal={arXiv preprint arXiv:2109.08079},
year={2021}
}
```
### Contributions
Thanks to [@him1411](https://github.com/him1411) for adding this dataset. |
dkshjn/mixqa_v0.2 | ---
dataset_info:
features:
- name: question
dtype: string
- name: optionsKey
dtype: string
- name: prompt
dtype: string
- name: gold
dtype: string
splits:
- name: train
num_bytes: 373803.0598068066
num_examples: 500
download_size: 239182
dataset_size: 373803.0598068066
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "mixqa_v0.2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
inria-soda/tabular-benchmark |
---
annotations_creators: []
license: []
pretty_name: tabular_benchmark
tags: []
task_categories:
- tabular-classification
- tabular-regression
configs:
- config_name: clf_cat_albert
data_files: clf_cat/albert.csv
- config_name: clf_cat_compas-two-years
data_files: clf_cat/compas-two-years.csv
- config_name: clf_cat_covertype
data_files: clf_cat/covertype.csv
- config_name: clf_cat_default-of-credit-card-clients
data_files: clf_cat/default-of-credit-card-clients.csv
- config_name: clf_cat_electricity
data_files: clf_cat/electricity.csv
- config_name: clf_cat_eye_movements
data_files: clf_cat/eye_movements.csv
- config_name: clf_cat_road-safety
data_files: clf_cat/road-safety.csv
- config_name: clf_num_Bioresponse
data_files: clf_num/Bioresponse.csv
- config_name: clf_num_Diabetes130US
data_files: clf_num/Diabetes130US.csv
- config_name: clf_num_Higgs
data_files: clf_num/Higgs.csv
- config_name: clf_num_MagicTelescope
data_files: clf_num/MagicTelescope.csv
- config_name: clf_num_MiniBooNE
data_files: clf_num/MiniBooNE.csv
- config_name: clf_num_bank-marketing
data_files: clf_num/bank-marketing.csv
- config_name: clf_num_california
data_files: clf_num/california.csv
- config_name: clf_num_covertype
data_files: clf_num/covertype.csv
- config_name: clf_num_credit
data_files: clf_num/credit.csv
- config_name: clf_num_default-of-credit-card-clients
data_files: clf_num/default-of-credit-card-clients.csv
- config_name: clf_num_electricity
data_files: clf_num/electricity.csv
- config_name: clf_num_eye_movements
data_files: clf_num/eye_movements.csv
- config_name: clf_num_heloc
data_files: clf_num/heloc.csv
- config_name: clf_num_house_16H
data_files: clf_num/house_16H.csv
- config_name: clf_num_jannis
data_files: clf_num/jannis.csv
- config_name: clf_num_pol
data_files: clf_num/pol.csv
- config_name: reg_cat_Airlines_DepDelay_1M
data_files: reg_cat/Airlines_DepDelay_1M.csv
- config_name: reg_cat_Allstate_Claims_Severity
data_files: reg_cat/Allstate_Claims_Severity.csv
- config_name: reg_cat_Bike_Sharing_Demand
data_files: reg_cat/Bike_Sharing_Demand.csv
- config_name: reg_cat_Brazilian_houses
data_files: reg_cat/Brazilian_houses.csv
- config_name: reg_cat_Mercedes_Benz_Greener_Manufacturing
data_files: reg_cat/Mercedes_Benz_Greener_Manufacturing.csv
- config_name: reg_cat_SGEMM_GPU_kernel_performance
data_files: reg_cat/SGEMM_GPU_kernel_performance.csv
- config_name: reg_cat_abalone
data_files: reg_cat/abalone.csv
- config_name: reg_cat_analcatdata_supreme
data_files: reg_cat/analcatdata_supreme.csv
- config_name: reg_cat_delays_zurich_transport
data_files: reg_cat/delays_zurich_transport.csv
- config_name: reg_cat_diamonds
data_files: reg_cat/diamonds.csv
- config_name: reg_cat_house_sales
data_files: reg_cat/house_sales.csv
- config_name: reg_cat_medical_charges
data_files: reg_cat/medical_charges.csv
- config_name: reg_cat_nyc-taxi-green-dec-2016
data_files: reg_cat/nyc-taxi-green-dec-2016.csv
- config_name: reg_cat_particulate-matter-ukair-2017
data_files: reg_cat/particulate-matter-ukair-2017.csv
- config_name: reg_cat_seattlecrime6
data_files: reg_cat/seattlecrime6.csv
- config_name: reg_cat_topo_2_1
data_files: reg_cat/topo_2_1.csv
- config_name: reg_cat_visualizing_soil
data_files: reg_cat/visualizing_soil.csv
- config_name: reg_num_Ailerons
data_files: reg_num/Ailerons.csv
- config_name: reg_num_Bike_Sharing_Demand
data_files: reg_num/Bike_Sharing_Demand.csv
- config_name: reg_num_Brazilian_houses
data_files: reg_num/Brazilian_houses.csv
- config_name: reg_num_MiamiHousing2016
data_files: reg_num/MiamiHousing2016.csv
- config_name: reg_num_abalone
data_files: reg_num/abalone.csv
- config_name: reg_num_cpu_act
data_files: reg_num/cpu_act.csv
- config_name: reg_num_delays_zurich_transport
data_files: reg_num/delays_zurich_transport.csv
- config_name: reg_num_diamonds
data_files: reg_num/diamonds.csv
- config_name: reg_num_elevators
data_files: reg_num/elevators.csv
- config_name: reg_num_house_16H
data_files: reg_num/house_16H.csv
- config_name: reg_num_house_sales
data_files: reg_num/house_sales.csv
- config_name: reg_num_houses
data_files: reg_num/houses.csv
- config_name: reg_num_medical_charges
data_files: reg_num/medical_charges.csv
- config_name: reg_num_nyc-taxi-green-dec-2016
data_files: reg_num/nyc-taxi-green-dec-2016.csv
- config_name: reg_num_pol
data_files: reg_num/pol.csv
- config_name: reg_num_sulfur
data_files: reg_num/sulfur.csv
- config_name: reg_num_superconduct
data_files: reg_num/superconduct.csv
- config_name: reg_num_wine_quality
data_files: reg_num/wine_quality.csv
- config_name: reg_num_yprop_4_1
data_files: reg_num/yprop_4_1.csv
---
# Tabular Benchmark
## Dataset Description
This dataset is a curation of various datasets from [openML](https://www.openml.org/) and is curated to benchmark performance of various machine learning algorithms.
- **Repository:** https://github.com/LeoGrin/tabular-benchmark/community
- **Paper:** https://hal.archives-ouvertes.fr/hal-03723551v2/document
### Dataset Summary
Benchmark made of curation of various tabular data learning tasks, including:
- Regression from Numerical and Categorical Features
- Regression from Numerical Features
- Classification from Numerical and Categorical Features
- Classification from Numerical Features
### Supported Tasks and Leaderboards
- `tabular-regression`
- `tabular-classification`
## Dataset Structure
### Data Splits
This dataset consists of four splits (folders) based on tasks and datasets included in tasks.
- reg_num: Task identifier for regression on numerical features.
- reg_cat: Task identifier for regression on numerical and categorical features.
- clf_num: Task identifier for classification on numerical features.
- clf_cat: Task identifier for classification on categorical features.
Depending on the dataset you want to load, you can load the dataset by passing `task_name/dataset_name` to `data_files` argument of `load_dataset` like below:
```python
from datasets import load_dataset
dataset = load_dataset("inria-soda/tabular-benchmark", data_files="reg_cat/house_sales.csv")
```
## Dataset Creation
### Curation Rationale
This dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below:
- **Heterogeneous columns**. Columns should correspond to features of different nature. This excludes
images or signal datasets where each column corresponds to the same signal on different sensors.
- **Not high dimensional**. We only keep datasets with a d/n ratio below 1/10.
- **Undocumented datasets** We remove datasets where too little information is available. We did keep
datasets with hidden column names if it was clear that the features were heterogeneous.
- **I.I.D. data**. We remove stream-like datasets or time series.
- **Real-world data**. We remove artificial datasets but keep some simulated datasets. The difference is
subtle, but we try to keep simulated datasets if learning these datasets are of practical importance
(like the Higgs dataset), and not just a toy example to test specific model capabilities.
- **Not too small**. We remove datasets with too few features (< 4) and too few samples (< 3 000). For
benchmarks on numerical features only, we remove categorical features before checking if enough
features and samples are remaining.
- **Not too easy**. We remove datasets which are too easy. Specifically, we remove a dataset if a simple model (max of a single tree and a regression, logistic or OLS)
reaches a score whose relative difference with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn)
is below 5%. Other benchmarks use different metrics to remove too easy datasets, like removing datasets perfectly separated by a single decision classifier [Bischl et al., 2021],
but this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014],
a close score for the simple and powerful models suggests that we are already close to the best achievable score.
- **Not deterministic**. We remove datasets where the target is a deterministic function of the data. This
mostly means removing datasets on games like poker and chess. Indeed, we believe that these
datasets are very different from most real-world tabular datasets, and should be studied separately
### Source Data
**Numerical Classification**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|electricity|38474.0|7.0|https://www.openml.org/d/151|https://www.openml.org/d/44120|
|covertype|566602.0|10.0|https://www.openml.org/d/293|https://www.openml.org/d/44121|
|pol|10082.0|26.0|https://www.openml.org/d/722|https://www.openml.org/d/44122|
|house_16H|13488.0|16.0|https://www.openml.org/d/821|https://www.openml.org/d/44123|
|MagicTelescope|13376.0|10.0|https://www.openml.org/d/1120|https://www.openml.org/d/44125|
|bank-marketing|10578.0|7.0|https://www.openml.org/d/1461|https://www.openml.org/d/44126|
|Bioresponse|3434.0|419.0|https://www.openml.org/d/4134|https://www.openml.org/d/45019|
|MiniBooNE|72998.0|50.0|https://www.openml.org/d/41150|https://www.openml.org/d/44128|
|default-of-credit-card-clients|13272.0|20.0|https://www.openml.org/d/42477|https://www.openml.org/d/45020|
|Higgs|940160.0|24.0|https://www.openml.org/d/42769|https://www.openml.org/d/44129|
|eye_movements|7608.0|20.0|https://www.openml.org/d/1044|https://www.openml.org/d/44130|
|Diabetes130US|71090.0|7.0|https://www.openml.org/d/4541|https://www.openml.org/d/45022|
|jannis|57580.0|54.0|https://www.openml.org/d/41168|https://www.openml.org/d/45021|
|heloc|10000.0|22.0|"https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc?select=heloc_dataset_v1+%281%29.csv"|https://www.openml.org/d/45026|
|credit|16714.0|10.0|"https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv"|https://www.openml.org/d/44089|
|california|20634.0|8.0|"https://www.dcc.fc.up.pt/ltorgo/Regression/cal_housing.html"|https://www.openml.org/d/45028|
**Categorical Classification**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|electricity|38474.0|8.0|https://www.openml.org/d/151|https://www.openml.org/d/44156|
|eye_movements|7608.0|23.0|https://www.openml.org/d/1044|https://www.openml.org/d/44157|
|covertype|423680.0|54.0|https://www.openml.org/d/1596|https://www.openml.org/d/44159|
|albert|58252.0|31.0|https://www.openml.org/d/41147|https://www.openml.org/d/45035|
|compas-two-years|4966.0|11.0|https://www.openml.org/d/42192|https://www.openml.org/d/45039|
|default-of-credit-card-clients|13272.0|21.0|https://www.openml.org/d/42477|https://www.openml.org/d/45036|
|road-safety|111762.0|32.0|https://www.openml.org/d/42803|https://www.openml.org/d/45038|
**Numerical Regression**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|cpu_act|8192.0|21.0|https://www.openml.org/d/197|https://www.openml.org/d/44132|
|pol|15000.0|26.0|https://www.openml.org/d/201|https://www.openml.org/d/44133|
|elevators|16599.0|16.0|https://www.openml.org/d/216|https://www.openml.org/d/44134|
|wine_quality|6497.0|11.0|https://www.openml.org/d/287|https://www.openml.org/d/44136|
|Ailerons|13750.0|33.0|https://www.openml.org/d/296|https://www.openml.org/d/44137|
|yprop_4_1|8885.0|42.0|https://www.openml.org/d/416|https://www.openml.org/d/45032|
|houses|20640.0|8.0|https://www.openml.org/d/537|https://www.openml.org/d/44138|
|house_16H|22784.0|16.0|https://www.openml.org/d/574|https://www.openml.org/d/44139|
|delays_zurich_transport|5465575.0|9.0|https://www.openml.org/d/40753|https://www.openml.org/d/45034|
|diamonds|53940.0|6.0|https://www.openml.org/d/42225|https://www.openml.org/d/44140|
|Brazilian_houses|10692.0|8.0|https://www.openml.org/d/42688|https://www.openml.org/d/44141|
|Bike_Sharing_Demand|17379.0|6.0|https://www.openml.org/d/42712|https://www.openml.org/d/44142|
|nyc-taxi-green-dec-2016|581835.0|9.0|https://www.openml.org/d/42729|https://www.openml.org/d/44143|
|house_sales|21613.0|15.0|https://www.openml.org/d/42731|https://www.openml.org/d/44144|
|sulfur|10081.0|6.0|https://www.openml.org/d/23515|https://www.openml.org/d/44145|
|medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/44146|
|MiamiHousing2016|13932.0|14.0|https://www.openml.org/d/43093|https://www.openml.org/d/44147|
|superconduct|21263.0|79.0|https://www.openml.org/d/43174|https://www.openml.org/d/44148|
**Categorical Regression**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|topo_2_1|8885.0|255.0|https://www.openml.org/d/422|https://www.openml.org/d/45041|
|analcatdata_supreme|4052.0|7.0|https://www.openml.org/d/504|https://www.openml.org/d/44055|
|visualizing_soil|8641.0|4.0|https://www.openml.org/d/688|https://www.openml.org/d/44056|
|delays_zurich_transport|5465575.0|12.0|https://www.openml.org/d/40753|https://www.openml.org/d/45045|
|diamonds|53940.0|9.0|https://www.openml.org/d/42225|https://www.openml.org/d/44059|
|Allstate_Claims_Severity|188318.0|124.0|https://www.openml.org/d/42571|https://www.openml.org/d/45046|
|Mercedes_Benz_Greener_Manufacturing|4209.0|359.0|https://www.openml.org/d/42570|https://www.openml.org/d/44061|
|Brazilian_houses|10692.0|11.0|https://www.openml.org/d/42688|https://www.openml.org/d/44062|
|Bike_Sharing_Demand|17379.0|11.0|https://www.openml.org/d/42712|https://www.openml.org/d/44063|
|Airlines_DepDelay_1M|1000000.0|5.0|https://www.openml.org/d/42721|https://www.openml.org/d/45047|
|nyc-taxi-green-dec-2016|581835.0|16.0|https://www.openml.org/d/42729|https://www.openml.org/d/44065|
|abalone|4177.0|8.0|https://www.openml.org/d/42726|https://www.openml.org/d/45042|
|house_sales|21613.0|17.0|https://www.openml.org/d/42731|https://www.openml.org/d/44066|
|seattlecrime6|52031.0|4.0|https://www.openml.org/d/42496|https://www.openml.org/d/45043|
|medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/45048|
|particulate-matter-ukair-2017|394299.0|6.0|https://www.openml.org/d/42207|https://www.openml.org/d/44068|
|SGEMM_GPU_kernel_performance|241600.0|9.0|https://www.openml.org/d/43144|https://www.openml.org/d/44069|
### Dataset Curators
Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux.
### Licensing Information
[More Information Needed]
### Citation Information
Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep
learning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New
Orleans, United States. ffhal-03723551v2f
|
scfengv/TVL_Game_Layer_topics | ---
task_categories:
- text-classification
language:
- zh
--- |
zzzzhhh/LLaMa-zn | ---
license: apache-2.0
---
|
qgallouedec/prj_gia_dataset_metaworld_coffee_button_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the coffee-button-v2 environment, sample for the policy coffee-button-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_coffee_button_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_coffee_button_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
MrDre/autotrain-data-feetfoot | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: feetfoot
## Dataset Description
This dataset has been automatically processed by AutoTrain for project feetfoot.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<180x320 RGB PIL image>",
"target": 0
},
{
"image": "<78x320 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['gettyimagefeet'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 97 |
| valid | 25 |
|
lhallee/abstract_domain_cvd | ---
dataset_info:
features:
- name: a
dtype: string
- name: b
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 685896937
num_examples: 181000
- name: valid
num_bytes: 17346151
num_examples: 4584
- name: test
num_bytes: 2872780
num_examples: 753
download_size: 208705249
dataset_size: 706115868
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
This dataset contains the cocitation abstracts related to CVD in the paper [Contrastive Learning and Mixture of Experts Enables Precise Vector Embeddings](arxiv.org/abs/2401.15713)
|
hatakeyama-llm-team/nhk-pages-metadata | ---
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: pubDate
dtype: string
- name: cate
dtype: string
- name: cate_group
sequence: string
- name: link
dtype: string
- name: imgPath
dtype: string
- name: iconPath
dtype: string
- name: videoPath
dtype: string
- name: videoDuration
dtype: string
- name: relationNews
sequence: 'null'
splits:
- name: train
num_bytes: 69515464
num_examples: 168872
download_size: 20763247
dataset_size: 69515464
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
mask-distilled-one-sec-cv12/chunk_192 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1327352008
num_examples: 260674
download_size: 1353032021
dataset_size: 1327352008
---
# Dataset Card for "chunk_192"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kiriyamaX/Nurburgring-J | ---
license: bigscience-openrail-m
---
# Dataset Card for Nurburgring-J
## Dataset Description
- **Homepage:** [NurburgringJ Dataset Homepage](https://huggingface.co/kiriyamaX)
- **Repository:** [NurburgringJ Dataset Repository](https://huggingface.co/datasets/kiriyamaX/Nurburgring-J)
- **Paper:** NurburgringJ: A Dataset for Fine-Grained Vehicle Classification and Traffic Flow Analysis (to be published soon)
- **Point of Contact:** [NurburgringJ POC](mailto:nurburgringj@protonmail.com)
|
CyberHarem/perth_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of perth/パース (Kantai Collection)
This is the dataset of perth/パース (Kantai Collection), containing 433 images and their tags.
The core tags of this character are `blonde_hair, braid, purple_eyes, hair_bun, braided_bun, braided_bangs, breasts, short_hair`, 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 | 433 | 477.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perth_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 433 | 283.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perth_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1043 | 627.29 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perth_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 433 | 427.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perth_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1043 | 865.05 MiB | [Download](https://huggingface.co/datasets/CyberHarem/perth_kantaicollection/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/perth_kantaicollection',
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 | 19 |  |  |  |  |  | 1girl, blue_necktie, green_vest, short_sleeves, solo, upper_body, white_shirt, dress_shirt, green_cape, cloak, looking_at_viewer, badge, hair_ribbon, school_uniform, simple_background, white_background, hair_between_eyes |
| 1 | 54 |  |  |  |  |  | 1girl, blue_necktie, blue_skirt, green_vest, plaid_skirt, pleated_skirt, school_uniform, white_shirt, dress_shirt, green_cape, short_sleeves, solo, badge, green_thighhighs, cloak, looking_at_viewer, cowboy_shot, white_background, simple_background |
| 2 | 7 |  |  |  |  |  | 1girl, alternate_costume, fake_animal_ears, playboy_bunny, rabbit_ears, solo, wrist_cuffs, detached_collar, strapless_leotard, cowboy_shot, large_breasts, rabbit_tail, cleavage, fake_tail, pantyhose, green_leotard, looking_at_viewer, necktie |
| 3 | 33 |  |  |  |  |  | 1girl, solo, white_hoodie, blue_skirt, hooded_sweater, long_sleeves, plaid_skirt, official_alternate_costume, pink_apron, pleated_skirt, looking_at_viewer, simple_background, white_background, blush, long_hair, thighhighs |
| 4 | 10 |  |  |  |  |  | 1girl, official_alternate_costume, solo, striped_bikini, simple_background, white_background, cowboy_shot, medium_breasts, navel, looking_at_viewer, blush, cleavage, hairclip, one-hour_drawing_challenge, sarong, collarbone, green_bikini, twitter_username |
| 5 | 5 |  |  |  |  |  | 1girl, cleavage, navel, outdoors, solo, striped_bikini, blue_sky, cloud, cowboy_shot, day, medium_breasts, blush, hairclip, looking_at_viewer, official_alternate_costume, collarbone, large_breasts, ocean, sarong, smile |
| 6 | 10 |  |  |  |  |  | 1girl, enmaided, frilled_apron, solo, white_apron, maid_headdress, black_dress, maid_apron, short_sleeves, blush, puffy_sleeves, waist_apron, cowboy_shot, large_breasts, looking_at_viewer, gloves, one-hour_drawing_challenge, simple_background |
| 7 | 6 |  |  |  |  |  | 1girl, blush, nude, solo_focus, 1boy, hair_ribbon, hetero, large_breasts, bangs, nipples, penis, simple_background, bar_censor, fellatio, hair_between_eyes, open_mouth, white_background |
| 8 | 5 |  |  |  |  |  | 1girl, green_kimono, print_kimono, alternate_costume, floral_print, long_hair, obi, wide_sleeves, blush, hair_flower, long_sleeves, food, hair_between_eyes, holding, large_breasts, open_mouth, solo_focus |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_necktie | green_vest | short_sleeves | solo | upper_body | white_shirt | dress_shirt | green_cape | cloak | looking_at_viewer | badge | hair_ribbon | school_uniform | simple_background | white_background | hair_between_eyes | blue_skirt | plaid_skirt | pleated_skirt | green_thighhighs | cowboy_shot | alternate_costume | fake_animal_ears | playboy_bunny | rabbit_ears | wrist_cuffs | detached_collar | strapless_leotard | large_breasts | rabbit_tail | cleavage | fake_tail | pantyhose | green_leotard | necktie | white_hoodie | hooded_sweater | long_sleeves | official_alternate_costume | pink_apron | blush | long_hair | thighhighs | striped_bikini | medium_breasts | navel | hairclip | one-hour_drawing_challenge | sarong | collarbone | green_bikini | twitter_username | outdoors | blue_sky | cloud | day | ocean | smile | enmaided | frilled_apron | white_apron | maid_headdress | black_dress | maid_apron | puffy_sleeves | waist_apron | gloves | nude | solo_focus | 1boy | hetero | bangs | nipples | penis | bar_censor | fellatio | open_mouth | green_kimono | print_kimono | floral_print | obi | wide_sleeves | hair_flower | food | holding |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-------------|:----------------|:-------|:-------------|:--------------|:--------------|:-------------|:--------|:--------------------|:--------|:--------------|:-----------------|:--------------------|:-------------------|:--------------------|:-------------|:--------------|:----------------|:-------------------|:--------------|:--------------------|:-------------------|:----------------|:--------------|:--------------|:------------------|:--------------------|:----------------|:--------------|:-----------|:------------|:------------|:----------------|:----------|:---------------|:-----------------|:---------------|:-----------------------------|:-------------|:--------|:------------|:-------------|:-----------------|:-----------------|:--------|:-----------|:-----------------------------|:---------|:-------------|:---------------|:-------------------|:-----------|:-----------|:--------|:------|:--------|:--------|:-----------|:----------------|:--------------|:-----------------|:--------------|:-------------|:----------------|:--------------|:---------|:-------|:-------------|:-------|:---------|:--------|:----------|:--------|:-------------|:-----------|:-------------|:---------------|:---------------|:---------------|:------|:---------------|:--------------|:-------|:----------|
| 0 | 19 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 54 |  |  |  |  |  | X | X | X | X | X | | X | X | X | X | X | X | | X | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | | | X | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 33 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | X | | | | | | X | | | | | | | | | | | X | | | | | | | | X | | X | | | | | | | | X | | X | | | X | X | X | X | | X | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 10 |  |  |  |  |  | X | | | X | X | | | | | | X | | | | X | | | | | | | X | | | | | | | | X | | | | | | | | | | | | X | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | |
| 7 | 6 |  |  |  |  |  | X | | | | | | | | | | | | X | | X | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | | | | | | | | | | | | | | | X | | | | | | X | | | | | | | X | | | | | | | | | X | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X |
|
japanese-asr/whisper_transcriptions.reazonspeech.all_41 | ---
dataset_info:
config_name: all
features:
- name: name
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: transcription
dtype: string
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 30428751523.0
num_examples: 267712
download_size: 30190505775
dataset_size: 30428751523.0
configs:
- config_name: all
data_files:
- split: train
path: all/train-*
---
|
mgp123/datascience-stackexchange-with-similar-questions | ---
dataset_info:
features:
- name: Id
dtype: string
- name: PostTypeId
dtype: string
- name: AcceptedAnswerId
dtype: string
- name: ParentId
dtype: string
- name: Score
dtype: string
- name: ViewCount
dtype: string
- name: Body
dtype: string
- name: Title
dtype: string
- name: ContentLicense
dtype: string
- name: FavoriteCount
dtype: string
- name: CreationDate
dtype: string
- name: LastActivityDate
dtype: string
- name: LastEditDate
dtype: string
- name: LastEditorUserId
dtype: string
- name: OwnerUserId
dtype: string
- name: Tags
sequence: string
- name: Answer
dtype: string
- name: SimilarQuestion
dtype: string
- name: SimilarQuestionAnswer
dtype: string
splits:
- name: train
num_bytes: 32869719
num_examples: 9172
download_size: 17840780
dataset_size: 32869719
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This dataset is a filtered version of heblackcat102/datascience-stackexchange-posts filtered to include only "data-science" related answers and paired by Question-Answer-Similar Question-Similar Answer
|
AdityaNG/BengaluruSemanticOccupancyDataset | ---
license: mit
tags:
- video
- driving
- Bengaluru
- disparity maps
- depth dataset
homepage: https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/
---
# Bengaluru Semantic Occupancy Dataset
<img src="https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/index_files/BDD_Iterator_Demo-2023-08-30_08.25.17.gif" >
## Dataset Summary
We gathered a dataset spanning 114 minutes and 165K frames in Bengaluru, India. Our dataset consists of video data from a calibrated camera sensor with a resolution of 1920×1080 recorded at a framerate of 30 Hz. We utilize a Depth Dataset Generation pipeline that only uses videos as input to produce high-resolution disparity maps.
- Dataset Iterator: https://github.com/AdityaNG/bdd_dataset_iterator
- Project Page: https://adityang.github.io/AdityaNG/BengaluruDrivingDataset/
- Dataset Download: https://huggingface.co/datasets/AdityaNG/BengaluruSemanticOccupancyDataset
## Paper
[Bengaluru Driving Dataset: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios](https://arxiv.org/abs/2307.10934)
## Citation
```bibtex
@misc{analgund2023octran,
title={Bengaluru Driving Dataset: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarios},
author={Ganesh, Aditya N and Pobbathi Badrinath, Dhruval and
Kumar, Harshith Mohan and S, Priya and Narayan, Surabhi
},
year={2023},
howpublished={Spotlight Presentation at the Transformers for Vision Workshop, CVPR},
url={https://sites.google.com/view/t4v-cvpr23/papers#h.enx3bt45p649},
note={Transformers for Vision Workshop, CVPR 2023}
} |
CyberHarem/chihaya_kisaragi_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of chihaya_kisaragi/如月千早/如月千早 (Azur Lane)
This is the dataset of chihaya_kisaragi/如月千早/如月千早 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `long_hair, blue_hair, brown_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 | 484.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihaya_kisaragi_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 335.73 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihaya_kisaragi_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1104 | 654.06 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihaya_kisaragi_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 446.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihaya_kisaragi_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1104 | 834.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/chihaya_kisaragi_azurlane/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/chihaya_kisaragi_azurlane',
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 | 16 |  |  |  |  |  | 1girl, solo, blush, smile, looking_at_viewer |
| 1 | 6 |  |  |  |  |  | 1girl, school_uniform, skirt, solo, smile, blazer, necktie, pantyhose |
| 2 | 11 |  |  |  |  |  | 1girl, belt, midriff, navel, solo, open_mouth, necklace, wrist_cuffs, skirt, smile, blush, hand_on_own_chest, cross |
| 3 | 20 |  |  |  |  |  | 1girl, shiny_hair, solo, blush, looking_at_viewer, bangs, upper_body, long_sleeves, very_long_hair, white_shirt, collared_shirt, dress_shirt, open_mouth, simple_background, white_background, closed_mouth, straight_hair, :d, floating_hair, wing_collar, collarbone |
| 4 | 10 |  |  |  |  |  | 1girl, dress, solo, twintails, bare_shoulders, blush, looking_at_viewer, mini_top_hat, open_mouth, hair_ribbon, smile, striped_thighhighs, white_gloves, heart |
| 5 | 16 |  |  |  |  |  | 1girl, smile, solo, dress, open_mouth, elbow_gloves, bare_shoulders, hair_ornament |
| 6 | 7 |  |  |  |  |  | blue_dress, hair_ornament, looking_at_viewer, 1girl, :d, earrings, open_mouth, shiny_hair, solo, very_long_hair, collarbone, floating_hair, necklace, standing, bangs, blush, choker, sleeveless_dress, strapless_dress, blue_gloves, cleavage, cowboy_shot, hair_between_eyes, holding_microphone, small_breasts, upper_body |
| 7 | 7 |  |  |  |  |  | 1girl, choker, smile, solo, hair_flower, skirt, blue_thighhighs, looking_at_viewer, mismatched_legwear, open_mouth, blush, microphone, white_background |
| 8 | 11 |  |  |  |  |  | 1girl, solo, flat_chest, nude, navel, nipples, blush, pussy, simple_background, open_mouth, small_breasts, smile |
| 9 | 10 |  |  |  |  |  | 1girl, solo, blush, cat_ears, school_swimsuit, tail, looking_at_viewer, paw_gloves, cat_paws, open_mouth, white_one-piece_swimsuit |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | blush | smile | looking_at_viewer | school_uniform | skirt | blazer | necktie | pantyhose | belt | midriff | navel | open_mouth | necklace | wrist_cuffs | hand_on_own_chest | cross | shiny_hair | bangs | upper_body | long_sleeves | very_long_hair | white_shirt | collared_shirt | dress_shirt | simple_background | white_background | closed_mouth | straight_hair | :d | floating_hair | wing_collar | collarbone | dress | twintails | bare_shoulders | mini_top_hat | hair_ribbon | striped_thighhighs | white_gloves | heart | elbow_gloves | hair_ornament | blue_dress | earrings | standing | choker | sleeveless_dress | strapless_dress | blue_gloves | cleavage | cowboy_shot | hair_between_eyes | holding_microphone | small_breasts | hair_flower | blue_thighhighs | mismatched_legwear | microphone | flat_chest | nude | nipples | pussy | cat_ears | school_swimsuit | tail | paw_gloves | cat_paws | white_one-piece_swimsuit |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:--------|:--------|:--------------------|:-----------------|:--------|:---------|:----------|:------------|:-------|:----------|:--------|:-------------|:-----------|:--------------|:--------------------|:--------|:-------------|:--------|:-------------|:---------------|:-----------------|:--------------|:-----------------|:--------------|:--------------------|:-------------------|:---------------|:----------------|:-----|:----------------|:--------------|:-------------|:--------|:------------|:-----------------|:---------------|:--------------|:---------------------|:---------------|:--------|:---------------|:----------------|:-------------|:-----------|:-----------|:---------|:-------------------|:------------------|:--------------|:-----------|:--------------|:--------------------|:---------------------|:----------------|:--------------|:------------------|:---------------------|:-------------|:-------------|:-------|:----------|:--------|:-----------|:------------------|:-------|:-------------|:-----------|:---------------------------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 11 |  |  |  |  |  | X | X | X | X | | | X | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 20 |  |  |  |  |  | X | X | X | | X | | | | | | | | | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 16 |  |  |  |  |  | X | X | | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 7 |  |  |  |  |  | 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 | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | X | X | X | | X | | | | | | | X | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | | | | | | | | | | |
| 8 | 11 |  |  |  |  |  | X | X | X | X | | | | | | | | | X | X | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | X | X | X | X | | | | | | |
| 9 | 10 |  |  |  |  |  | X | X | X | | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X |
|
bigbio/pico_extraction |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: PICO Annotation
homepage: https://github.com/Markus-Zlabinger/pico-annotation
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for PICO Annotation
## Dataset Description
- **Homepage:** https://github.com/Markus-Zlabinger/pico-annotation
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
This dataset contains annotations for Participants, Interventions, and Outcomes (referred to as PICO task).
For 423 sentences, annotations collected by 3 medical experts are available.
To get the final annotations, we perform the majority voting.
## Citation Information
```
@inproceedings{zlabinger-etal-2020-effective,
title = "Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports",
author = {Zlabinger, Markus and
Sabou, Marta and
Hofst{"a}tter, Sebastian and
Hanbury, Allan},
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.274",
doi = "10.18653/v1/2020.findings-emnlp.274",
pages = "3064--3074",
}
```
|
open-llm-leaderboard/details_Locutusque__Orca-2-13b-SFT-v6 | ---
pretty_name: Evaluation run of Locutusque/Orca-2-13b-SFT-v6
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Locutusque/Orca-2-13b-SFT-v6](https://huggingface.co/Locutusque/Orca-2-13b-SFT-v6)\
\ 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_Locutusque__Orca-2-13b-SFT-v6\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-30T02:03:43.380204](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Orca-2-13b-SFT-v6/blob/main/results_2023-12-30T02-03-43.380204.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\": 0.5890270904640104,\n\
\ \"acc_stderr\": 0.03291493635145001,\n \"acc_norm\": 0.5988157276074748,\n\
\ \"acc_norm_stderr\": 0.033710582507890004,\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.016987039266142985,\n \"mc2\": 0.5400874549545076,\n\
\ \"mc2_stderr\": 0.015468319271968397\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5622866894197952,\n \"acc_stderr\": 0.01449757388110829,\n\
\ \"acc_norm\": 0.6040955631399317,\n \"acc_norm_stderr\": 0.014291228393536585\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6218880701055567,\n\
\ \"acc_stderr\": 0.004839247332606039,\n \"acc_norm\": 0.8046205935072694,\n\
\ \"acc_norm_stderr\": 0.003956821705018451\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6666666666666666,\n\
\ \"acc_stderr\": 0.04072314811876837,\n \"acc_norm\": 0.6666666666666666,\n\
\ \"acc_norm_stderr\": 0.04072314811876837\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7631578947368421,\n \"acc_stderr\": 0.03459777606810535,\n\
\ \"acc_norm\": 0.7631578947368421,\n \"acc_norm_stderr\": 0.03459777606810535\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.6150943396226415,\n \"acc_stderr\": 0.02994649856769995,\n\
\ \"acc_norm\": 0.6150943396226415,\n \"acc_norm_stderr\": 0.02994649856769995\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6736111111111112,\n\
\ \"acc_stderr\": 0.03921067198982266,\n \"acc_norm\": 0.6736111111111112,\n\
\ \"acc_norm_stderr\": 0.03921067198982266\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.31,\n\
\ \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\": 0.31,\n \
\ \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.5491329479768786,\n \"acc_stderr\": 0.037940126746970296,\n\
\ \"acc_norm\": 0.5491329479768786,\n \"acc_norm_stderr\": 0.037940126746970296\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.29411764705882354,\n\
\ \"acc_stderr\": 0.04533838195929775,\n \"acc_norm\": 0.29411764705882354,\n\
\ \"acc_norm_stderr\": 0.04533838195929775\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\":\
\ 0.5063829787234042,\n \"acc_stderr\": 0.03268335899936336,\n \"\
acc_norm\": 0.5063829787234042,\n \"acc_norm_stderr\": 0.03268335899936336\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.044346007015849245,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.044346007015849245\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192117,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192117\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.35978835978835977,\n \"acc_stderr\": 0.024718075944129288,\n \"\
acc_norm\": 0.35978835978835977,\n \"acc_norm_stderr\": 0.024718075944129288\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.29365079365079366,\n\
\ \"acc_stderr\": 0.040735243221471255,\n \"acc_norm\": 0.29365079365079366,\n\
\ \"acc_norm_stderr\": 0.040735243221471255\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.36,\n \"acc_stderr\": 0.048241815132442176,\n \
\ \"acc_norm\": 0.36,\n \"acc_norm_stderr\": 0.048241815132442176\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.6967741935483871,\n \"acc_stderr\": 0.026148685930671742,\n \"\
acc_norm\": 0.6967741935483871,\n \"acc_norm_stderr\": 0.026148685930671742\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4827586206896552,\n \"acc_stderr\": 0.035158955511656986,\n \"\
acc_norm\": 0.4827586206896552,\n \"acc_norm_stderr\": 0.035158955511656986\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.62,\n \"acc_stderr\": 0.04878317312145632,\n \"acc_norm\"\
: 0.62,\n \"acc_norm_stderr\": 0.04878317312145632\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7515151515151515,\n \"acc_stderr\": 0.033744026441394036,\n\
\ \"acc_norm\": 0.7515151515151515,\n \"acc_norm_stderr\": 0.033744026441394036\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365907,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365907\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8497409326424871,\n \"acc_stderr\": 0.025787723180723875,\n\
\ \"acc_norm\": 0.8497409326424871,\n \"acc_norm_stderr\": 0.025787723180723875\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5820512820512821,\n \"acc_stderr\": 0.02500732988246122,\n \
\ \"acc_norm\": 0.5820512820512821,\n \"acc_norm_stderr\": 0.02500732988246122\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3074074074074074,\n \"acc_stderr\": 0.028133252578815642,\n \
\ \"acc_norm\": 0.3074074074074074,\n \"acc_norm_stderr\": 0.028133252578815642\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5756302521008403,\n \"acc_stderr\": 0.032104790510157764,\n\
\ \"acc_norm\": 0.5756302521008403,\n \"acc_norm_stderr\": 0.032104790510157764\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.36423841059602646,\n \"acc_stderr\": 0.03929111781242741,\n \"\
acc_norm\": 0.36423841059602646,\n \"acc_norm_stderr\": 0.03929111781242741\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7834862385321101,\n \"acc_stderr\": 0.01765871059444313,\n \"\
acc_norm\": 0.7834862385321101,\n \"acc_norm_stderr\": 0.01765871059444313\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4212962962962963,\n \"acc_stderr\": 0.03367462138896078,\n \"\
acc_norm\": 0.4212962962962963,\n \"acc_norm_stderr\": 0.03367462138896078\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7843137254901961,\n \"acc_stderr\": 0.028867431449849313,\n \"\
acc_norm\": 0.7843137254901961,\n \"acc_norm_stderr\": 0.028867431449849313\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8143459915611815,\n \"acc_stderr\": 0.025310495376944856,\n \
\ \"acc_norm\": 0.8143459915611815,\n \"acc_norm_stderr\": 0.025310495376944856\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6636771300448431,\n\
\ \"acc_stderr\": 0.031708824268455005,\n \"acc_norm\": 0.6636771300448431,\n\
\ \"acc_norm_stderr\": 0.031708824268455005\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7175572519083969,\n \"acc_stderr\": 0.03948406125768361,\n\
\ \"acc_norm\": 0.7175572519083969,\n \"acc_norm_stderr\": 0.03948406125768361\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070415,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070415\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\": 0.7055214723926381,\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\": 0.38392857142857145,\n\
\ \"acc_stderr\": 0.04616143075028547,\n \"acc_norm\": 0.38392857142857145,\n\
\ \"acc_norm_stderr\": 0.04616143075028547\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7475728155339806,\n \"acc_stderr\": 0.04301250399690876,\n\
\ \"acc_norm\": 0.7475728155339806,\n \"acc_norm_stderr\": 0.04301250399690876\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.023086635086841407,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.023086635086841407\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.59,\n \"acc_stderr\": 0.04943110704237102,\n \
\ \"acc_norm\": 0.59,\n \"acc_norm_stderr\": 0.04943110704237102\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.776500638569604,\n\
\ \"acc_stderr\": 0.01489723522945071,\n \"acc_norm\": 0.776500638569604,\n\
\ \"acc_norm_stderr\": 0.01489723522945071\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6907514450867052,\n \"acc_stderr\": 0.02488314057007176,\n\
\ \"acc_norm\": 0.6907514450867052,\n \"acc_norm_stderr\": 0.02488314057007176\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3407821229050279,\n\
\ \"acc_stderr\": 0.015852002449862103,\n \"acc_norm\": 0.3407821229050279,\n\
\ \"acc_norm_stderr\": 0.015852002449862103\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281406,\n\
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281406\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.662379421221865,\n\
\ \"acc_stderr\": 0.026858825879488544,\n \"acc_norm\": 0.662379421221865,\n\
\ \"acc_norm_stderr\": 0.026858825879488544\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7253086419753086,\n \"acc_stderr\": 0.024836057868294677,\n\
\ \"acc_norm\": 0.7253086419753086,\n \"acc_norm_stderr\": 0.024836057868294677\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.45390070921985815,\n \"acc_stderr\": 0.029700453247291488,\n \
\ \"acc_norm\": 0.45390070921985815,\n \"acc_norm_stderr\": 0.029700453247291488\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.42633637548891784,\n\
\ \"acc_stderr\": 0.012630884771599698,\n \"acc_norm\": 0.42633637548891784,\n\
\ \"acc_norm_stderr\": 0.012630884771599698\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5735294117647058,\n \"acc_stderr\": 0.030042615832714864,\n\
\ \"acc_norm\": 0.5735294117647058,\n \"acc_norm_stderr\": 0.030042615832714864\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6013071895424836,\n \"acc_stderr\": 0.019808281317449838,\n \
\ \"acc_norm\": 0.6013071895424836,\n \"acc_norm_stderr\": 0.019808281317449838\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7020408163265306,\n \"acc_stderr\": 0.029279567411065677,\n\
\ \"acc_norm\": 0.7020408163265306,\n \"acc_norm_stderr\": 0.029279567411065677\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7263681592039801,\n\
\ \"acc_stderr\": 0.03152439186555402,\n \"acc_norm\": 0.7263681592039801,\n\
\ \"acc_norm_stderr\": 0.03152439186555402\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \
\ \"acc_norm\": 0.79,\n \"acc_norm_stderr\": 0.040936018074033256\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5240963855421686,\n\
\ \"acc_stderr\": 0.03887971849597264,\n \"acc_norm\": 0.5240963855421686,\n\
\ \"acc_norm_stderr\": 0.03887971849597264\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.379436964504284,\n\
\ \"mc1_stderr\": 0.016987039266142985,\n \"mc2\": 0.5400874549545076,\n\
\ \"mc2_stderr\": 0.015468319271968397\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7742699289660616,\n \"acc_stderr\": 0.011749626260902542\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05079605761940864,\n \
\ \"acc_stderr\": 0.006048352096878093\n }\n}\n```"
repo_url: https://huggingface.co/Locutusque/Orca-2-13b-SFT-v6
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_12_30T02_03_43.380204
path:
- '**/details_harness|arc:challenge|25_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|gsm8k|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hellaswag|10_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-03-43.380204.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-30T02-03-43.380204.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- '**/details_harness|winogrande|5_2023-12-30T02-03-43.380204.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-30T02-03-43.380204.parquet'
- config_name: results
data_files:
- split: 2023_12_30T02_03_43.380204
path:
- results_2023-12-30T02-03-43.380204.parquet
- split: latest
path:
- results_2023-12-30T02-03-43.380204.parquet
---
# Dataset Card for Evaluation run of Locutusque/Orca-2-13b-SFT-v6
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Locutusque/Orca-2-13b-SFT-v6](https://huggingface.co/Locutusque/Orca-2-13b-SFT-v6) 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_Locutusque__Orca-2-13b-SFT-v6",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-30T02:03:43.380204](https://huggingface.co/datasets/open-llm-leaderboard/details_Locutusque__Orca-2-13b-SFT-v6/blob/main/results_2023-12-30T02-03-43.380204.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": 0.5890270904640104,
"acc_stderr": 0.03291493635145001,
"acc_norm": 0.5988157276074748,
"acc_norm_stderr": 0.033710582507890004,
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142985,
"mc2": 0.5400874549545076,
"mc2_stderr": 0.015468319271968397
},
"harness|arc:challenge|25": {
"acc": 0.5622866894197952,
"acc_stderr": 0.01449757388110829,
"acc_norm": 0.6040955631399317,
"acc_norm_stderr": 0.014291228393536585
},
"harness|hellaswag|10": {
"acc": 0.6218880701055567,
"acc_stderr": 0.004839247332606039,
"acc_norm": 0.8046205935072694,
"acc_norm_stderr": 0.003956821705018451
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6666666666666666,
"acc_stderr": 0.04072314811876837,
"acc_norm": 0.6666666666666666,
"acc_norm_stderr": 0.04072314811876837
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7631578947368421,
"acc_stderr": 0.03459777606810535,
"acc_norm": 0.7631578947368421,
"acc_norm_stderr": 0.03459777606810535
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.68,
"acc_stderr": 0.04688261722621504,
"acc_norm": 0.68,
"acc_norm_stderr": 0.04688261722621504
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6150943396226415,
"acc_stderr": 0.02994649856769995,
"acc_norm": 0.6150943396226415,
"acc_norm_stderr": 0.02994649856769995
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6736111111111112,
"acc_stderr": 0.03921067198982266,
"acc_norm": 0.6736111111111112,
"acc_norm_stderr": 0.03921067198982266
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5491329479768786,
"acc_stderr": 0.037940126746970296,
"acc_norm": 0.5491329479768786,
"acc_norm_stderr": 0.037940126746970296
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.29411764705882354,
"acc_stderr": 0.04533838195929775,
"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929775
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5063829787234042,
"acc_stderr": 0.03268335899936336,
"acc_norm": 0.5063829787234042,
"acc_norm_stderr": 0.03268335899936336
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.044346007015849245,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.044346007015849245
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192117,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192117
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.35978835978835977,
"acc_stderr": 0.024718075944129288,
"acc_norm": 0.35978835978835977,
"acc_norm_stderr": 0.024718075944129288
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.29365079365079366,
"acc_stderr": 0.040735243221471255,
"acc_norm": 0.29365079365079366,
"acc_norm_stderr": 0.040735243221471255
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.36,
"acc_stderr": 0.048241815132442176,
"acc_norm": 0.36,
"acc_norm_stderr": 0.048241815132442176
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.6967741935483871,
"acc_stderr": 0.026148685930671742,
"acc_norm": 0.6967741935483871,
"acc_norm_stderr": 0.026148685930671742
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4827586206896552,
"acc_stderr": 0.035158955511656986,
"acc_norm": 0.4827586206896552,
"acc_norm_stderr": 0.035158955511656986
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.62,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.62,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7515151515151515,
"acc_stderr": 0.033744026441394036,
"acc_norm": 0.7515151515151515,
"acc_norm_stderr": 0.033744026441394036
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365907,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365907
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8497409326424871,
"acc_stderr": 0.025787723180723875,
"acc_norm": 0.8497409326424871,
"acc_norm_stderr": 0.025787723180723875
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5820512820512821,
"acc_stderr": 0.02500732988246122,
"acc_norm": 0.5820512820512821,
"acc_norm_stderr": 0.02500732988246122
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3074074074074074,
"acc_stderr": 0.028133252578815642,
"acc_norm": 0.3074074074074074,
"acc_norm_stderr": 0.028133252578815642
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5756302521008403,
"acc_stderr": 0.032104790510157764,
"acc_norm": 0.5756302521008403,
"acc_norm_stderr": 0.032104790510157764
},
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"acc": 0.7175572519083969,
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"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-nutrition|5": {
"acc": 0.673202614379085,
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},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.662379421221865,
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"acc_norm": 0.662379421221865,
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},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7253086419753086,
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"acc_norm": 0.7253086419753086,
"acc_norm_stderr": 0.024836057868294677
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.45390070921985815,
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"acc_norm_stderr": 0.029700453247291488
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.42633637548891784,
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},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5735294117647058,
"acc_stderr": 0.030042615832714864,
"acc_norm": 0.5735294117647058,
"acc_norm_stderr": 0.030042615832714864
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6013071895424836,
"acc_stderr": 0.019808281317449838,
"acc_norm": 0.6013071895424836,
"acc_norm_stderr": 0.019808281317449838
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7020408163265306,
"acc_stderr": 0.029279567411065677,
"acc_norm": 0.7020408163265306,
"acc_norm_stderr": 0.029279567411065677
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7263681592039801,
"acc_stderr": 0.03152439186555402,
"acc_norm": 0.7263681592039801,
"acc_norm_stderr": 0.03152439186555402
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5240963855421686,
"acc_stderr": 0.03887971849597264,
"acc_norm": 0.5240963855421686,
"acc_norm_stderr": 0.03887971849597264
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8070175438596491,
"acc_stderr": 0.030267457554898458,
"acc_norm": 0.8070175438596491,
"acc_norm_stderr": 0.030267457554898458
},
"harness|truthfulqa:mc|0": {
"mc1": 0.379436964504284,
"mc1_stderr": 0.016987039266142985,
"mc2": 0.5400874549545076,
"mc2_stderr": 0.015468319271968397
},
"harness|winogrande|5": {
"acc": 0.7742699289660616,
"acc_stderr": 0.011749626260902542
},
"harness|gsm8k|5": {
"acc": 0.05079605761940864,
"acc_stderr": 0.006048352096878093
}
}
```
## 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] |
joey234/mmlu-high_school_computer_science | ---
dataset_info:
features:
- name: question
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: negate_openai_prompt
struct:
- name: content
dtype: string
- name: role
dtype: string
- name: neg_question
dtype: string
- name: fewshot_context
dtype: string
- name: fewshot_context_neg
dtype: string
splits:
- name: dev
num_bytes: 7186
num_examples: 5
- name: test
num_bytes: 551036
num_examples: 100
download_size: 100819
dataset_size: 558222
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-high_school_computer_science"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Archeane/tag-0 | ---
license: apache-2.0
---
|
mahdibaghbanzadeh/GUE_EMP_H3K14ac | ---
dataset_info:
features:
- name: sequence
dtype: string
- name: labels
dtype:
class_label:
names:
'0': '0'
'1': '1'
splits:
- name: train
num_bytes: 13536015
num_examples: 26438
- name: val
num_bytes: 1691950
num_examples: 3305
- name: test
num_bytes: 1692160
num_examples: 3305
download_size: 7984315
dataset_size: 16920125
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
---
|
CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_C_Q_rices_ns_25994 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: question
dtype: string
- name: true_label
sequence: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_all_patches_Salesforce_blip_image_captioning_large_clean_
num_bytes: 3698359
num_examples: 25994
download_size: 1326018
dataset_size: 3698359
---
# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_D_PNP_GENERIC_C_Q_rices_ns_25994"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NathanRoll/TalkBank_CA_CLAPI | ---
dataset_info:
features:
- name: audio
sequence: float32
- name: __index_level_0__
dtype: string
splits:
- name: train
num_bytes: 1650568570
num_examples: 24
download_size: 1653241682
dataset_size: 1650568570
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "TalkBank_CA_CLAPI"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yucx0626/Gamora-CSA-Multiplier | ---
license: bsd
---
|
jmeld/kidzbop | ---
task_categories:
- text-generation
language:
- en
tags:
- music
- art
pretty_name: Kidz Bopify
size_categories:
- 10K<n<100K
--- |
ravithejads/yahma_alpaca_cleaned_telugu_filtered_and_romanized | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 144668251
num_examples: 72682
download_size: 60754962
dataset_size: 144668251
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
p1atdev/japanese-stackexchange | ---
dataset_info:
- config_name: default
features:
- name: question
struct:
- name: accepted_answer_id
dtype: string
- name: answer_count
dtype: int64
- name: body
dtype: string
- name: comment_count
dtype: int64
- name: content_license
dtype: string
- name: creation_date
dtype: string
- name: favorite_count
dtype: int64
- name: id
dtype: string
- name: last_activity_date
dtype: string
- name: last_edit_date
dtype: string
- name: last_editor_user_id
dtype: string
- name: owner_user_id
dtype: string
- name: post_type
dtype: string
- name: score
dtype: int64
- name: tags
sequence: string
- name: title
dtype: string
- name: view_count
dtype: int64
- name: answers
list:
- name: body
dtype: string
- name: comment_count
dtype: int64
- name: content_license
dtype: string
- name: creation_date
dtype: string
- name: id
dtype: string
- name: last_activity_date
dtype: string
- name: last_edit_date
dtype: string
- name: last_editor_user_id
dtype: string
- name: owner_user_id
dtype: string
- name: parent_id
dtype: string
- name: post_type
dtype: string
- name: score
dtype: int64
- name: id
dtype: string
- name: accepted_answer_id
dtype: string
- name: popular_answer_id
dtype: string
splits:
- name: train
num_bytes: 67721507
num_examples: 28428
download_size: 38951308
dataset_size: 67721507
- config_name: simple
features:
- name: id
dtype: string
- name: accepted_answer_id
dtype: string
- name: popular_answer_id
dtype: string
- name: title
dtype: string
- name: question_body
dtype: string
- name: question_score
dtype: int64
- name: accepted_answer_body
dtype: string
- name: accepted_answer_score
dtype: int64
- name: popular_answer_body
dtype: string
- name: popular_answer_score
dtype: int64
- name: tags
sequence: string
splits:
- name: train
num_bytes: 66135683
num_examples: 28428
download_size: 40717946
dataset_size: 66135683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- config_name: simple
data_files:
- split: train
path: simple/train-*
license: cc-by-sa-4.0
task_categories:
- text-generation
- question-answering
language:
- en
- ja
tags:
- stackexchange
pretty_name: Japanese StackExchange
size_categories:
- 10K<n<100K
---
# japanese-stackexchange
英語による日本語に関する質問ができる [Japanese Stack Exchange](https://japanese.stackexchange.com/) の[データダンプ](https://archive.org/download/stackexchange) をもとにデータを加工し、質問文と回答文のペアになるように調整した QA データセット。
日本語翻訳された StackExchange ではないです。
## データ構造
投稿本文は `html2text` を使ってマークダウン化されています。その際、
- コードブロックは \`\`\` で囲まれるように変更されています。
- 画像 URL に base64 エンコードされた画像が含まれる場合、 `[unk]` に置き換えています。
### `default` サブセット
- `id`: 質問投稿の ID
- `question`: 質問投稿
- `answers`: 質問に対する回答投稿のリスト
- `accepted_answer_id`: 質問者に選ばれた回答のID。`null` の可能性がある
- `popular_answer_id`: もっともスコアが高かった回答のID。`null` の可能性がある
### `simple` サブセット
`default` サブセットから、 `question` と `answers` の辞書を展開しシンプルにしたもの。
- `id`: 質問投稿の ID
- `accepted_answer_id`: 質問者に選ばれた回答のID。`null` の可能性がある
- `popular_answer_id`: もっともスコアが高かった回答のID。`null` の可能性がある
- `title`: 質問のタイトル
- `question_body`: 質問本文
- `question_score`: 質問のスコア
- `tags`: 質問に関連付けられたタグ
- `accepted_answer_body`: 質問者に選ばれた回答の本文。`null` の可能性がある
- `accepted_answer_score`: 質問者に選ばれた回答のスコア。`null` の可能性がある
- `popular_answer_body`: もっともスコアが高かった回答の本文。`null` の可能性がある
- `popular_answer_score`: もっともスコアが高かった回答のスコア。`null` の可能性がある
## 使い方
datasets ライブラリを用いて簡単に利用できます。
```py
from datasets import load_dataset
dataset = load_dataset("p1atdev/japanese-stackexchange", name="simple" split="train")
print(dataset)
#Dataset({
# features: ['id', 'accepted_answer_id', 'popular_answer_id', 'title', 'question_body', 'question_score', 'accepted_answer_body', 'accepted_answer_score', 'popular_answer_body', 'popular_answer_score', 'tags'],
# num_rows: 28428
#})
```
## ライセンス
StackExchange に基づき、[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja)
|
Lo/clip-bert-data | ---
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
---
# CLIP-BERT training data
This data was used to train the CLIP-BERT model first described in [this paper](https://arxiv.org/abs/2109.11321).
The dataset is based on text and images from MS COCO, SBU Captions, Visual Genome QA and Conceptual Captions.
The image features have been extracted using the CLIP model [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) available on Huggingface. |
minh21/COVID-QA-question-answering-biencoder-data-75_25 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: context_chunks
sequence: string
- name: document_id
dtype: int64
- name: id
dtype: int64
splits:
- name: train
num_bytes: 59010693
num_examples: 1348
- name: validation
num_bytes: 4567041
num_examples: 158
download_size: 13833996
dataset_size: 63577734
---
# Dataset Card for "COVID-QA-question-answering-biencoder-data-75_25"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt | ---
pretty_name: Evaluation run of h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt)\
\ 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_h2oai__h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-21T18:24:41.819664](https://huggingface.co/datasets/open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt/blob/main/results_2023-10-21T18-24-41.819664.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\": 0.0014681208053691276,\n\
\ \"em_stderr\": 0.00039210421902985155,\n \"f1\": 0.05380872483221496,\n\
\ \"f1_stderr\": 0.0013618747592707128,\n \"acc\": 0.33039128803540985,\n\
\ \"acc_stderr\": 0.008404668659041216\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.0014681208053691276,\n \"em_stderr\": 0.00039210421902985155,\n\
\ \"f1\": 0.05380872483221496,\n \"f1_stderr\": 0.0013618747592707128\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.015163002274450341,\n \
\ \"acc_stderr\": 0.003366022949726341\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6456195737963694,\n \"acc_stderr\": 0.013443314368356092\n\
\ }\n}\n```"
repo_url: https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt
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_07_19T17_21_26.476069
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_21T18_24_41.819664
path:
- '**/details_harness|drop|3_2023-10-21T18-24-41.819664.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-21T18-24-41.819664.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_21T18_24_41.819664
path:
- '**/details_harness|gsm8k|5_2023-10-21T18-24-41.819664.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-21T18-24-41.819664.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:21:26.476069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:21:26.476069.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T17:21:26.476069.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_21T18_24_41.819664
path:
- '**/details_harness|winogrande|5_2023-10-21T18-24-41.819664.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-21T18-24-41.819664.parquet'
- config_name: results
data_files:
- split: 2023_07_19T17_21_26.476069
path:
- results_2023-07-19T17:21:26.476069.parquet
- split: 2023_10_21T18_24_41.819664
path:
- results_2023-10-21T18-24-41.819664.parquet
- split: latest
path:
- results_2023-10-21T18-24-41.819664.parquet
---
# Dataset Card for Evaluation run of h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt
- **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 [h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt) 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_h2oai__h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-21T18:24:41.819664](https://huggingface.co/datasets/open-llm-leaderboard/details_h2oai__h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt/blob/main/results_2023-10-21T18-24-41.819664.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": 0.0014681208053691276,
"em_stderr": 0.00039210421902985155,
"f1": 0.05380872483221496,
"f1_stderr": 0.0013618747592707128,
"acc": 0.33039128803540985,
"acc_stderr": 0.008404668659041216
},
"harness|drop|3": {
"em": 0.0014681208053691276,
"em_stderr": 0.00039210421902985155,
"f1": 0.05380872483221496,
"f1_stderr": 0.0013618747592707128
},
"harness|gsm8k|5": {
"acc": 0.015163002274450341,
"acc_stderr": 0.003366022949726341
},
"harness|winogrande|5": {
"acc": 0.6456195737963694,
"acc_stderr": 0.013443314368356092
}
}
```
### 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] |
vyoma/acl-ocl-fork-gemini-power-responses | ---
license: mit
---
|
k0ntra/shirazfa2 | ---
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splits:
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num_bytes: 147456
num_examples: 96
download_size: 324302
dataset_size: 147456
---
# Dataset Card for "shirazfa2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_DatPySci__pythia-1b-sft-full | ---
pretty_name: Evaluation run of DatPySci/pythia-1b-sft-full
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [DatPySci/pythia-1b-sft-full](https://huggingface.co/DatPySci/pythia-1b-sft-full)\
\ 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_DatPySci__pythia-1b-sft-full\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-13T16:10:25.536341](https://huggingface.co/datasets/open-llm-leaderboard/details_DatPySci__pythia-1b-sft-full/blob/main/results_2024-02-13T16-10-25.536341.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\": 0.2437500378442946,\n\
\ \"acc_stderr\": 0.030213863245287735,\n \"acc_norm\": 0.24468974101675026,\n\
\ \"acc_norm_stderr\": 0.03094305925119546,\n \"mc1\": 0.2252141982864137,\n\
\ \"mc1_stderr\": 0.014623240768023496,\n \"mc2\": 0.37081334738032573,\n\
\ \"mc2_stderr\": 0.014356461899633393\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.27303754266211605,\n \"acc_stderr\": 0.013019332762635753,\n\
\ \"acc_norm\": 0.295221843003413,\n \"acc_norm_stderr\": 0.013329750293382316\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.38697470623381797,\n\
\ \"acc_stderr\": 0.004860623733461137,\n \"acc_norm\": 0.48914558852818163,\n\
\ \"acc_norm_stderr\": 0.004988605498273906\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\": 0.24444444444444444,\n\
\ \"acc_stderr\": 0.03712537833614866,\n \"acc_norm\": 0.24444444444444444,\n\
\ \"acc_norm_stderr\": 0.03712537833614866\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.16447368421052633,\n \"acc_stderr\": 0.0301675334686327,\n\
\ \"acc_norm\": 0.16447368421052633,\n \"acc_norm_stderr\": 0.0301675334686327\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.2,\n\
\ \"acc_stderr\": 0.04020151261036843,\n \"acc_norm\": 0.2,\n \
\ \"acc_norm_stderr\": 0.04020151261036843\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.26037735849056604,\n \"acc_stderr\": 0.027008766090708094,\n\
\ \"acc_norm\": 0.26037735849056604,\n \"acc_norm_stderr\": 0.027008766090708094\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.20833333333333334,\n\
\ \"acc_stderr\": 0.033961162058453336,\n \"acc_norm\": 0.20833333333333334,\n\
\ \"acc_norm_stderr\": 0.033961162058453336\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.18,\n \"acc_stderr\": 0.038612291966536934,\n \
\ \"acc_norm\": 0.18,\n \"acc_norm_stderr\": 0.038612291966536934\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\"\
: 0.36,\n \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-college_mathematics|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-college_medicine|5\": {\n \"acc\": 0.27167630057803466,\n\
\ \"acc_stderr\": 0.0339175032232166,\n \"acc_norm\": 0.27167630057803466,\n\
\ \"acc_norm_stderr\": 0.0339175032232166\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.22549019607843138,\n \"acc_stderr\": 0.04158307533083286,\n\
\ \"acc_norm\": 0.22549019607843138,\n \"acc_norm_stderr\": 0.04158307533083286\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.24,\n \"acc_stderr\": 0.04292346959909284,\n \"acc_norm\": 0.24,\n\
\ \"acc_norm_stderr\": 0.04292346959909284\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.251063829787234,\n \"acc_stderr\": 0.028346963777162452,\n\
\ \"acc_norm\": 0.251063829787234,\n \"acc_norm_stderr\": 0.028346963777162452\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.21052631578947367,\n\
\ \"acc_stderr\": 0.0383515395439942,\n \"acc_norm\": 0.21052631578947367,\n\
\ \"acc_norm_stderr\": 0.0383515395439942\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.1793103448275862,\n \"acc_stderr\": 0.031967664333731875,\n\
\ \"acc_norm\": 0.1793103448275862,\n \"acc_norm_stderr\": 0.031967664333731875\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2619047619047619,\n \"acc_stderr\": 0.022644212615525218,\n \"\
acc_norm\": 0.2619047619047619,\n \"acc_norm_stderr\": 0.022644212615525218\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.25396825396825395,\n\
\ \"acc_stderr\": 0.03893259610604673,\n \"acc_norm\": 0.25396825396825395,\n\
\ \"acc_norm_stderr\": 0.03893259610604673\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.22903225806451613,\n\
\ \"acc_stderr\": 0.023904914311782644,\n \"acc_norm\": 0.22903225806451613,\n\
\ \"acc_norm_stderr\": 0.023904914311782644\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2019704433497537,\n \"acc_stderr\": 0.028247350122180253,\n\
\ \"acc_norm\": 0.2019704433497537,\n \"acc_norm_stderr\": 0.028247350122180253\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\"\
: 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.19393939393939394,\n \"acc_stderr\": 0.0308741451365621,\n\
\ \"acc_norm\": 0.19393939393939394,\n \"acc_norm_stderr\": 0.0308741451365621\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.18181818181818182,\n \"acc_stderr\": 0.027479603010538808,\n \"\
acc_norm\": 0.18181818181818182,\n \"acc_norm_stderr\": 0.027479603010538808\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.21761658031088082,\n \"acc_stderr\": 0.029778663037752954,\n\
\ \"acc_norm\": 0.21761658031088082,\n \"acc_norm_stderr\": 0.029778663037752954\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.23076923076923078,\n \"acc_stderr\": 0.02136202772522272,\n\
\ \"acc_norm\": 0.23076923076923078,\n \"acc_norm_stderr\": 0.02136202772522272\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2777777777777778,\n \"acc_stderr\": 0.02730914058823019,\n \
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.02730914058823019\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.20588235294117646,\n \"acc_stderr\": 0.02626502460827589,\n\
\ \"acc_norm\": 0.20588235294117646,\n \"acc_norm_stderr\": 0.02626502460827589\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.2185430463576159,\n \"acc_stderr\": 0.03374235550425694,\n \"\
acc_norm\": 0.2185430463576159,\n \"acc_norm_stderr\": 0.03374235550425694\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.28623853211009176,\n \"acc_stderr\": 0.019379436628919965,\n \"\
acc_norm\": 0.28623853211009176,\n \"acc_norm_stderr\": 0.019379436628919965\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.35185185185185186,\n \"acc_stderr\": 0.03256850570293648,\n \"\
acc_norm\": 0.35185185185185186,\n \"acc_norm_stderr\": 0.03256850570293648\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.23039215686274508,\n \"acc_stderr\": 0.029554292605695053,\n \"\
acc_norm\": 0.23039215686274508,\n \"acc_norm_stderr\": 0.029554292605695053\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.2616033755274262,\n \"acc_stderr\": 0.028609516716994934,\n \
\ \"acc_norm\": 0.2616033755274262,\n \"acc_norm_stderr\": 0.028609516716994934\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.29596412556053814,\n\
\ \"acc_stderr\": 0.030636591348699813,\n \"acc_norm\": 0.29596412556053814,\n\
\ \"acc_norm_stderr\": 0.030636591348699813\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.24427480916030533,\n \"acc_stderr\": 0.03768335959728744,\n\
\ \"acc_norm\": 0.24427480916030533,\n \"acc_norm_stderr\": 0.03768335959728744\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.23140495867768596,\n \"acc_stderr\": 0.03849856098794088,\n \"\
acc_norm\": 0.23140495867768596,\n \"acc_norm_stderr\": 0.03849856098794088\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.26851851851851855,\n\
\ \"acc_stderr\": 0.04284467968052192,\n \"acc_norm\": 0.26851851851851855,\n\
\ \"acc_norm_stderr\": 0.04284467968052192\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.22699386503067484,\n \"acc_stderr\": 0.03291099578615771,\n\
\ \"acc_norm\": 0.22699386503067484,\n \"acc_norm_stderr\": 0.03291099578615771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.2857142857142857,\n\
\ \"acc_stderr\": 0.04287858751340456,\n \"acc_norm\": 0.2857142857142857,\n\
\ \"acc_norm_stderr\": 0.04287858751340456\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.18446601941747573,\n \"acc_stderr\": 0.03840423627288276,\n\
\ \"acc_norm\": 0.18446601941747573,\n \"acc_norm_stderr\": 0.03840423627288276\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2777777777777778,\n\
\ \"acc_stderr\": 0.029343114798094472,\n \"acc_norm\": 0.2777777777777778,\n\
\ \"acc_norm_stderr\": 0.029343114798094472\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.27,\n \"acc_stderr\": 0.044619604333847394,\n \
\ \"acc_norm\": 0.27,\n \"acc_norm_stderr\": 0.044619604333847394\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.26947637292464877,\n\
\ \"acc_stderr\": 0.015866243073215054,\n \"acc_norm\": 0.26947637292464877,\n\
\ \"acc_norm_stderr\": 0.015866243073215054\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2398843930635838,\n \"acc_stderr\": 0.022989592543123567,\n\
\ \"acc_norm\": 0.2398843930635838,\n \"acc_norm_stderr\": 0.022989592543123567\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24581005586592178,\n\
\ \"acc_stderr\": 0.01440029642922563,\n \"acc_norm\": 0.24581005586592178,\n\
\ \"acc_norm_stderr\": 0.01440029642922563\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.23202614379084968,\n \"acc_stderr\": 0.024170840879341012,\n\
\ \"acc_norm\": 0.23202614379084968,\n \"acc_norm_stderr\": 0.024170840879341012\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.24115755627009647,\n\
\ \"acc_stderr\": 0.024296594034763426,\n \"acc_norm\": 0.24115755627009647,\n\
\ \"acc_norm_stderr\": 0.024296594034763426\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.0242885336377261,\n\
\ \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.0242885336377261\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.24822695035460993,\n \"acc_stderr\": 0.025770015644290413,\n \
\ \"acc_norm\": 0.24822695035460993,\n \"acc_norm_stderr\": 0.025770015644290413\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.23989569752281617,\n\
\ \"acc_stderr\": 0.01090628261798164,\n \"acc_norm\": 0.23989569752281617,\n\
\ \"acc_norm_stderr\": 0.01090628261798164\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.2977941176470588,\n \"acc_stderr\": 0.027778298701545443,\n\
\ \"acc_norm\": 0.2977941176470588,\n \"acc_norm_stderr\": 0.027778298701545443\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2565359477124183,\n \"acc_stderr\": 0.01766784161237899,\n \
\ \"acc_norm\": 0.2565359477124183,\n \"acc_norm_stderr\": 0.01766784161237899\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.21818181818181817,\n\
\ \"acc_stderr\": 0.03955932861795833,\n \"acc_norm\": 0.21818181818181817,\n\
\ \"acc_norm_stderr\": 0.03955932861795833\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.1836734693877551,\n \"acc_stderr\": 0.024789071332007643,\n\
\ \"acc_norm\": 0.1836734693877551,\n \"acc_norm_stderr\": 0.024789071332007643\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.2537313432835821,\n\
\ \"acc_stderr\": 0.03076944496729602,\n \"acc_norm\": 0.2537313432835821,\n\
\ \"acc_norm_stderr\": 0.03076944496729602\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.14,\n \"acc_stderr\": 0.03487350880197772,\n \
\ \"acc_norm\": 0.14,\n \"acc_norm_stderr\": 0.03487350880197772\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.24096385542168675,\n\
\ \"acc_stderr\": 0.0332939411907353,\n \"acc_norm\": 0.24096385542168675,\n\
\ \"acc_norm_stderr\": 0.0332939411907353\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.2046783625730994,\n \"acc_stderr\": 0.030944459778533207,\n\
\ \"acc_norm\": 0.2046783625730994,\n \"acc_norm_stderr\": 0.030944459778533207\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2252141982864137,\n\
\ \"mc1_stderr\": 0.014623240768023496,\n \"mc2\": 0.37081334738032573,\n\
\ \"mc2_stderr\": 0.014356461899633393\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.5367008681925809,\n \"acc_stderr\": 0.014014578458843262\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.019711902956785442,\n \
\ \"acc_stderr\": 0.0038289829787357134\n }\n}\n```"
repo_url: https://huggingface.co/DatPySci/pythia-1b-sft-full
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_02_13T16_10_25.536341
path:
- '**/details_harness|arc:challenge|25_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|gsm8k|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hellaswag|10_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T16-10-25.536341.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-13T16-10-25.536341.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- '**/details_harness|winogrande|5_2024-02-13T16-10-25.536341.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-13T16-10-25.536341.parquet'
- config_name: results
data_files:
- split: 2024_02_13T16_10_25.536341
path:
- results_2024-02-13T16-10-25.536341.parquet
- split: latest
path:
- results_2024-02-13T16-10-25.536341.parquet
---
# Dataset Card for Evaluation run of DatPySci/pythia-1b-sft-full
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [DatPySci/pythia-1b-sft-full](https://huggingface.co/DatPySci/pythia-1b-sft-full) 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_DatPySci__pythia-1b-sft-full",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-13T16:10:25.536341](https://huggingface.co/datasets/open-llm-leaderboard/details_DatPySci__pythia-1b-sft-full/blob/main/results_2024-02-13T16-10-25.536341.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": 0.2437500378442946,
"acc_stderr": 0.030213863245287735,
"acc_norm": 0.24468974101675026,
"acc_norm_stderr": 0.03094305925119546,
"mc1": 0.2252141982864137,
"mc1_stderr": 0.014623240768023496,
"mc2": 0.37081334738032573,
"mc2_stderr": 0.014356461899633393
},
"harness|arc:challenge|25": {
"acc": 0.27303754266211605,
"acc_stderr": 0.013019332762635753,
"acc_norm": 0.295221843003413,
"acc_norm_stderr": 0.013329750293382316
},
"harness|hellaswag|10": {
"acc": 0.38697470623381797,
"acc_stderr": 0.004860623733461137,
"acc_norm": 0.48914558852818163,
"acc_norm_stderr": 0.004988605498273906
},
"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": 0.24444444444444444,
"acc_stderr": 0.03712537833614866,
"acc_norm": 0.24444444444444444,
"acc_norm_stderr": 0.03712537833614866
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.16447368421052633,
"acc_stderr": 0.0301675334686327,
"acc_norm": 0.16447368421052633,
"acc_norm_stderr": 0.0301675334686327
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036843,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036843
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.26037735849056604,
"acc_stderr": 0.027008766090708094,
"acc_norm": 0.26037735849056604,
"acc_norm_stderr": 0.027008766090708094
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.20833333333333334,
"acc_stderr": 0.033961162058453336,
"acc_norm": 0.20833333333333334,
"acc_norm_stderr": 0.033961162058453336
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.36,
"acc_stderr": 0.04824181513244218,
"acc_norm": 0.36,
"acc_norm_stderr": 0.04824181513244218
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.27167630057803466,
"acc_stderr": 0.0339175032232166,
"acc_norm": 0.27167630057803466,
"acc_norm_stderr": 0.0339175032232166
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.22549019607843138,
"acc_stderr": 0.04158307533083286,
"acc_norm": 0.22549019607843138,
"acc_norm_stderr": 0.04158307533083286
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909284,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909284
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.251063829787234,
"acc_stderr": 0.028346963777162452,
"acc_norm": 0.251063829787234,
"acc_norm_stderr": 0.028346963777162452
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.21052631578947367,
"acc_stderr": 0.0383515395439942,
"acc_norm": 0.21052631578947367,
"acc_norm_stderr": 0.0383515395439942
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.1793103448275862,
"acc_stderr": 0.031967664333731875,
"acc_norm": 0.1793103448275862,
"acc_norm_stderr": 0.031967664333731875
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2619047619047619,
"acc_stderr": 0.022644212615525218,
"acc_norm": 0.2619047619047619,
"acc_norm_stderr": 0.022644212615525218
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.25396825396825395,
"acc_stderr": 0.03893259610604673,
"acc_norm": 0.25396825396825395,
"acc_norm_stderr": 0.03893259610604673
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.22903225806451613,
"acc_stderr": 0.023904914311782644,
"acc_norm": 0.22903225806451613,
"acc_norm_stderr": 0.023904914311782644
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2019704433497537,
"acc_stderr": 0.028247350122180253,
"acc_norm": 0.2019704433497537,
"acc_norm_stderr": 0.028247350122180253
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.19393939393939394,
"acc_stderr": 0.0308741451365621,
"acc_norm": 0.19393939393939394,
"acc_norm_stderr": 0.0308741451365621
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.18181818181818182,
"acc_stderr": 0.027479603010538808,
"acc_norm": 0.18181818181818182,
"acc_norm_stderr": 0.027479603010538808
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.21761658031088082,
"acc_stderr": 0.029778663037752954,
"acc_norm": 0.21761658031088082,
"acc_norm_stderr": 0.029778663037752954
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.23076923076923078,
"acc_stderr": 0.02136202772522272,
"acc_norm": 0.23076923076923078,
"acc_norm_stderr": 0.02136202772522272
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.02730914058823019,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.02730914058823019
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.20588235294117646,
"acc_stderr": 0.02626502460827589,
"acc_norm": 0.20588235294117646,
"acc_norm_stderr": 0.02626502460827589
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.2185430463576159,
"acc_stderr": 0.03374235550425694,
"acc_norm": 0.2185430463576159,
"acc_norm_stderr": 0.03374235550425694
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.28623853211009176,
"acc_stderr": 0.019379436628919965,
"acc_norm": 0.28623853211009176,
"acc_norm_stderr": 0.019379436628919965
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.35185185185185186,
"acc_stderr": 0.03256850570293648,
"acc_norm": 0.35185185185185186,
"acc_norm_stderr": 0.03256850570293648
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.23039215686274508,
"acc_stderr": 0.029554292605695053,
"acc_norm": 0.23039215686274508,
"acc_norm_stderr": 0.029554292605695053
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.2616033755274262,
"acc_stderr": 0.028609516716994934,
"acc_norm": 0.2616033755274262,
"acc_norm_stderr": 0.028609516716994934
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.29596412556053814,
"acc_stderr": 0.030636591348699813,
"acc_norm": 0.29596412556053814,
"acc_norm_stderr": 0.030636591348699813
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.24427480916030533,
"acc_stderr": 0.03768335959728744,
"acc_norm": 0.24427480916030533,
"acc_norm_stderr": 0.03768335959728744
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.23140495867768596,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.23140495867768596,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.26851851851851855,
"acc_stderr": 0.04284467968052192,
"acc_norm": 0.26851851851851855,
"acc_norm_stderr": 0.04284467968052192
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.22699386503067484,
"acc_stderr": 0.03291099578615771,
"acc_norm": 0.22699386503067484,
"acc_norm_stderr": 0.03291099578615771
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.04287858751340456,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.04287858751340456
},
"harness|hendrycksTest-management|5": {
"acc": 0.18446601941747573,
"acc_stderr": 0.03840423627288276,
"acc_norm": 0.18446601941747573,
"acc_norm_stderr": 0.03840423627288276
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.2777777777777778,
"acc_stderr": 0.029343114798094472,
"acc_norm": 0.2777777777777778,
"acc_norm_stderr": 0.029343114798094472
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.27,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.27,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.26947637292464877,
"acc_stderr": 0.015866243073215054,
"acc_norm": 0.26947637292464877,
"acc_norm_stderr": 0.015866243073215054
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.2398843930635838,
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"acc_norm": 0.2398843930635838,
"acc_norm_stderr": 0.022989592543123567
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.24581005586592178,
"acc_stderr": 0.01440029642922563,
"acc_norm": 0.24581005586592178,
"acc_norm_stderr": 0.01440029642922563
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.23202614379084968,
"acc_stderr": 0.024170840879341012,
"acc_norm": 0.23202614379084968,
"acc_norm_stderr": 0.024170840879341012
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.24115755627009647,
"acc_stderr": 0.024296594034763426,
"acc_norm": 0.24115755627009647,
"acc_norm_stderr": 0.024296594034763426
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.25617283950617287,
"acc_stderr": 0.0242885336377261,
"acc_norm": 0.25617283950617287,
"acc_norm_stderr": 0.0242885336377261
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.24822695035460993,
"acc_stderr": 0.025770015644290413,
"acc_norm": 0.24822695035460993,
"acc_norm_stderr": 0.025770015644290413
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.23989569752281617,
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"acc_norm": 0.23989569752281617,
"acc_norm_stderr": 0.01090628261798164
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.2977941176470588,
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"acc_norm": 0.2977941176470588,
"acc_norm_stderr": 0.027778298701545443
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.2565359477124183,
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"acc_norm_stderr": 0.01766784161237899
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.21818181818181817,
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"acc_norm": 0.21818181818181817,
"acc_norm_stderr": 0.03955932861795833
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.1836734693877551,
"acc_stderr": 0.024789071332007643,
"acc_norm": 0.1836734693877551,
"acc_norm_stderr": 0.024789071332007643
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.2537313432835821,
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"acc_norm": 0.2537313432835821,
"acc_norm_stderr": 0.03076944496729602
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.14,
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"acc_norm": 0.14,
"acc_norm_stderr": 0.03487350880197772
},
"harness|hendrycksTest-virology|5": {
"acc": 0.24096385542168675,
"acc_stderr": 0.0332939411907353,
"acc_norm": 0.24096385542168675,
"acc_norm_stderr": 0.0332939411907353
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.2046783625730994,
"acc_stderr": 0.030944459778533207,
"acc_norm": 0.2046783625730994,
"acc_norm_stderr": 0.030944459778533207
},
"harness|truthfulqa:mc|0": {
"mc1": 0.2252141982864137,
"mc1_stderr": 0.014623240768023496,
"mc2": 0.37081334738032573,
"mc2_stderr": 0.014356461899633393
},
"harness|winogrande|5": {
"acc": 0.5367008681925809,
"acc_stderr": 0.014014578458843262
},
"harness|gsm8k|5": {
"acc": 0.019711902956785442,
"acc_stderr": 0.0038289829787357134
}
}
```
## 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
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[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. -->
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## Dataset Card Contact
[More Information Needed] |
oserikov/arabic_billion_words_old | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: Arabic Billion Words
dataset_info:
- config_name: Alittihad
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1601790302
num_examples: 349342
download_size: 348259999
dataset_size: 1601790302
- config_name: Almasryalyoum
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1056197870
num_examples: 291723
download_size: 242604438
dataset_size: 1056197870
- config_name: Almustaqbal
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1545659336
num_examples: 446873
download_size: 350826797
dataset_size: 1545659336
- config_name: Alqabas
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2631729746
num_examples: 817274
download_size: 595274646
dataset_size: 2631729746
- config_name: Echoroukonline
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 464386206
num_examples: 139732
download_size: 108184378
dataset_size: 464386206
- config_name: Ryiadh
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3101294859
num_examples: 858188
download_size: 691264971
dataset_size: 3101294859
- config_name: Sabanews
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 198019614
num_examples: 92149
download_size: 38214558
dataset_size: 198019614
- config_name: SaudiYoum
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 2723291416
num_examples: 888068
download_size: 605537923
dataset_size: 2723291416
- config_name: Techreen
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 1103458209
num_examples: 314597
download_size: 252976781
dataset_size: 1103458209
- config_name: Youm7
features:
- name: url
dtype: string
- name: head_line
dtype: string
- name: date
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3004689464
num_examples: 1172136
download_size: 617708074
dataset_size: 3004689464
config_names:
- Alittihad
- Almasryalyoum
- Almustaqbal
- Alqabas
- Echoroukonline
- Ryiadh
- Sabanews
- SaudiYoum
- Techreen
- Youm7
---
# Dataset Card for Arabic Billion Words Corpus
## 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:** http://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus
- **Repository:**
- **Paper:** https://arxiv.org/pdf/1611.04033
- **Leaderboard:**
- **Point of Contact:**[Ibrahim Abu El-Khair](iabuelkhair@gmail.com)
### Dataset Summary
Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML.
**NB:** this dataset is based on the [unofficial copy](https://drive.google.com/drive/folders/1F2wCEfFHzJqX7eTuWhh-pGtrsaHPvTT8?usp=drive_link) ([discussion](https://huggingface.co/datasets/arabic_billion_words/discussions/3)) of the data, and assumes it was downloaded properly. Put the `new_data_*` files to the `./dataset` folder like this:
```
[user@machine /path/to/dataset]$ tree
.
├── arabic_billion_words.py
├── dataset
│ ├── new_data_Alittihad_XML_utf_8.rar
│ ├── new_data_Almasryalyoum_XML_utf_8.rar
│ ├── new_data_Almustaqbal_XML_utf_8.rar
│ ├── new_data_Alqabas_XML_utf_8.rar
│ ├── new_data_Echoroukonline_XML_utf_8.rar
│ ├── new_data_Ryiadh_XML_utf_8.rar
│ ├── new_data_Sabanews_XML_utf_8.rar
│ ├── new_data_SaudiYoum_XML_utf_8.rar
│ ├── new_data_Techreen_XML_utf_8.rar
│ └── new_data_Youm7_XML_utf_8.rar
├── dataset_infos.json
├── README.md
└── usage_example.py
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
This is an example of the "Almasryalyoum" configuration subset:
```python
{
"url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300",
"head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد",
"date": "19/5/2007",
"text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره.
وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري.
يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري.
وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""",
}
```
### Data Fields
The data fields are:
- "url": string, original url of the article,
- "head_line": string, headline of the article,
- "date": string, date of the article,
- "text": string, text content of the article,
### Data Splits
There is only one "training" split for all configuration subsets, containing the following number of examples:
| | Number of examples |
|:---------------|-------------------:|
| Alittihad | 349342 |
| Almasryalyoum | 291723 |
| Almustaqbal | 446873 |
| Alqabas | 817274 |
| Echoroukonline | 139732 |
| Ryiadh | 858188 |
| Sabanews | 92149 |
| SaudiYoum | 888068 |
| Techreen | 314597 |
| Youm7 | 1172136 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
bdsaglam/webnlg-musique-jerx-sft-multi-turn-openai | ---
dataset_info:
features:
- name: chat
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: test
num_bytes: 3038884
num_examples: 3611
- name: dev
num_bytes: 1711885
num_examples: 2217
- name: train
num_bytes: 13679534
num_examples: 17780
download_size: 5388158
dataset_size: 18430303
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: dev
path: data/dev-*
- split: train
path: data/train-*
---
|
finnstrom3693/wordpress_dataset_id_raw_2k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1033562
num_examples: 2000
download_size: 681781
dataset_size: 1033562
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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