datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
shuyuej/prompt_consistency_training | ---
license: apache-2.0
---
# 🚀 Load Dataset
```python
from datasets import load_dataset
dataset = load_dataset("shuyuej/prompt_consistency_training")
dataset = dataset["train"]
print(dataset)
```
|
iwatzon/EconData | ---
dataset_info:
features:
- name: input
dtype: string
- name: context
dtype: string
- name: output
dtype: int64
splits:
- name: train
num_bytes: 4671810
num_examples: 13156
download_size: 1464923
dataset_size: 4671810
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Hobis/bark-polish-semantic-wav-training | ---
language:
- pl
--- |
laion/chirp-v2-dataset | ---
dataset_info:
features:
- name: user_prompt
dtype: string
- name: system_prompt
dtype: string
- name: lyrics
dtype: string
- name: audio
dtype: audio
- name: link
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
splits:
- name: train
num_bytes: 0
num_examples: 0
download_size: 0
dataset_size: 0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Use the Edit dataset card button to edit.
|
florin-hf/nq_open_gold | ---
task_categories:
- question-answering
language:
- en
size_categories:
- 10K<n<100K
---
# Natural Questions Open Dataset with Gold Documents
This dataset is a curated version of the [Natural Questions open dataset](https://huggingface.co/datasets/nq_open),
with the inclusion of the gold documents from the original [Natural Questions](https://huggingface.co/datasets/natural_questions) (NQ) dataset.
The main difference with the NQ-open dataset is that some entries were excluded, as their respective gold documents exceeded 512 tokens in length.
This is due to the pre-processing of the gold documents, as detailed in this related [dataset](https://huggingface.co/datasets/florin-hf/wiki_dump2018_nq_open).
The dataset is designed to facilitate research in question-answering systems, especially focusing on integrating gold documents for training and testing purposes.
## Dataset Sources
The Natural Questions (NQ) dataset is a large-scale collection of real-world queries derived from Google search data. Each
entry in the dataset consists of a user query and the corresponding Wikipedia page containing the answer.
The NQ-open dataset, a subset of the NQ dataset, differs by removing the restriction of linking answers to specific Wikipedia passages, thereby
mimicking a more general information retrieval scenario similar to web searches.
This version of the NQ-open dataset was used in the paper [The Power of Noise: Redefining Retrieval for RAG Systems](https://arxiv.org/abs/2401.14887).
## Dataset Structure
A sample in the dataset has the following format:
```
{
'example_id' (int64): an identifier for the question, consistent with the original NQ dataset,
'question' (str): a question, that is identical to the question in the original NQ,
'answers' (List[str]): the list of correct answers in the original NQ,
'text' (str): gold document, associated with the question, in the original NQ,
'idx_gold_in_corpus' (int64): index of the gold document in the full corpus.
}
Ex.
{
'example_id': -3440030035760311385,
'question': 'who owned the millennium falcon before han solo',
'answers': [Lando Calrissian],
'text': "Han Solo won the Millennium Falcon from Lando Calrissian in the card game ' sabacc ' several years before the events of the film A New Hope..."
'idx_gold_in_corpus': 20995349
}
```
## Dataset Splits
- **Train set**: 72,209 (50,2 MB)
- **Validation set**: 8,006 (5,57 BM)
- **Test set**: 2889 (1,96 MB)
## Citation Information
```
@article{doi:10.1162/tacl\_a\_00276,
author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
title = {Natural Questions: A Benchmark for Question Answering Research},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {453-466},
year = {2019},
doi = {10.1162/tacl\_a\_00276},
URL = {
https://doi.org/10.1162/tacl_a_00276
},
eprint = {
https://doi.org/10.1162/tacl_a_00276
},
abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated sequestered as test data. We present experiments validating quality of the data. We also describe analysis of 25-way annotations on 302 examples, giving insights into human variability on the annotation task. We introduce robust metrics for the purposes of evaluating question answering systems; demonstrate high human upper bounds on these metrics; and establish baseline results using competitive methods drawn from related literature. }
}
@inproceedings{lee-etal-2019-latent,
title = "Latent Retrieval for Weakly Supervised Open Domain Question Answering",
author = "Lee, Kenton and
Chang, Ming-Wei and
Toutanova, Kristina",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1612",
doi = "10.18653/v1/P19-1612",
pages = "6086--6096",
abstract = "Recent work on open domain question answering (QA) assumes strong supervision of the supporting evidence and/or assumes a blackbox information retrieval (IR) system to retrieve evidence candidates. We argue that both are suboptimal, since gold evidence is not always available, and QA is fundamentally different from IR. We show for the first time that it is possible to jointly learn the retriever and reader from question-answer string pairs and without any IR system. In this setting, evidence retrieval from all of Wikipedia is treated as a latent variable. Since this is impractical to learn from scratch, we pre-train the retriever with an Inverse Cloze Task. We evaluate on open versions of five QA datasets. On datasets where the questioner already knows the answer, a traditional IR system such as BM25 is sufficient. On datasets where a user is genuinely seeking an answer, we show that learned retrieval is crucial, outperforming BM25 by up to 19 points in exact match.",
}
@misc{cuconasu2024power,
title={The Power of Noise: Redefining Retrieval for RAG Systems},
author={Florin Cuconasu and Giovanni Trappolini and Federico Siciliano and Simone Filice and Cesare Campagnano and Yoelle Maarek and Nicola Tonellotto and Fabrizio Silvestri},
year={2024},
eprint={2401.14887},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
``` |
thorirhrafn/rmh_subset_large | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2916969362
num_examples: 1128641
- name: test
num_bytes: 40265924
num_examples: 10000
- name: eval
num_bytes: 4719273
num_examples: 2000
download_size: 1808550301
dataset_size: 2961954559
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: eval
path: data/eval-*
---
|
brwac | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: brwac
pretty_name: BrWaC
dataset_info:
features:
- name: doc_id
dtype: string
- name: title
dtype: string
- name: uri
dtype: string
- name: text
sequence:
- name: paragraphs
sequence: string
splits:
- name: train
num_bytes: 18828421452
num_examples: 3530796
download_size: 0
dataset_size: 18828421452
---
# Dataset Card for BrWaC
## 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:** [BrWaC homepage](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC)
- **Repository:** [BrWaC repository](https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC)
- **Paper:** [The brWaC Corpus: A New Open Resource for Brazilian Portuguese](https://www.aclweb.org/anthology/L18-1686/)
- **Point of Contact:** [Jorge A. Wagner Filho](mailto:jawfilho@inf.ufrgs.br)
### Dataset Summary
The BrWaC (Brazilian Portuguese Web as Corpus) is a large corpus constructed following the Wacky framework,
which was made public for research purposes. The current corpus version, released in January 2017, is composed by
3.53 million documents, 2.68 billion tokens and 5.79 million types. Please note that this resource is available
solely for academic research purposes, and you agreed not to use it for any commercial applications.
Manually download at https://www.inf.ufrgs.br/pln/wiki/index.php?title=BrWaC
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Portuguese
## Dataset Structure
### Data Instances
An example from the BrWaC dataset looks as follows:
```
{
"doc_id": "netg-1afc73",
"text": {
"paragraphs": [
[
"Conteúdo recente"
],
[
"ESPUMA MARROM CHAMADA \"NINGUÉM MERECE\""
],
[
"31 de Agosto de 2015, 7:07 , por paulo soavinski - | No one following this article yet."
],
[
"Visualizado 202 vezes"
],
[
"JORNAL ELETRÔNICO DA ILHA DO MEL"
],
[
"Uma espuma marrom escuro tem aparecido com frequência na Praia de Fora.",
"Na faixa de areia ela aparece disseminada e não chama muito a atenção.",
"No Buraco do Aipo, com muitas pedras, ela aparece concentrada.",
"É fácil saber que esta espuma estranha está lá, quando venta.",
"Pequenos algodões de espuma começam a flutuar no espaço, pertinho da Praia do Saquinho.",
"Quem pode ajudar na coleta deste material, envio a laboratório renomado e pagamento de análises, favor entrar em contato com o site."
]
]
},
"title": "ESPUMA MARROM CHAMADA ‟NINGUÃÂM MERECE‟ - paulo soavinski",
"uri": "http://blogoosfero.cc/ilhadomel/pousadasilhadomel.com.br/espuma-marrom-chamada-ninguem-merece"
}
```
### Data Fields
- `doc_id`: The document ID
- `title`: The document title
- `uri`: URI where the document was extracted from
- `text`: A list of document paragraphs (with a list of sentences in it as a list of strings)
### Data Splits
The data is only split into train set with size of 3530796 samples.
## 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
```
@inproceedings{wagner2018brwac,
title={The brwac corpus: A new open resource for brazilian portuguese},
author={Wagner Filho, Jorge A and Wilkens, Rodrigo and Idiart, Marco and Villavicencio, Aline},
booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year={2018}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. |
PranavVerma-droid/manifesto | ---
license: mit
language:
- en
tags:
- llama2
size_categories:
- 1K<n<10K
task_categories:
- text-classification
- text-generation
---
# Manifesto DB
This is a General-Purpose Dataset. This Includes Information About Math, Real Word Events, Science, Instructions to Do Things in Real Life, etc.
This Database has No Foul Language or Spilled Data, it is completely safe and open-source to use!
Written by [PranavVerma-droid](https://portfolio.craftingrealm.tk) <br>
This Code is Licensed, Please Use With Crediting the Owner. |
fabiochiu/kira-dog | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 1300183.0
num_examples: 5
download_size: 1301094
dataset_size: 1300183.0
---
# Dataset Card for "kira-dog"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cellfabrik/cultured_meat | ---
license: apache-2.0
---
|
QuangDuy/1024token | ---
dataset_info:
features:
- name: Context
dtype: string
- name: Statement
dtype: string
- name: labels
dtype: string
- name: text
dtype: string
splits:
- name: dataset
num_bytes: 12788903
num_examples: 1447
download_size: 5015823
dataset_size: 12788903
configs:
- config_name: default
data_files:
- split: dataset
path: data/dataset-*
---
|
Nexdata/1990000_Groups_Chinese_Czech_Parallel_Corpus_Data | ---
license: cc-by-nc-nd-4.0
---
## Description
1,990,000 sets of Chinese and Czech language parallel translation corpus, data storage format is txt document. Data cleaning, desensitization, and quality inspection have been carried out, which can be used as a basic corpus for text data analysis and in fields such as machine translation.
For more details, please refer to the link: https://www.nexdata.ai/dataset/1336?source=Huggingface
## Storage format
TXT
## Data content
Chinese-Czech Parallel Corpus Data, content has been preliminarily categorized, covering the fields of technology, healthcare, tourism, spoken, news and military.
## Data size
1.99 million pairs of Chinese-Czech Parallel Corpus Data.
## Language
Chinese, Czech
## Application scenario
machine translation
# Licensing Information
Commercial License
|
distilled-from-one-sec-cv12/chunk_93 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1268086408
num_examples: 247094
download_size: 1295684952
dataset_size: 1268086408
---
# Dataset Card for "chunk_93"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/sheeda_fireemblem | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sheeda (Fire Emblem)
This is the dataset of sheeda (Fire Emblem), containing 427 images and their tags.
The core tags of this character are `blue_hair, long_hair, blue_eyes, breasts, large_breasts, 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 | 427 | 510.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 427 | 315.76 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 961 | 630.26 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 427 | 463.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 961 | 846.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sheeda_fireemblem/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/sheeda_fireemblem',
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 | 18 |  |  |  |  |  | 1girl, navel, nipples, blush, solo, looking_at_viewer, open_mouth, completely_nude, collarbone, pussy, sitting, smile, censored |
| 1 | 8 |  |  |  |  |  | 1girl, elbow_gloves, pegasus_knight_uniform_(fire_emblem), smile, solo, simple_background, belt, breastplate, thighhighs, zettai_ryouiki, fingerless_gloves, looking_at_viewer, shoulder_armor, side_slit, white_background, armored_dress |
| 2 | 7 |  |  |  |  |  | 1girl, blush, elbow_gloves, looking_at_viewer, red_dress, short_dress, white_gloves, shoulder_armor, solo, thighs, black_thighhighs, boots, breastplate, pantyshot, pegasus_knight_uniform_(fire_emblem), short_sleeves, white_panties, embarrassed, from_behind, looking_back, upskirt, garter_straps, holding, simple_background, white_background |
| 3 | 5 |  |  |  |  |  | 1girl, red_dress, solo, thighs, blush, closed_mouth, hair_between_eyes, looking_at_viewer, short_dress, short_sleeves, white_panties, ass, cameltoe, partially_visible_vulva, sitting, smile, black_thighhighs, heart, impossible_clothes, on_back, spread_legs |
| 4 | 10 |  |  |  |  |  | 1girl, hat, solo, simple_background, smile, white_background, boots, bracelet, looking_at_viewer, pantyhose, white_dress, cape, holding_book, elbow_gloves, full_body, medium_breasts, open_mouth, shiny_hair, white_footwear, white_gloves |
| 5 | 15 |  |  |  |  |  | 1girl, bride, wedding_dress, white_dress, hair_flower, smile, bouquet, solo, bare_shoulders, gloves, blush, simple_background, open_mouth, bridal_veil, looking_at_viewer, strapless, official_alternate_costume |
| 6 | 7 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, red_bikini, smile, solo, closed_mouth, official_alternate_costume, blush, navel, simple_background, white_background, cowboy_shot, bare_shoulders, belt, bracelet, holding_staff, midriff, see-through, skirt |
| 7 | 5 |  |  |  |  |  | 1girl, hair_flower, looking_at_viewer, navel, official_alternate_costume, red_bikini, solo, beach, bracelet, open_mouth, water, blush, smile, day, lying, outdoors, sky |
| 8 | 7 |  |  |  |  |  | 1girl, hetero, nipples, solo_focus, 3boys, blush, gangbang, multiple_penises, vaginal, handjob, mosaic_censoring, open_mouth, thighhighs, cum_in_pussy, facial, medium_breasts, torn_clothes, bukkake, closed_eyes, cowgirl_position, cum_on_breasts, elbow_gloves, fellatio, white_gloves |
| 9 | 10 |  |  |  |  |  | 1boy, 1girl, blush, hetero, nipples, open_mouth, solo_focus, sweat, navel, penis, cum_in_pussy, vaginal, collarbone, mosaic_censoring, sex_from_behind, thighhighs, breast_grab, completely_nude, grabbing_from_behind, hair_ornament, heart-shaped_pupils, spread_legs, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | navel | nipples | blush | solo | looking_at_viewer | open_mouth | completely_nude | collarbone | pussy | sitting | smile | censored | elbow_gloves | pegasus_knight_uniform_(fire_emblem) | simple_background | belt | breastplate | thighhighs | zettai_ryouiki | fingerless_gloves | shoulder_armor | side_slit | white_background | armored_dress | red_dress | short_dress | white_gloves | thighs | black_thighhighs | boots | pantyshot | short_sleeves | white_panties | embarrassed | from_behind | looking_back | upskirt | garter_straps | holding | closed_mouth | hair_between_eyes | ass | cameltoe | partially_visible_vulva | heart | impossible_clothes | on_back | spread_legs | hat | bracelet | pantyhose | white_dress | cape | holding_book | full_body | medium_breasts | shiny_hair | white_footwear | bride | wedding_dress | hair_flower | bouquet | bare_shoulders | gloves | bridal_veil | strapless | official_alternate_costume | red_bikini | cowboy_shot | holding_staff | midriff | see-through | skirt | beach | water | day | lying | outdoors | sky | hetero | solo_focus | 3boys | gangbang | multiple_penises | vaginal | handjob | mosaic_censoring | cum_in_pussy | facial | torn_clothes | bukkake | closed_eyes | cowgirl_position | cum_on_breasts | fellatio | 1boy | sweat | penis | sex_from_behind | breast_grab | grabbing_from_behind | hair_ornament | heart-shaped_pupils | standing |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:--------|:----------|:--------|:-------|:--------------------|:-------------|:------------------|:-------------|:--------|:----------|:--------|:-----------|:---------------|:---------------------------------------|:--------------------|:-------|:--------------|:-------------|:-----------------|:--------------------|:-----------------|:------------|:-------------------|:----------------|:------------|:--------------|:---------------|:---------|:-------------------|:--------|:------------|:----------------|:----------------|:--------------|:--------------|:---------------|:----------|:----------------|:----------|:---------------|:--------------------|:------|:-----------|:--------------------------|:--------|:---------------------|:----------|:--------------|:------|:-----------|:------------|:--------------|:-------|:---------------|:------------|:-----------------|:-------------|:-----------------|:--------|:----------------|:--------------|:----------|:-----------------|:---------|:--------------|:------------|:-----------------------------|:-------------|:--------------|:----------------|:----------|:--------------|:--------|:--------|:--------|:------|:--------|:-----------|:------|:---------|:-------------|:--------|:-----------|:-------------------|:----------|:----------|:-------------------|:---------------|:---------|:---------------|:----------|:--------------|:-------------------|:-----------------|:-----------|:-------|:--------|:--------|:------------------|:--------------|:-----------------------|:----------------|:----------------------|:-----------|
| 0 | 18 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | 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 | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 15 |  |  |  |  |  | X | | | X | X | X | X | | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | X | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | | X | X | X | X | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | X | | | | | | X | X | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 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 | | | | | | | | | |
| 9 | 10 |  |  |  |  |  | X | X | X | X | | | X | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | X | | X | X | | | | | | | | X | X | X | X | X | X | X | X | X |
|
coralexbadea/sidewalk-imagery | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 86083036.0
num_examples: 10
download_size: 3930138
dataset_size: 86083036.0
---
# Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bakhitovd/covid_breathing_deep | ---
license: mit
---
|
e-mohammadii/adsfddb | ---
license: afl-3.0
---
|
joey234/mmlu-computer_security-neg-prepend | ---
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
- name: neg_prompt
dtype: string
- name: fewshot_context_neg
dtype: string
- name: fewshot_context_ori
dtype: string
splits:
- name: dev
num_bytes: 6196
num_examples: 5
- name: test
num_bytes: 687108
num_examples: 100
download_size: 128252
dataset_size: 693304
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "mmlu-computer_security-neg-prepend"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fightfei/llama2-path-concentration-1.5k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 600075.1011080332
num_examples: 1429
- name: test
num_bytes: 6298.898891966759
num_examples: 15
download_size: 78423
dataset_size: 606374.0
---
# Dataset Card for "llama2-path-concentration-1.5k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-staging-eval-emotion-default-a0d22e-17376346 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- emotion
eval_info:
task: multi_class_classification
model: autoevaluate/multi-class-classification-not-evaluated
metrics: []
dataset_name: emotion
dataset_config: default
dataset_split: test
col_mapping:
text: text
target: label
---
# 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: autoevaluate/multi-class-classification-not-evaluated
* Dataset: emotion
* Config: default
* Split: test
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. |
Dstycoon/trial8 | ---
dataset_info:
features:
- name: data
dtype: string
- name: conversation
dtype: string
- name: predicted_disease
dtype: string
- name: rationale
dtype: string
splits:
- name: train
num_bytes: 56747
num_examples: 10
download_size: 61186
dataset_size: 56747
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "trial8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
bishad/gsplat | ---
license: apache-2.0
---
|
BangumiBase/kochikame | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Kochikame
This is the image base of bangumi Kochikame, we detected 85 characters, 22061 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 | 14049 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 336 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 396 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 159 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 115 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 178 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 26 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 57 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 126 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 27 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 138 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 39 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 215 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 126 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 107 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 465 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 47 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 56 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 36 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 1290 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 27 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 56 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 50 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 40 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 21 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 20 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 36 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 45 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 60 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 170 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 55 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 38 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 146 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 22 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 39 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 72 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 28 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 44 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 14 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 270 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 25 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 83 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 68 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 35 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 39 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 47 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 41 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 22 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 31 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 24 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 105 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 28 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 39 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 34 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 17 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 30 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 46 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 58 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 951 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 26 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 26 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 194 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 24 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 37 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 19 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 57 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 28 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 20 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 11 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 118 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 16 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 27 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 21 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 22 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 55 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 66 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 17 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 32 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 25 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 10 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 47 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 21 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 16 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 14 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 148 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
wavpub/JinJinLeDao_QA_Dataset | ---
task_categories:
- question-answering
- text-generation
language:
- zh
pretty_name: JinJinLeDao QA Dataset
size_categories:
- 10K<n<100K
---
# JinJinLeDao QA Dataset
## Dataset Description
**Repository**: https://github.com/tech-podcasts/JinJinLeDao_QA_Dataset
**HuggingFace**: https://huggingface.co/datasets/wavpub/JinJinLeDao_QA_Dataset
### Dataset Summary
The dataset contains over 18,000 Chinese question-answer pairs extracted from 281 episodes of the Chinese podcast "[JinJinLeDao](https://dao.fm/)". The subtitles were extracted using the OpenAI Whisper transcription tool, and the question-answer pairs were generated using GPT-3.5 by dividing the subtitles into blocks and prompting the model to generate questions and answers.
### Supported Tasks and Leaderboards
This dataset can be used for various natural language processing tasks, such as question answering and text generation, among others.
### Languages
The dataset is in Chinese (Mandarin).
## Dataset Structure
### Data Instances
The dataset contains over 18,000 question-answer pairs.
### Data Fields
Each data instance contains the following fields:
question: The generated question based on the text block.
answer: The corresponding answer to the generated question.
episode: The title of the podcast episode from which the question-answer pair was extracted.
podcast: The name of the specific program within the "[JinJinLeDao](https://dao.fm/)" podcast where the episode was featured.
### Data Splits
The dataset does not have predefined splits. Users can split the data according to their own requirements.
## Dataset Creation
### Curation Rationale
The dataset was created to provide a resource for Chinese language natural language processing research.
### Source Data
#### Initial Data Collection and Normalization
The source data consists of 281 episodes of the Chinese podcast "[JinJinLeDao](https://dao.fm/)", which were transcribed using the OpenAI Whisper transcription tool.
#### Who are the source language producers?
The source language producers are the hosts of the "[JinJinLeDao](https://dao.fm/)" podcast.
### Annotations
#### Annotation process
The dataset was annotated using an automated process, in which GPT-3.5 was used to generate questions and answers based on text prompts.
#### Who are the annotators?
The initial annotation of the dataset was carried out through an automated process, without the involvement of human annotators. However, we later introduced a manual correction step to improve the accuracy of the data, and we would like to express our gratitude to [Chunhui Gao](https://www.ipip.net/) for taking the time to assist us with this task.
### Personal and Sensitive Information
The dataset does not contain any personal or sensitive information, except for some user names mentioned in the audio content.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset was created for academic and research purposes only.
### Discussion of Biases
As the dataset was generated using an automated process, there may be biases in the generated questions and answers.
### Other Known Limitations
The dataset was generated using an automated process, which may result in lower quality data compared to manually annotated datasets.
## Additional Information
### Dataset Curators
The dataset was curated [JinJinLeDao](https://dao.fm/) and [Hongyang Jin](https://github.com/GanymedeNil).
### Licensing Information
The dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.
### Citation Information
If you use this dataset in your research, please cite the following paper:
N/A
### Contributions
Thanks to [JinJinLeDao](https://dao.fm/) for providing the data and to [Hongyang Jin](https://github.com/GanymedeNil) for curating and sharing this dataset.We would also like to express our gratitude to [Chunhui Gao](https://www.ipip.net/) for his assistance in improving the accuracy of the data. |
eunbinni/ola_polyglot_5.8B_t1_data | ---
dataset_info:
features:
- name: input
dtype: string
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 691281335
num_examples: 580812
download_size: 399933748
dataset_size: 691281335
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "ola_polyglot_5.8B_t1_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mteb/cqadupstack-mathematica | ---
language:
- en
multilinguality:
- monolingual
task_categories:
- text-retrieval
source_datasets:
- cqadupstack-mathematica
task_ids:
- document-retrieval
config_names:
- corpus
tags:
- text-retrieval
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: float64
splits:
- name: test
num_bytes: 34691
num_examples: 1358
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_bytes: 19568620
num_examples: 16705
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 49576
num_examples: 804
configs:
- config_name: default
data_files:
- split: test
path: qrels/test.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
--- |
pharaouk/CoT-Collection | ---
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
language:
- en
size_categories:
- 1M<n<10M
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:https://github.com/kaistAI/CoT-Collection**
- **Repository:https://github.com/kaistAI/CoT-Collection**
- **Paper:https://arxiv.org/abs/2305.14045**
- **Point of Contact:seungone@kaist.ac.kr**
### Dataset Summary
The CoT Collection is a dataset designed to induce Chain-of-Thought (CoT) capabilities into language models.
While proprietary LLMs excel at generating Chain-of-Thoughts based on prompting, smaller LMs do not have this capability. Thus, by fine-tuning to generate Chain-of-Thoughts, it could acquire such abilities.
The CoT Collection provides 1.84 million Chain-of-Thoughts augmented across 1060 tasks from the Flan Collection.\\
Experimental results show that fine-tuning on the CoT Collection results in (1) better zero-shot performance and (2) a better base model for few-shot learning.
We also provide a multilingual version of CoT Collection at this [link](https://huggingface.co/datasets/kaist-ai/Multilingual-CoT-Collection).
### Supported Tasks and Leaderboards
1060 tasks chosen from the Flan Collection.
The list of categories within the CoT Collection are:
* Natural Language Inference
* Extractive Question Answering
* Closed Book Question Answering
* Science
* Toxic Classification
* Arithmetic
* Program Execution
* Dialogue
* Ethics
* Commonsense Reasoning
* Multiple Choice Question Answering
### Languages
English
## Dataset Structure
* source: The input that is given to the language model (LM).
* target: The ground truth answer to the source.
* rationale: The Chain of Thought (CoT) that explains how the target could be derived from the source.
* task: A category that shows which dataset the source and target was extracted from.
In our paper, we trained the underlying language model to generate in the following format:
```
\{rationale\}
[RESULT]
\{target\}
```
Then during evaluation, we parsed the prediction after the phrase ```[RESULT]```.
### Data Splits
| name | train |
|-------------------|------:|
|CoT-Collection|1837928|
### Citation Information
If you find the following model helpful, please considering citing our paper!
```
@article{kim2023cot,
title={The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning},
author={Kim, Seungone and Joo, Se June and Kim, Doyoung and Jang, Joel and Ye, Seonghyeon and Shin, Jamin and Seo, Minjoon},
journal={arXiv preprint arXiv:2305.14045},
year={2023}
}
``` |
bdsaglam/web_nlg-erx-sft-multi-turn-sharegpt | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 12108226
num_examples: 11752
- name: dev
num_bytes: 1530347
num_examples: 1484
- name: test
num_bytes: 2752494
num_examples: 2460
download_size: 5230058
dataset_size: 16391067
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
|
FahedShadid/tshirt-captions | ---
license: cc-by-nc-4.0
---
|
davidfant/natural-questions-chunk-10 | ---
dataset_info:
features:
- name: id
dtype: string
- name: document
struct:
- name: html
dtype: string
- name: title
dtype: string
- name: tokens
sequence:
- name: end_byte
dtype: int64
- name: is_html
dtype: bool
- name: start_byte
dtype: int64
- name: token
dtype: string
- name: url
dtype: string
- name: question
struct:
- name: text
dtype: string
- name: tokens
sequence: string
- name: long_answer_candidates
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: top_level
dtype: bool
- name: annotations
sequence:
- name: id
dtype: string
- name: long_answer
struct:
- name: candidate_index
dtype: int64
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: short_answers
sequence:
- name: end_byte
dtype: int64
- name: end_token
dtype: int64
- name: start_byte
dtype: int64
- name: start_token
dtype: int64
- name: text
dtype: string
- name: yes_no_answer
dtype:
class_label:
names:
'0': 'NO'
'1': 'YES'
splits:
- name: train
num_bytes: 4621850979
num_examples: 10000
download_size: 1795009419
dataset_size: 4621850979
---
# Dataset Card for "natural-questions-chunk-10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Nebulous/gpt4all_pruned | ---
license: cc
---
Pruned gpt4all dataset meant to reduce annoying behvaiors and nonsensical prompts |
ashwathjadhav23/Spanish_MLM_6 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 2305167
num_examples: 24999
download_size: 1495017
dataset_size: 2305167
---
# Dataset Card for "Spanish_MLM_6"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
qazisaad/llama_2_product_titles-esci_train-temp-neg | ---
dataset_info:
features:
- name: index
dtype: int64
- name: query
dtype: string
- name: average_score
dtype: float64
- name: total_score
dtype: float64
- name: text
dtype: string
- name: preds
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 1894756
num_examples: 960
download_size: 289567
dataset_size: 1894756
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama_2_product_titles-esci_train-temp-neg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
felixz/open_leaderboard_dataset | ---
license: apache-2.0
---
|
erkam/clevr-full-v3 | ---
dataset_info:
features:
- name: target_img
dtype: image
- name: source_img
dtype: image
- name: target_layout
dtype: image
- name: target_obj
sequence: int64
- name: source_obj
sequence: int64
- name: target_box
sequence:
sequence: float32
- name: source_box
sequence:
sequence: float32
- name: target_tri
sequence:
sequence: int64
- name: source_tri
sequence:
sequence: int64
splits:
- name: test
num_bytes: 15623406.0
num_examples: 119
- name: train
num_bytes: 126305058.0
num_examples: 960
- name: val
num_bytes: 15746167.0
num_examples: 119
download_size: 156084556
dataset_size: 157674631.0
---
# Dataset Card for "clevr-full-v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CrypticMax/twitter_dataset_1712687552 | ---
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: 18718
num_examples: 43
download_size: 15558
dataset_size: 18718
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
izou3/Food-Prototype-Bruce | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 74206828.0
num_examples: 400
download_size: 73784241
dataset_size: 74206828.0
---
# Dataset Card for "Food-Prototype-Bruce"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Seanxh/twitter_dataset_1713191047 | ---
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: 45430
num_examples: 104
download_size: 21534
dataset_size: 45430
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
limingcv/MultiGen-20M_depth_eval | ---
dataset_info:
features:
- name: image
dtype: image
- name: control_depth
dtype: image
- name: text
dtype: string
splits:
- name: validation
num_bytes: 2170804093.0
num_examples: 5000
download_size: 2168117335
dataset_size: 2170804093.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
lmms-lab/MMBench_EN | ---
dataset_info:
features:
- name: index
dtype: int64
- name: question
dtype: string
- name: hint
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: answer
dtype: string
- name: category
dtype: string
- name: image
dtype: image
- name: source
dtype: string
- name: l2-category
dtype: string
- name: comment
dtype: string
- name: split
dtype: string
splits:
- name: dev
num_bytes: 103845260.875
num_examples: 4377
- name: test
num_bytes: 149612780.25
num_examples: 6718
download_size: 240192616
dataset_size: 253458041.125
configs:
- config_name: default
data_files:
- split: dev
path: data/dev-*
- split: test
path: data/test-*
---
# Dataset Card for "MMBench_EN"
<p align="center" width="100%">
<img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%">
</p>
# Large-scale Multi-modality Models Evaluation Suite
> Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval`
🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab)
# This Dataset
This is a formatted version of the English subset of [MMBench](https://arxiv.org/abs/2307.06281). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models.
```
@article{MMBench,
author = {Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, Dahua Lin},
journal = {arXiv:2307.06281},
title = {MMBench: Is Your Multi-modal Model an All-around Player?},
year = {2023},
}
```
|
realshyfox/DStyle500 | ---
license: llama2
---
|
NobuLuis/zeein | ---
license: other
---
|
Korrie/Pokemon_Images | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 8631269.0
num_examples: 833
download_size: 8458136
dataset_size: 8631269.0
---
# Dataset Card for "Pokemon_Images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
zqz979/meta-review | ---
task_categories:
- summarization
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Meta-Review dataset is a dataset created based on the ORSUM dataset proposed in the paper "Meta-review Generation with Checklist-guided Iterative Introspection" by Zeng et al. Downloaded from their official GitHub Repo: https://github.com/Mankeerat/orsum-meta-review-generation
### Supported Tasks and Leaderboards
Multi-Document Summarization
### Languages
English
## 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] |
nielsr/datacomp_small_basic_filtering | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: uid
dtype: string
- name: url
dtype: string
- name: text
dtype: string
- name: original_width
dtype: int64
- name: original_height
dtype: int64
- name: clip_b32_similarity_score
dtype: float32
- name: clip_l14_similarity_score
dtype: float32
- name: face_bboxes
sequence:
sequence: float64
- name: sha256
dtype: string
- name: detected_language
dtype: string
splits:
- name: train
num_bytes: 1213734175.4314582
num_examples: 3781297
download_size: 981179220
dataset_size: 1213734175.4314582
---
# Dataset Card for "datacomp_small_basic_filtering"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SUSTech/ultrachat_ppl | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: ppl
sequence: float64
splits:
- name: large
num_bytes: 311222566
num_examples: 46389
- name: small
num_bytes: 286125221
num_examples: 46389
- name: random
num_bytes: 279755132
num_examples: 46389
download_size: 464073008
dataset_size: 877102919
configs:
- config_name: default
data_files:
- split: large
path: data/large-*
- split: small
path: data/small-*
- split: random
path: data/random-*
---
|
tungnd/climax_ckpts | ---
license: mit
---
|
liuyanchen1015/MULTI_VALUE_cola_degree_adj_for_adv | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 67
num_examples: 1
- name: test
num_bytes: 51
num_examples: 1
- name: train
num_bytes: 1814
num_examples: 21
download_size: 6648
dataset_size: 1932
---
# Dataset Card for "MULTI_VALUE_cola_degree_adj_for_adv"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
taesiri/GameplayCaptionsRankings | ---
license: mit
---
|
arcee-ai/code-retriever-query-passage-pairs | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: function_name
dtype: string
- name: query
dtype: string
- name: passage
dtype: string
splits:
- name: train
num_bytes: 426521305
num_examples: 245261
download_size: 184574807
dataset_size: 426521305
---
|
CyberHarem/maribel_hearn_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of maribel_hearn/マエリベリーハーン/마에리베리한 (Touhou)
This is the dataset of maribel_hearn/マエリベリーハーン/마에리베리한 (Touhou), containing 63 images and their tags.
The core tags of this character are `hat, blonde_hair, mob_cap, ribbon, purple_eyes, white_headwear, long_hair, bangs, bow, 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 | 63 | 83.11 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maribel_hearn_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 63 | 50.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maribel_hearn_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 151 | 107.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maribel_hearn_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 63 | 74.37 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maribel_hearn_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 151 | 139.49 MiB | [Download](https://huggingface.co/datasets/CyberHarem/maribel_hearn_touhou/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/maribel_hearn_touhou',
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, purple_dress, smile, looking_at_viewer, long_sleeves, yellow_eyes |
| 1 | 8 |  |  |  |  |  | 1girl, blush, looking_at_viewer, purple_dress, solo, neck_ribbon, red_ribbon, hair_between_eyes, upper_body, breasts, frills, open_mouth, smile, closed_mouth, collarbone, juliet_sleeves, simple_background |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | solo | purple_dress | smile | looking_at_viewer | long_sleeves | yellow_eyes | blush | neck_ribbon | red_ribbon | hair_between_eyes | upper_body | breasts | frills | open_mouth | closed_mouth | collarbone | juliet_sleeves | simple_background |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-------|:---------------|:--------|:--------------------|:---------------|:--------------|:--------|:--------------|:-------------|:--------------------|:-------------|:----------|:---------|:-------------|:---------------|:-------------|:-----------------|:--------------------|
| 0 | 16 |  |  |  |  |  | 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 |
|
IsaNLP/RuSRL | ---
language:
- ru
multilinguality:
- monolingual
annotations_creators:
- expert-generated
pretty_name: RuSRL
license: cc-by-nc-4.0
task_categories:
- token-classification
subtasks:
- semantic-role-labeling
- parsing
tags:
- semantic-role-labeling
- syntax-parsing
- tokenization
size_categories:
- 1K<n<10K
---
# Dataset Card for RuSRL
## Dataset Summary
This dataset contains annotations of semantic frames and intra-frame syntax for 1500 Russian sentences.
### Dataset Description
Each sentence is annotated with predicate-argument structures. Syntactic information is also provided for each frame.
```
{
"sent_id": 1404,
"tokens": ["в", "такой", "ситуации", "основные", "метеоэлементы",
"-", "температура", ",", "влажность", ",", "давление", "-",
"претерпевают", "малые", "суточные", "изменения", "."],
"synt_head": [12, 2, 0, 4, 12, -1, 4, -1, 6, -1, 8, -1, -1, 15, 15, 12, -1],
"sem_head": [-1, -1, -1, -1, 12, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 12, -1],
"sem_role": ["_", "_", "_", "_", "субъект", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "предикат", "_"]
}
```
- **Language:** Russian
- **Size:** 1500 sentences
## Citation
```
@inproceedings{shelmanov2014methods,
title={Methods for semantic role labeling of Russian texts},
author={Shelmanov, AO and Smirnov, IV},
booktitle={Computational Linguistics and Intellectual Technologies: Papers from the Annual International Conference Dialogue},
volume={13},
number={20},
pages={607--620},
year={2014}
}
``` |
nmd2k/multi-task-instruction | ---
license: mit
task_categories:
- text-generation
tags:
- code
size_categories:
- 100K<n<1M
--- |
open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1 | ---
pretty_name: Evaluation run of lmsys/vicuna-7b-delta-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [lmsys/vicuna-7b-delta-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 3 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-12T14:40:56.820234](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1/blob/main/results_2023-10-12T14-40-56.820234.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\": 0.11388422818791946,\n\
\ \"em_stderr\": 0.00325324428862373,\n \"f1\": 0.16976719798657605,\n\
\ \"f1_stderr\": 0.003380156230610554,\n \"acc\": 0.38244753834582057,\n\
\ \"acc_stderr\": 0.009528517622122097\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.11388422818791946,\n \"em_stderr\": 0.00325324428862373,\n\
\ \"f1\": 0.16976719798657605,\n \"f1_stderr\": 0.003380156230610554\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.05534495830174375,\n \
\ \"acc_stderr\": 0.006298221796179588\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7095501183898973,\n \"acc_stderr\": 0.012758813448064607\n\
\ }\n}\n```"
repo_url: https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|arc:challenge|25_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|arc:challenge|25_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_12T14_40_56.820234
path:
- '**/details_harness|drop|3_2023-10-12T14-40-56.820234.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-12T14-40-56.820234.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_12T14_40_56.820234
path:
- '**/details_harness|gsm8k|5_2023-10-12T14-40-56.820234.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-12T14-40-56.820234.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hellaswag|10_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hellaswag|10_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:22:17.969682.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T12:35:58.134991.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-18T12:22:17.969682.parquet'
- split: 2023_08_03T12_35_58.134991
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-03T12:35:58.134991.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-03T12:35:58.134991.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_12T14_40_56.820234
path:
- '**/details_harness|winogrande|5_2023-10-12T14-40-56.820234.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-12T14-40-56.820234.parquet'
- config_name: results
data_files:
- split: 2023_07_18T12_22_17.969682
path:
- results_2023-07-18T12:22:17.969682.parquet
- split: 2023_08_03T12_35_58.134991
path:
- results_2023-08-03T12:35:58.134991.parquet
- split: 2023_10_12T14_40_56.820234
path:
- results_2023-10-12T14-40-56.820234.parquet
- split: latest
path:
- results_2023-10-12T14-40-56.820234.parquet
---
# Dataset Card for Evaluation run of lmsys/vicuna-7b-delta-v1.1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/lmsys/vicuna-7b-delta-v1.1
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [lmsys/vicuna-7b-delta-v1.1](https://huggingface.co/lmsys/vicuna-7b-delta-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 3 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-12T14:40:56.820234](https://huggingface.co/datasets/open-llm-leaderboard/details_lmsys__vicuna-7b-delta-v1.1/blob/main/results_2023-10-12T14-40-56.820234.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": 0.11388422818791946,
"em_stderr": 0.00325324428862373,
"f1": 0.16976719798657605,
"f1_stderr": 0.003380156230610554,
"acc": 0.38244753834582057,
"acc_stderr": 0.009528517622122097
},
"harness|drop|3": {
"em": 0.11388422818791946,
"em_stderr": 0.00325324428862373,
"f1": 0.16976719798657605,
"f1_stderr": 0.003380156230610554
},
"harness|gsm8k|5": {
"acc": 0.05534495830174375,
"acc_stderr": 0.006298221796179588
},
"harness|winogrande|5": {
"acc": 0.7095501183898973,
"acc_stderr": 0.012758813448064607
}
}
```
### 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] |
pvduy/airoboros_preference_synthesis_vicuna_oa | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: selected
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 374113692
num_examples: 200364
- name: test
num_bytes: 27548381
num_examples: 16221
download_size: 229848900
dataset_size: 401662073
---
# Dataset Card for "airoboros_preference_synthesis_vicuna_oa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vincentmin/eli5_rlhf | ---
task_categories:
- conversational
- text2text-generation
- text-generation
- question-answering
language:
- en
tags:
- rlhf
- reinforcement learning from human feedback
pretty_name: >-
Reddit Explain Like I am Five dataset for Reinforcement Learning from Human
Feedback
size_categories:
- 1M<n<10M
---
ELI5 paired
This is a processed version of the [eli5](https://huggingface.co/datasets/eli5) dataset. The dataset was created following very closely the steps in the [stack-exchange-paired dataset](https://huggingface.co/datasets/lvwerra/stack-exchange-paired). The following steps were applied:
- Create pairs (response_j, response_k) where j was rated better than k
- Sample at most 10 pairs per question
- Shuffle the dataset globally
This dataset is designed to be used for preference learning using techniques such as Reinforcement Learning from Human Feedback. The processing notebook is in the repository as well.
If you want to construct a "question" column in this data, you can either use just the "title" column, or concatenate the "title" column with the "selftext" column as follows:
```
def get_question(example):
title = example["title"]
selftext = example["selftext"]
if selftext:
if selftext[-1] not in [".", "?", "!"]:
seperator = ". "
else:
seperator = " "
question = title + seperator + selftext
else:
question = title
example["question"] = question
return example
dataset = load_dataset("vincentmin/eli5_askscience_askhistorians_rlhf")
dataset = dataset.map(get_question)
```
For the license, see the [eli5 dataset](https://huggingface.co/datasets/eli5) which states
"The licensing status of the dataset hinges on the legal status of the Pushshift.io data which is unclear."
at the time of creation of this dataset. |
sam9033/srkvoice | ---
license: other
---
|
liuyanchen1015/VALUE_qnli_dey_it | ---
dataset_info:
features:
- name: question
dtype: string
- name: sentence
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: dev
num_bytes: 57471
num_examples: 198
- name: test
num_bytes: 53168
num_examples: 190
- name: train
num_bytes: 935316
num_examples: 3508
download_size: 591641
dataset_size: 1045955
---
# Dataset Card for "VALUE_qnli_dey_it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pradeep239/Donut_sample10_testing | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: ground_truth
dtype: string
splits:
- name: train
num_bytes: 11511796.0
num_examples: 17
- name: validation
num_bytes: 912196.0
num_examples: 2
- name: test
num_bytes: 1092203.0
num_examples: 1
download_size: 10065911
dataset_size: 13516195.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
nluai/mini | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: validation
num_bytes: 1313
num_examples: 6
download_size: 3040
dataset_size: 1313
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
|
Shaier/CILM_data | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
- name: embedding
sequence: float32
length: 768
- name: embedding_index
dtype: int32
- name: pubmed_id
dtype: int32
splits:
- name: train
num_bytes: 84220525361.0
num_examples: 20364481
- name: validation
num_bytes: 339634903.0
num_examples: 104253
- name: test
num_bytes: 339791727.0
num_examples: 104277
download_size: 13219414702
dataset_size: 84899951991.0
---
# Dataset Card for "CILM_data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
esahit/coral-health-classification | ---
license: cc
---
# Coral health classification dataset
This dataset is a combination of three coral image datasets:
* Bleached and unbleached dataset (BU) by Jamil et al. [1]
* Bleached, healthy and dead dataset (BHD) by Jamil et al. [1]
* Dead subset of the EILAT dataset [2]
The labels of each image and source dataset is included in the filename i.e. "label_dataset_ID.png".
## More info about the dataset:
* Number of images: 1599
* Number of classes: 3 (healthy, unhealthy, dead)
* Healthy
* Number of images: 661
* 124 from BU
* 537 from BHD
* Unhealthy (bleached)
* Number of images: 508
* 134 from BU
* 374 from BHD
* Dead
* Number of images: 430
* 150 from BHD
* 280 from EILAT
---
References
[1] S. Jamil, M. Rahman, and A. Haider, “Bag of Features (BOF) based deep learning framework for bleached corals detection,” Big Data and Cognitive Computing, vol. 5, no. 4, p. 53, Oct. 2021, doi: 10.3390/bdcc5040053.
[2] A. Shihavuddin, N. Gracias, R. García, A. C. R. Gleason, and B. Gintert, “Image-Based coral reef classification and thematic mapping”, Remote Sensing, vol. 5, no. 4, pp. 1809–1841, Apr. 2013, doi: 10.3390/rs5041809. |
yzhuang/autotree_pmlb_100000_Hill_Valley_without_noise_sgosdt_l256_dim10_d3_sd0 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 2364400000
num_examples: 100000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 1014035692
dataset_size: 2600840000
---
# Dataset Card for "autotree_pmlb_100000_Hill_Valley_without_noise_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cvzion/dqg-dataset-v3-final | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 79869
num_examples: 136
download_size: 32385
dataset_size: 79869
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_sethuiyer__SynthIQ-7b | ---
pretty_name: Evaluation run of sethuiyer/SynthIQ-7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [sethuiyer/SynthIQ-7b](https://huggingface.co/sethuiyer/SynthIQ-7b) 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_sethuiyer__SynthIQ-7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-29T20:56:49.534074](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__SynthIQ-7b/blob/main/results_2023-12-29T20-56-49.534074.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\": 0.6496837874821432,\n\
\ \"acc_stderr\": 0.031931649395756406,\n \"acc_norm\": 0.6512485789591291,\n\
\ \"acc_norm_stderr\": 0.03256964811222345,\n \"mc1\": 0.39657282741738065,\n\
\ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.570003601485072,\n\
\ \"mc2_stderr\": 0.015572869395398968\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6254266211604096,\n \"acc_stderr\": 0.014144193471893449,\n\
\ \"acc_norm\": 0.658703071672355,\n \"acc_norm_stderr\": 0.013855831287497724\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6709818761202948,\n\
\ \"acc_stderr\": 0.004688963175758135,\n \"acc_norm\": 0.858195578570006,\n\
\ \"acc_norm_stderr\": 0.003481364840770978\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.04852365870939099,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.04852365870939099\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7018867924528301,\n \"acc_stderr\": 0.028152837942493864,\n\
\ \"acc_norm\": 0.7018867924528301,\n \"acc_norm_stderr\": 0.028152837942493864\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7847222222222222,\n\
\ \"acc_stderr\": 0.03437079344106135,\n \"acc_norm\": 0.7847222222222222,\n\
\ \"acc_norm_stderr\": 0.03437079344106135\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\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\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.04093601807403326,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.04093601807403326\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4824561403508772,\n\
\ \"acc_stderr\": 0.04700708033551038,\n \"acc_norm\": 0.4824561403508772,\n\
\ \"acc_norm_stderr\": 0.04700708033551038\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555497,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555497\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.02510742548113728,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.02510742548113728\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.023415293433568525,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.023415293433568525\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7575757575757576,\n \"acc_stderr\": 0.03346409881055953,\n\
\ \"acc_norm\": 0.7575757575757576,\n \"acc_norm_stderr\": 0.03346409881055953\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7878787878787878,\n \"acc_stderr\": 0.029126522834586815,\n \"\
acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.029126522834586815\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.020986854593289733,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.020986854593289733\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971128,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971128\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.337037037037037,\n \"acc_stderr\": 0.02882088466625326,\n \
\ \"acc_norm\": 0.337037037037037,\n \"acc_norm_stderr\": 0.02882088466625326\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634325,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634325\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.33112582781456956,\n \"acc_stderr\": 0.038425817186598696,\n \"\
acc_norm\": 0.33112582781456956,\n \"acc_norm_stderr\": 0.038425817186598696\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8422018348623853,\n \"acc_stderr\": 0.01563002297009244,\n \"\
acc_norm\": 0.8422018348623853,\n \"acc_norm_stderr\": 0.01563002297009244\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8284313725490197,\n \"acc_stderr\": 0.026460569561240644,\n \"\
acc_norm\": 0.8284313725490197,\n \"acc_norm_stderr\": 0.026460569561240644\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8185654008438819,\n \"acc_stderr\": 0.025085961144579654,\n \
\ \"acc_norm\": 0.8185654008438819,\n \"acc_norm_stderr\": 0.025085961144579654\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7862595419847328,\n \"acc_stderr\": 0.0359546161177469,\n\
\ \"acc_norm\": 0.7862595419847328,\n \"acc_norm_stderr\": 0.0359546161177469\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8016528925619835,\n \"acc_stderr\": 0.03640118271990947,\n \"\
acc_norm\": 0.8016528925619835,\n \"acc_norm_stderr\": 0.03640118271990947\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.03755265865037181,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.03755265865037181\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7668711656441718,\n \"acc_stderr\": 0.0332201579577674,\n\
\ \"acc_norm\": 0.7668711656441718,\n \"acc_norm_stderr\": 0.0332201579577674\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.039166677628225836,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.039166677628225836\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8803418803418803,\n\
\ \"acc_stderr\": 0.021262719400406964,\n \"acc_norm\": 0.8803418803418803,\n\
\ \"acc_norm_stderr\": 0.021262719400406964\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \
\ \"acc_norm\": 0.74,\n \"acc_norm_stderr\": 0.04408440022768078\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8199233716475096,\n\
\ \"acc_stderr\": 0.013740797258579825,\n \"acc_norm\": 0.8199233716475096,\n\
\ \"acc_norm_stderr\": 0.013740797258579825\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508287,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508287\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.42569832402234636,\n\
\ \"acc_stderr\": 0.01653682964899711,\n \"acc_norm\": 0.42569832402234636,\n\
\ \"acc_norm_stderr\": 0.01653682964899711\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.025646863097137897,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.025646863097137897\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7041800643086816,\n\
\ \"acc_stderr\": 0.025922371788818763,\n \"acc_norm\": 0.7041800643086816,\n\
\ \"acc_norm_stderr\": 0.025922371788818763\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7314814814814815,\n \"acc_stderr\": 0.024659685185967284,\n\
\ \"acc_norm\": 0.7314814814814815,\n \"acc_norm_stderr\": 0.024659685185967284\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4787234042553192,\n \"acc_stderr\": 0.029800481645628693,\n \
\ \"acc_norm\": 0.4787234042553192,\n \"acc_norm_stderr\": 0.029800481645628693\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4654498044328553,\n\
\ \"acc_stderr\": 0.012739711554045708,\n \"acc_norm\": 0.4654498044328553,\n\
\ \"acc_norm_stderr\": 0.012739711554045708\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.02841820861940676,\n\
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.02841820861940676\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6633986928104575,\n \"acc_stderr\": 0.019117213911495148,\n \
\ \"acc_norm\": 0.6633986928104575,\n \"acc_norm_stderr\": 0.019117213911495148\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.0449429086625209,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.0449429086625209\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8606965174129353,\n\
\ \"acc_stderr\": 0.02448448716291397,\n \"acc_norm\": 0.8606965174129353,\n\
\ \"acc_norm_stderr\": 0.02448448716291397\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.0358870281282637,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.0358870281282637\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5602409638554217,\n\
\ \"acc_stderr\": 0.03864139923699122,\n \"acc_norm\": 0.5602409638554217,\n\
\ \"acc_norm_stderr\": 0.03864139923699122\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061456,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061456\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39657282741738065,\n\
\ \"mc1_stderr\": 0.017124930942023515,\n \"mc2\": 0.570003601485072,\n\
\ \"mc2_stderr\": 0.015572869395398968\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7868981846882399,\n \"acc_stderr\": 0.01150895769072275\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.640636846095527,\n \
\ \"acc_stderr\": 0.013216456309851523\n }\n}\n```"
repo_url: https://huggingface.co/sethuiyer/SynthIQ-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_12_29T20_56_49.534074
path:
- '**/details_harness|arc:challenge|25_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|gsm8k|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hellaswag|10_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-29T20-56-49.534074.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-29T20-56-49.534074.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- '**/details_harness|winogrande|5_2023-12-29T20-56-49.534074.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-29T20-56-49.534074.parquet'
- config_name: results
data_files:
- split: 2023_12_29T20_56_49.534074
path:
- results_2023-12-29T20-56-49.534074.parquet
- split: latest
path:
- results_2023-12-29T20-56-49.534074.parquet
---
# Dataset Card for Evaluation run of sethuiyer/SynthIQ-7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [sethuiyer/SynthIQ-7b](https://huggingface.co/sethuiyer/SynthIQ-7b) 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_sethuiyer__SynthIQ-7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-29T20:56:49.534074](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__SynthIQ-7b/blob/main/results_2023-12-29T20-56-49.534074.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": 0.6496837874821432,
"acc_stderr": 0.031931649395756406,
"acc_norm": 0.6512485789591291,
"acc_norm_stderr": 0.03256964811222345,
"mc1": 0.39657282741738065,
"mc1_stderr": 0.017124930942023515,
"mc2": 0.570003601485072,
"mc2_stderr": 0.015572869395398968
},
"harness|arc:challenge|25": {
"acc": 0.6254266211604096,
"acc_stderr": 0.014144193471893449,
"acc_norm": 0.658703071672355,
"acc_norm_stderr": 0.013855831287497724
},
"harness|hellaswag|10": {
"acc": 0.6709818761202948,
"acc_stderr": 0.004688963175758135,
"acc_norm": 0.858195578570006,
"acc_norm_stderr": 0.003481364840770978
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.04852365870939099,
"acc_norm": 0.63,
"acc_norm_stderr": 0.04852365870939099
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7018867924528301,
"acc_stderr": 0.028152837942493864,
"acc_norm": 0.7018867924528301,
"acc_norm_stderr": 0.028152837942493864
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7847222222222222,
"acc_stderr": 0.03437079344106135,
"acc_norm": 0.7847222222222222,
"acc_norm_stderr": 0.03437079344106135
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"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": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4215686274509804,
"acc_stderr": 0.04913595201274498,
"acc_norm": 0.4215686274509804,
"acc_norm_stderr": 0.04913595201274498
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.04093601807403326,
"acc_norm": 0.79,
"acc_norm_stderr": 0.04093601807403326
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4824561403508772,
"acc_stderr": 0.04700708033551038,
"acc_norm": 0.4824561403508772,
"acc_norm_stderr": 0.04700708033551038
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.04130740879555497,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555497
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.02510742548113728,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.02510742548113728
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.023415293433568525,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.023415293433568525
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7575757575757576,
"acc_stderr": 0.03346409881055953,
"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.029126522834586815,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.029126522834586815
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.020986854593289733,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.020986854593289733
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6692307692307692,
"acc_stderr": 0.023854795680971128,
"acc_norm": 0.6692307692307692,
"acc_norm_stderr": 0.023854795680971128
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## More Information [optional]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
NarchAI1992/bigfile_Luxtury_walnut | ---
license: openrail
---
|
communityai/akjindal53244___Arithmo-Data-120k | ---
dataset_info:
features:
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dtype: string
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dataset_size: 270496923.67481923
configs:
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data_files:
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path: data/train-*
---
|
Gopal2002/donut_finetuning | ---
dataset_info:
features:
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dtype: image
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dtype: string
splits:
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num_bytes: 2117606.0
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configs:
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data_files:
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path: data/train-*
---
|
mohanrajanbalagan/testing | ---
license: unknown
---
|
AdapterOcean/med_alpaca_standardized_cluster_69_alpaca | ---
dataset_info:
features:
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dtype: string
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dtype: string
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download_size: 7142645
dataset_size: 13260451
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_69_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ARTemAI/hands | ---
license: openrail
---
|
elenahuang/primary-sector-bottom-1k | ---
dataset_info:
features:
- name: text
dtype: string
splits:
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num_bytes: 8620437
num_examples: 1000
download_size: 4571154
dataset_size: 8620437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "primary-sector-bottom-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ted_talks_iwslt | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
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- am
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- arq
- art
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language_bcp47:
- art-x-bork
- fr-CA
- pt-BR
- zh-CN
- zh-TW
license:
- cc-by-nc-nd-4.0
multilinguality:
- translation
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: Web Inventory of Transcribed & Translated (WIT) Ted Talks
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download_size: 1666674366
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config_names:
- de_ja_2014
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- de_ja_2016
- eu_ca_2014
- eu_ca_2015
- eu_ca_2016
- fr-ca_hi_2014
- fr-ca_hi_2015
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- nl_en_2014
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- nl_hi_2014
- nl_hi_2015
- nl_hi_2016
---
# Dataset Card for Web Inventory of Transcribed & Translated(WIT) Ted Talks
## 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://wit3.fbk.eu/home
- **Repository:** https://drive.google.com/file/d/1Cz1Un9p8Xn9IpEMMrg2kXSDt0dnjxc4z/view?usp=sharing
- **Paper:** https://www.aclweb.org/anthology/2012.eamt-1.60.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mauro Cettolo](mailto:cettolo@fbk.eu)
[Roldano Cattoni](mailto:cattoni@fbk.eu)
### Dataset Summary
The Web Inventory Talk is a collection of the original Ted talks and their translated version. The translations are available in more than 109+ languages, though the distribution is not uniform.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
E.g.
`dataset = load_dataset("ted_talks_iwslt", language_pair=("it", "pl"), year="2014")`
The full list of languages is: 'af', 'am', 'ar', 'arq', 'art-x-bork', 'as', 'ast', 'az', 'be', 'bg', 'bi', 'bn', 'bo', 'bs', 'ca', 'ceb', 'cnh', 'cs', 'da', 'de', 'el', 'en', 'eo', 'es', 'et', 'eu', 'fa', 'fi', 'fil', 'fr', 'fr-ca', 'ga', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hup', 'hy', 'id', 'ig', 'inh', 'is', 'it', 'ja', 'ka', 'kk', 'km', 'kn', 'ko', 'ku', 'ky', 'la', 'lb', 'lo', 'lt', 'ltg', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'mt', 'my', 'nb', 'ne', 'nl', 'nn', 'oc', 'pa', 'pl', 'ps', 'pt', 'pt-br', 'ro', 'ru', 'rup', 'sh', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'srp', 'sv', 'sw', 'szl', 'ta', 'te', 'tg', 'th', 'tl', 'tlh', 'tr', 'tt', 'ug', 'uk', 'ur', 'uz', 'vi', 'zh', 'zh-cn', 'zh-tw'.
The full list of years is: '2014', '2015', '2016'.
### Supported Tasks and Leaderboards
machine learning task, language modeling and generation
### Languages
Ted talks are mostly held in English (`en`). Almost all of the talks have been translated, by volunteers, into Arabic, Bulgarian, Chinese (simplified), French, Italian, Korean, Portuguese (Brazil) and Spanish. For about 70 other languages, the number of translated talks ranges from several hundreds (e.g. such as other Dutch, German, Hebrew, Romanian) to one (e.g. Hausa, Hupa, Bislama, Ingush, Maltese).
The languages in the dataset are:
- af
- am
- ar
- arq
- art
- as
- ast
- az
- be
- bg
- bi
- bn
- bo
- bs
- ca
- ceb
- cnh
- cs
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- ga
- gl
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hup
- hy
- id
- ig
- inh
- is
- it
- ja
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- ltg
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- mt
- my
- nb
- ne
- nl
- nn
- oc
- pa
- pl
- ps
- pt
- ro
- ru
- rup
- sh
- si
- sk
- sl
- so
- sq
- sr
- srp: Serbian (`sr`)
- sv
- sw
- szl
- ta
- te
- tg
- th
- tl
- tlh
- tr
- tt
- ug
- uk
- ur
- uz
- vi
- zh
## Dataset Structure
### Data Instances
One example from the dataset is:
```
{'translation': {'hi': 'जब मार्च २०१४ में इबोला का प्रकोप छाया, पर्डिस सबेटी और उनकी टीम को वाइरस के जीनोम का अनुक्रमण करना था, सीखना था कि यह कैसे परवतिर्त होते हैं और फैलते हैं। सबेटी ने तुरंत ही अपने अनुसंधान को वेब में जारी किया, ताकि दुनिया भर के वाइरस ट्रैकर्स और वैज्ञानिक इस तत्काल लड़ाई में शामिल हो सकें। इस बातचीत में, वह दिखाती हैं कि सबका सहयोग ही कुंजी है वाइरस को रोकने के लिए--और लड़ने के लिए आगे आने वाले हमलों से। सबेटी ने कहा,"हमने खुले तौर पर काम किया, साझा किया और साथ काम किया"। "हमे दुनिया को एक वाइरस के विनाश से नहीं, पर अरबों दिलों और दिमागों की एकता से परिभाषित करना है"।',
'nl': 'Toen Ebola in maart 2014 uitbrak, zijn Pardis Sabeti en haar team aan het werk gegaan om het genoom in kaart te brengen. Zo ontdekten ze hoe het virus zich verspreidde en muteerde. Sabeti zette direct haar onderzoek op het internet, zodat wereldwijd virus-jagers en wetenschappers mee konden werken aan de strijd. In deze talk laat ze zien hoe die openheid geholpen heeft bij het stoppen van het virus en hoe het kan helpen bij de strijd tegen het volgende virus. "We moesten transparant werken, delen en samenwerken". Sabeti zegt:"Laat de wereld niet ten onder gaan aan een virus, maar verlicht worden door miljoenen harten en geesten die samenwerken."'}}
```
The original XML files are formatted like this example:
```
<file id="1">
<head>
<url>http://www.ted.com/talks/ryan_holladay_to_hear_this_music_you_have_to_be_there_literally.html</url>
<pagesize>66634</pagesize>
<dtime>Sun Jan 12 15:17:32 CET 2014</dtime>
<content-type>text/html; charset=utf-8</content-type>
<encoding>utf-8</encoding>
<videourl>http://download.ted.com/talks/RyanHolladay_2013S.mp4</videourl>
<videopath>talks/RyanHolladay_2013S.mp4</videopath>
<transcription>
<seekvideo id="2939">(Music)</seekvideo>
<seekvideo id="7555">For any of you who have visited or lived in New York City,</seekvideo>
<seekvideo id="11221">these shots might start to look familiar.</seekvideo>
<seekvideo id="16116">This is Central Park,</seekvideo>
.
.
.
<seekvideo id="361992">for people to interact with</seekvideo>
<seekvideo id="363709">and experience music.</seekvideo>
<seekvideo id="365451">Thank you.</seekvideo>
<seekvideo id="367495">(Applause)</seekvideo>
</transcription>
<talkid>1903</talkid>
<title>Ryan Holladay: To hear this music you have to be there. Literally</title>
<description>The music industry ......segments of sounds that only play when a listener is physically nearby. (Filmed at TED@BCG.)</description>
<keywords>entertainment,music,technology</keywords>
<image>http://images.ted.com/images/ted/d98c17773da6f84e9f915895c270c7ffd2de3778_389x292.jpg</image>
<date>2014/01/12</date>
<wordnum>885</wordnum>
<charnum>5051</charnum>
</head>
<content>(Music) For any of you who have visited or lived in New York City, these shots might start to look familiar. This is Central Park, ............new ways for people to interact with and experience music. Thank you. (Applause)</content>
</file>
```
### Data Fields
The fields of the dataset are:
- translation:
- <lang1>: text in <lang1>
- <lang2>L translated text in <lang2>
Information about the original data files:
For each language, a single XML file is generated which includes all talks subtitled in
that language. Each talk is enclosed in tags `<file id="int">` and `</file>` and includes, among other tags:
| Tags | Description |
|---|:---|
| `<url>`| the address of the original HTML document of the talk |
| `<speaker>` | the name of the talk speaker |
| `<talkid>` | the numeric talk identifier |
| `<transcript>` | talk subtitles split in captions |
| `<date>` | the issue date of the talk |
| `<content>` | talk subtitles |
### Data Splits
The paper doesn't provide any specific train-test-dev splits. However data can be split by available years (2014, 2015, 2016)
## Dataset Creation
### Curation Rationale
TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English and their translations in more than 80 languages. Aside from its cultural and social relevance, this content, which is published under the Creative Commons BYNC-ND license, also represents a precious language resource for the machine translation research community, thanks to its size, variety of topics, and covered languages.
### Source Data
#### Initial Data Collection and Normalization
The talks were collected from the [Ted Conference website](http://www.ted.com/)
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
Translation has been contributed by volunteers
### Personal and Sensitive Information
No personal and sensitive information is provided in the dataset. All talks are publicly available
## Considerations for Using the Data
### Social Impact of Dataset
In statistical machine translation, large amount of in-domain parallel data are usually required to properly train translation and reordering models. With more than 900+ Ted talks (as of 2011) and translation in more than 90+ languages. This dataset provides a useful resource for the MT research community.
In turn, this enables easy access to a vast treasure trove of human knowledge.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
The original dataset was curated by:
[Mauro Cettolo](mailto:cettolo@fbk.eu)
[Roldano Cattoni](mailto:cattoni@fbk.eu)
Author:
Christian Girardi
For issues with the HuggingFace Dataset implementation, reach out: [Aakash Gupta](mailto:aakashg80@gmail.com)
### Licensing Information
cc-by-nc-nd-4.0
### Citation Information
```
@inproceedings{cettolo-etal-2012-wit3,
title = "{WIT}3: Web Inventory of Transcribed and Translated Talks",
author = "Cettolo, Mauro and
Girardi, Christian and
Federico, Marcello",
booktitle = "Proceedings of the 16th Annual conference of the European Association for Machine Translation",
month = may # " 28{--}30",
year = "2012",
address = "Trento, Italy",
publisher = "European Association for Machine Translation",
url = "https://www.aclweb.org/anthology/2012.eamt-1.60",
pages = "261--268",
}
```
### Contributions
Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset. |
krr-oxford/OntoLAMA | ---
license: apache-2.0
task_categories:
- text-classification
tags:
- Ontologies
- Subsumption Inference
- Natural Language Inference
- Conceptual Knowledge
- LMs-as-KBs
pretty_name: OntoLAMA
size_categories:
- 1M<n<10M
language:
- en
dataset_info:
- config_name: schemaorg-atomic-SI
features:
- name: v_sub_concept
dtype: string
- name: v_super_concept
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative_subsumption
'1': positive_subsumption
- name: axiom
dtype: string
splits:
- name: train
num_bytes: 103485
num_examples: 808
- name: validation
num_bytes: 51523
num_examples: 404
- name: test
num_bytes: 361200
num_examples: 2830
download_size: 82558
dataset_size: 516208
- config_name: doid-atomic-SI
features:
- name: v_sub_concept
dtype: string
- name: v_super_concept
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative_subsumption
'1': positive_subsumption
- name: axiom
dtype: string
splits:
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num_bytes: 15803053
num_examples: 90500
- name: validation
num_bytes: 1978584
num_examples: 11312
- name: test
num_bytes: 1977582
num_examples: 11314
download_size: 3184028
dataset_size: 19759219
- config_name: foodon-atomic-SI
features:
- name: v_sub_concept
dtype: string
- name: v_super_concept
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative_subsumption
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dtype: string
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num_examples: 96060
- name: test
num_bytes: 16098373
num_examples: 96062
download_size: 28499028
dataset_size: 160926634
- config_name: go-atomic-SI
features:
- name: v_sub_concept
dtype: string
- name: v_super_concept
dtype: string
- name: label
dtype:
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names:
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dtype: string
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num_examples: 772870
- name: validation
num_bytes: 19060490
num_examples: 96608
- name: test
num_bytes: 19069265
num_examples: 96610
download_size: 32379717
dataset_size: 190666988
- config_name: bimnli
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': contradiction
'1': entailment
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num_examples: 12906
download_size: 19264134
dataset_size: 50602187
- config_name: foodon-complex-SI
features:
- name: v_sub_concept
dtype: string
- name: v_super_concept
dtype: string
- name: label
dtype:
class_label:
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features:
- name: v_sub_concept
dtype: string
- name: v_super_concept
dtype: string
- name: label
dtype:
class_label:
names:
'0': negative_subsumption
'1': positive_subsumption
- name: axiom
dtype: string
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dtype: string
splits:
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num_bytes: 5671713
num_examples: 9040
- name: test
num_bytes: 5667069
num_examples: 9040
download_size: 5059364
dataset_size: 56667584
---
# OntoLAMA: LAnguage Model Analysis for Ontology Subsumption Inference
### Dataset Summary
OntoLAMA is a set of language model (LM) probing datasets for ontology subsumption inference.
The work follows the "LMs-as-KBs" literature but focuses on conceptualised knowledge extracted from formalised KBs
such as the OWL ontologies. Specifically, the subsumption inference (SI) task is introduced and formulated in the
Natural Language Inference (NLI) style, where the sub-concept and the super-concept involved in a subsumption
axiom are verbalised and fitted into a template to form the premise and hypothesis, respectively.
The sampled axioms are verified through ontology reasoning. The SI task is further divided into Atomic SI and
Complex SI where the former involves only atomic named concepts and the latter involves both atomic and complex concepts.
Real-world ontologies of different scales and domains are used for constructing OntoLAMA and in total there are four Atomic
SI datasets and two Complex SI datasets.
See dataset specifications on [DeepOnto](https://krr-oxford.github.io/DeepOnto/ontolama/).
See the published paper on [Arxiv](https://arxiv.org/abs/2302.06761) or [ACL Anthology](https://aclanthology.org/2023.findings-acl.213/).
### Languages
The text in the dataset is in English, as used in the source ontologies. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example in the **Atomic SI** dataset created from the Gene Ontology (GO) is as follows:
```
{
'v_sub_concept': 'ctpase activity',
'v_super_concept': 'ribonucleoside triphosphate phosphatase activity',
'label': 1,
'axiom': 'SubClassOf(<http://purl.obolibrary.org/obo/GO_0043273> <http://purl.obolibrary.org/obo/GO_0017111>)'
}
```
An example in the **Complex SI** dataset created from the Food Ontology (FoodOn) is as follows:
```
{
'v_sub_concept': 'ham and cheese sandwich that derives from some lima bean (whole)',
'v_super_concept': 'lima bean substance',
'label': 0,
'axiom': 'SubClassOf(ObjectIntersectionOf(<http://purl.obolibrary.org/obo/FOODON_03307824> ObjectSomeValuesFrom(<http://purl.obolibrary.org/obo/RO_0001000> <http://purl.obolibrary.org/obo/FOODON_03302053>)) <http://purl.obolibrary.org/obo/FOODON_00002776>)',
'anchor_axiom': 'EquivalentClasses(<http://purl.obolibrary.org/obo/FOODON_00002776> ObjectIntersectionOf(<http://purl.obolibrary.org/obo/FOODON_00002000> ObjectSomeValuesFrom(<http://purl.obolibrary.org/obo/RO_0001000> <http://purl.obolibrary.org/obo/FOODON_03302053>)) )'
}
```
An example in the **biMNLI** dataset created from the MNLI dataset is as follows:
```
{
'premise': 'At the turn of the 19th century Los Angeles and Salt Lake City were among the burgeoning metropolises of the new American West.',
'hypothesis': 'Salt Lake City was booming in the early 19th century.',
'label': 1
}
```
### Data Fields
#### SI Data Fields
- `v_sub_concept`: verbalised sub-concept expression.
- `v_super_concept`: verbalised super-concept expression.
- `label`: a binary class label indicating whether two concepts really form a subsumption relationship (`1` means yes).
- `axiom`: a string representation of the original subsumption axiom which is useful for tracing back to the ontology.
- `anchor_axiom`: (for complex SI only) a string representation of the anchor equivalence axiom used for sampling the `axiom`.
#### biMNLI Data Fields
- `premise`: inheritated from the MNLI dataset.
- `hypothesis`: inheritated from the MNLI dataset.
- `label`: a binary class label indicating `contradiction` (`0`) or `entailment` (`1`).
### Data Splits
| Source | #NamedConcepts | #EquivAxioms | #Dataset (Train/Dev/Test) |
|------------|----------------|--------------|------------------------------------------------------------------------|
| Schema.org | 894 | - | Atomic SI: 808/404/2,830 |
| DOID | 11,157 | - | Atomic SI: 90,500/11,312/11,314 |
| FoodOn | 30,995 | 2,383 | Atomic SI: 768,486/96,060/96,062 <br /> Complex SI: 3,754/1,850/13,080 |
| GO | 43,303 | 11,456 | Atomic SI: 772,870/96,608/96,610 <br /> Complex SI: 72,318/9,040/9,040 |
| MNLI | - | - | biMNLI: 235,622/26,180/12,906 |
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
The relevant paper has been accepted at Findings of ACL 2023.
```
@inproceedings{he-etal-2023-language,
title = "Language Model Analysis for Ontology Subsumption Inference",
author = "He, Yuan and
Chen, Jiaoyan and
Jimenez-Ruiz, Ernesto and
Dong, Hang and
Horrocks, Ian",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.213",
doi = "10.18653/v1/2023.findings-acl.213",
pages = "3439--3453"
}
``` |
carterzin/eu | ---
license: artistic-2.0
---
|
open-llm-leaderboard/details_janhq__supermario-slerp-v2 | ---
pretty_name: Evaluation run of janhq/supermario-slerp-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [janhq/supermario-slerp-v2](https://huggingface.co/janhq/supermario-slerp-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_janhq__supermario-slerp-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-12-12T07:20:29.210830](https://huggingface.co/datasets/open-llm-leaderboard/details_janhq__supermario-slerp-v2/blob/main/results_2023-12-12T07-20-29.210830.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\": 0.6523192880412831,\n\
\ \"acc_stderr\": 0.031929152104876686,\n \"acc_norm\": 0.6535004820907845,\n\
\ \"acc_norm_stderr\": 0.03257318036897926,\n \"mc1\": 0.47613219094247244,\n\
\ \"mc1_stderr\": 0.017483547156961574,\n \"mc2\": 0.6295900737174474,\n\
\ \"mc2_stderr\": 0.015194573521166509\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6680887372013652,\n \"acc_stderr\": 0.01376098820088054,\n\
\ \"acc_norm\": 0.6936860068259386,\n \"acc_norm_stderr\": 0.013470584417276513\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6837283409679347,\n\
\ \"acc_stderr\": 0.004640699483543311,\n \"acc_norm\": 0.8659629555865366,\n\
\ \"acc_norm_stderr\": 0.003399958334372066\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6444444444444445,\n\
\ \"acc_stderr\": 0.04135176749720385,\n \"acc_norm\": 0.6444444444444445,\n\
\ \"acc_norm_stderr\": 0.04135176749720385\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7236842105263158,\n \"acc_stderr\": 0.03639057569952928,\n\
\ \"acc_norm\": 0.7236842105263158,\n \"acc_norm_stderr\": 0.03639057569952928\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438662,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438662\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7638888888888888,\n\
\ \"acc_stderr\": 0.03551446610810826,\n \"acc_norm\": 0.7638888888888888,\n\
\ \"acc_norm_stderr\": 0.03551446610810826\n },\n \"harness|hendrycksTest-college_chemistry|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_computer_science|5\": {\n \"acc\"\
: 0.55,\n \"acc_stderr\": 0.05,\n \"acc_norm\": 0.55,\n \"\
acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_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\": 0.6589595375722543,\n\
\ \"acc_stderr\": 0.03614665424180826,\n \"acc_norm\": 0.6589595375722543,\n\
\ \"acc_norm_stderr\": 0.03614665424180826\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4117647058823529,\n \"acc_stderr\": 0.048971049527263666,\n\
\ \"acc_norm\": 0.4117647058823529,\n \"acc_norm_stderr\": 0.048971049527263666\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5872340425531914,\n \"acc_stderr\": 0.03218471141400351,\n\
\ \"acc_norm\": 0.5872340425531914,\n \"acc_norm_stderr\": 0.03218471141400351\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5793103448275863,\n \"acc_stderr\": 0.0411391498118926,\n\
\ \"acc_norm\": 0.5793103448275863,\n \"acc_norm_stderr\": 0.0411391498118926\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055273,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055273\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\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\"\
: 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268552,\n \"\
acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268552\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4975369458128079,\n \"acc_stderr\": 0.03517945038691063,\n \"\
acc_norm\": 0.4975369458128079,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.73,\n \"acc_stderr\": 0.044619604333847394,\n \"acc_norm\"\
: 0.73,\n \"acc_norm_stderr\": 0.044619604333847394\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.03192271569548301,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.03192271569548301\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.803030303030303,\n \"acc_stderr\": 0.028335609732463362,\n \"\
acc_norm\": 0.803030303030303,\n \"acc_norm_stderr\": 0.028335609732463362\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.917098445595855,\n \"acc_stderr\": 0.01989934131572178,\n\
\ \"acc_norm\": 0.917098445595855,\n \"acc_norm_stderr\": 0.01989934131572178\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.0395802723112157,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.0395802723112157\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8550458715596331,\n \"acc_stderr\": 0.015094215699700488,\n \"\
acc_norm\": 0.8550458715596331,\n \"acc_norm_stderr\": 0.015094215699700488\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5,\n \"acc_stderr\": 0.034099716973523674,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.034099716973523674\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156861,\n\
\ \"acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156861\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8270042194092827,\n \"acc_stderr\": 0.024621562866768427,\n \
\ \"acc_norm\": 0.8270042194092827,\n \"acc_norm_stderr\": 0.024621562866768427\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6816143497757847,\n\
\ \"acc_stderr\": 0.03126580522513713,\n \"acc_norm\": 0.6816143497757847,\n\
\ \"acc_norm_stderr\": 0.03126580522513713\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7870370370370371,\n\
\ \"acc_stderr\": 0.0395783547198098,\n \"acc_norm\": 0.7870370370370371,\n\
\ \"acc_norm_stderr\": 0.0395783547198098\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.039166677628225836,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.039166677628225836\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.022509033937077802,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.022509033937077802\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8365261813537676,\n\
\ \"acc_stderr\": 0.013223928616741624,\n \"acc_norm\": 0.8365261813537676,\n\
\ \"acc_norm_stderr\": 0.013223928616741624\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7167630057803468,\n \"acc_stderr\": 0.02425790170532338,\n\
\ \"acc_norm\": 0.7167630057803468,\n \"acc_norm_stderr\": 0.02425790170532338\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.41675977653631285,\n\
\ \"acc_stderr\": 0.016489134962438954,\n \"acc_norm\": 0.41675977653631285,\n\
\ \"acc_norm_stderr\": 0.016489134962438954\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7009646302250804,\n\
\ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.7009646302250804,\n\
\ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765137,\n\
\ \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765137\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.02979071924382972,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.02979071924382972\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4595827900912647,\n\
\ \"acc_stderr\": 0.01272844606766997,\n \"acc_norm\": 0.4595827900912647,\n\
\ \"acc_norm_stderr\": 0.01272844606766997\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6911764705882353,\n \"acc_stderr\": 0.02806499816704009,\n\
\ \"acc_norm\": 0.6911764705882353,\n \"acc_norm_stderr\": 0.02806499816704009\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6683006535947712,\n \"acc_stderr\": 0.019047485239360378,\n \
\ \"acc_norm\": 0.6683006535947712,\n \"acc_norm_stderr\": 0.019047485239360378\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6909090909090909,\n\
\ \"acc_stderr\": 0.044262946482000985,\n \"acc_norm\": 0.6909090909090909,\n\
\ \"acc_norm_stderr\": 0.044262946482000985\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.025196929874827072,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.025196929874827072\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.03379976689896309,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.03379976689896309\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.536144578313253,\n\
\ \"acc_stderr\": 0.038823108508905954,\n \"acc_norm\": 0.536144578313253,\n\
\ \"acc_norm_stderr\": 0.038823108508905954\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\": 0.47613219094247244,\n\
\ \"mc1_stderr\": 0.017483547156961574,\n \"mc2\": 0.6295900737174474,\n\
\ \"mc2_stderr\": 0.015194573521166509\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8082083662194159,\n \"acc_stderr\": 0.011065209664659527\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6345716451857468,\n \
\ \"acc_stderr\": 0.013264282030266635\n }\n}\n```"
repo_url: https://huggingface.co/janhq/supermario-slerp-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_12_12T07_20_29.210830
path:
- '**/details_harness|arc:challenge|25_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|gsm8k|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hellaswag|10_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T07-20-29.210830.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-12-12T07-20-29.210830.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- '**/details_harness|winogrande|5_2023-12-12T07-20-29.210830.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-12-12T07-20-29.210830.parquet'
- config_name: results
data_files:
- split: 2023_12_12T07_20_29.210830
path:
- results_2023-12-12T07-20-29.210830.parquet
- split: latest
path:
- results_2023-12-12T07-20-29.210830.parquet
---
# Dataset Card for Evaluation run of janhq/supermario-slerp-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [janhq/supermario-slerp-v2](https://huggingface.co/janhq/supermario-slerp-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_janhq__supermario-slerp-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-12T07:20:29.210830](https://huggingface.co/datasets/open-llm-leaderboard/details_janhq__supermario-slerp-v2/blob/main/results_2023-12-12T07-20-29.210830.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": 0.6523192880412831,
"acc_stderr": 0.031929152104876686,
"acc_norm": 0.6535004820907845,
"acc_norm_stderr": 0.03257318036897926,
"mc1": 0.47613219094247244,
"mc1_stderr": 0.017483547156961574,
"mc2": 0.6295900737174474,
"mc2_stderr": 0.015194573521166509
},
"harness|arc:challenge|25": {
"acc": 0.6680887372013652,
"acc_stderr": 0.01376098820088054,
"acc_norm": 0.6936860068259386,
"acc_norm_stderr": 0.013470584417276513
},
"harness|hellaswag|10": {
"acc": 0.6837283409679347,
"acc_stderr": 0.004640699483543311,
"acc_norm": 0.8659629555865366,
"acc_norm_stderr": 0.003399958334372066
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.28,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.28,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6444444444444445,
"acc_stderr": 0.04135176749720385,
"acc_norm": 0.6444444444444445,
"acc_norm_stderr": 0.04135176749720385
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7236842105263158,
"acc_stderr": 0.03639057569952928,
"acc_norm": 0.7236842105263158,
"acc_norm_stderr": 0.03639057569952928
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.61,
"acc_stderr": 0.04902071300001975,
"acc_norm": 0.61,
"acc_norm_stderr": 0.04902071300001975
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.028254200344438662,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.028254200344438662
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.55,
"acc_stderr": 0.05,
"acc_norm": 0.55,
"acc_norm_stderr": 0.05
},
"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": 0.6589595375722543,
"acc_stderr": 0.03614665424180826,
"acc_norm": 0.6589595375722543,
"acc_norm_stderr": 0.03614665424180826
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4117647058823529,
"acc_stderr": 0.048971049527263666,
"acc_norm": 0.4117647058823529,
"acc_norm_stderr": 0.048971049527263666
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.76,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.76,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5872340425531914,
"acc_stderr": 0.03218471141400351,
"acc_norm": 0.5872340425531914,
"acc_norm_stderr": 0.03218471141400351
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
"acc_stderr": 0.04702880432049615,
"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5793103448275863,
"acc_stderr": 0.0411391498118926,
"acc_norm": 0.5793103448275863,
"acc_norm_stderr": 0.0411391498118926
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.025355741263055273,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.025355741263055273
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"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": 0.7870967741935484,
"acc_stderr": 0.023287665127268552,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268552
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4975369458128079,
"acc_stderr": 0.03517945038691063,
"acc_norm": 0.4975369458128079,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.73,
"acc_stderr": 0.044619604333847394,
"acc_norm": 0.73,
"acc_norm_stderr": 0.044619604333847394
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.03192271569548301,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.03192271569548301
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.803030303030303,
"acc_stderr": 0.028335609732463362,
"acc_norm": 0.803030303030303,
"acc_norm_stderr": 0.028335609732463362
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.917098445595855,
"acc_stderr": 0.01989934131572178,
"acc_norm": 0.917098445595855,
"acc_norm_stderr": 0.01989934131572178
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.676923076923077,
"acc_stderr": 0.02371088850197057,
"acc_norm": 0.676923076923077,
"acc_norm_stderr": 0.02371088850197057
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.680672268907563,
"acc_stderr": 0.030283995525884396,
"acc_norm": 0.680672268907563,
"acc_norm_stderr": 0.030283995525884396
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
"acc_stderr": 0.0395802723112157,
"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.0395802723112157
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8550458715596331,
"acc_stderr": 0.015094215699700488,
"acc_norm": 0.8550458715596331,
"acc_norm_stderr": 0.015094215699700488
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5,
"acc_stderr": 0.034099716973523674,
"acc_norm": 0.5,
"acc_norm_stderr": 0.034099716973523674
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.02450980392156861,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.02450980392156861
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8270042194092827,
"acc_stderr": 0.024621562866768427,
"acc_norm": 0.8270042194092827,
"acc_norm_stderr": 0.024621562866768427
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6816143497757847,
"acc_stderr": 0.03126580522513713,
"acc_norm": 0.6816143497757847,
"acc_norm_stderr": 0.03126580522513713
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7709923664122137,
"acc_stderr": 0.036853466317118506,
"acc_norm": 0.7709923664122137,
"acc_norm_stderr": 0.036853466317118506
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
"acc_stderr": 0.03695980128098824,
"acc_norm": 0.7933884297520661,
"acc_norm_stderr": 0.03695980128098824
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7870370370370371,
"acc_stderr": 0.0395783547198098,
"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0395783547198098
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5089285714285714,
"acc_stderr": 0.04745033255489123,
"acc_norm": 0.5089285714285714,
"acc_norm_stderr": 0.04745033255489123
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.039166677628225836,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.039166677628225836
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
"acc_stderr": 0.022509033937077802,
"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.022509033937077802
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8365261813537676,
"acc_stderr": 0.013223928616741624,
"acc_norm": 0.8365261813537676,
"acc_norm_stderr": 0.013223928616741624
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7167630057803468,
"acc_stderr": 0.02425790170532338,
"acc_norm": 0.7167630057803468,
"acc_norm_stderr": 0.02425790170532338
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.41675977653631285,
"acc_stderr": 0.016489134962438954,
"acc_norm": 0.41675977653631285,
"acc_norm_stderr": 0.016489134962438954
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818737,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818737
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7009646302250804,
"acc_stderr": 0.026003301117885135,
"acc_norm": 0.7009646302250804,
"acc_norm_stderr": 0.026003301117885135
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7160493827160493,
"acc_stderr": 0.025089478523765137,
"acc_norm": 0.7160493827160493,
"acc_norm_stderr": 0.025089478523765137
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.02979071924382972,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.02979071924382972
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4595827900912647,
"acc_stderr": 0.01272844606766997,
"acc_norm": 0.4595827900912647,
"acc_norm_stderr": 0.01272844606766997
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6911764705882353,
"acc_stderr": 0.02806499816704009,
"acc_norm": 0.6911764705882353,
"acc_norm_stderr": 0.02806499816704009
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6683006535947712,
"acc_stderr": 0.019047485239360378,
"acc_norm": 0.6683006535947712,
"acc_norm_stderr": 0.019047485239360378
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6909090909090909,
"acc_stderr": 0.044262946482000985,
"acc_norm": 0.6909090909090909,
"acc_norm_stderr": 0.044262946482000985
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.028123429335142783,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.028123429335142783
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8507462686567164,
"acc_stderr": 0.025196929874827072,
"acc_norm": 0.8507462686567164,
"acc_norm_stderr": 0.025196929874827072
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.87,
"acc_stderr": 0.03379976689896309,
"acc_norm": 0.87,
"acc_norm_stderr": 0.03379976689896309
},
"harness|hendrycksTest-virology|5": {
"acc": 0.536144578313253,
"acc_stderr": 0.038823108508905954,
"acc_norm": 0.536144578313253,
"acc_norm_stderr": 0.038823108508905954
},
"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": 0.47613219094247244,
"mc1_stderr": 0.017483547156961574,
"mc2": 0.6295900737174474,
"mc2_stderr": 0.015194573521166509
},
"harness|winogrande|5": {
"acc": 0.8082083662194159,
"acc_stderr": 0.011065209664659527
},
"harness|gsm8k|5": {
"acc": 0.6345716451857468,
"acc_stderr": 0.013264282030266635
}
}
```
## 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]
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## Dataset Card Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
manishiitg/human_eval | ---
dataset_info:
features:
- name: type
dtype: string
- name: messages
dtype: string
- name: answer
dtype: string
- name: answer_english
dtype: string
- name: lang
dtype: string
- name: hi_answer
dtype: string
- name: mt_question
sequence: string
splits:
- name: train
num_bytes: 756440
num_examples: 734
download_size: 219666
dataset_size: 756440
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Evaluation on hindi and english prompts borrowed from teknimum, airoboros, https://huggingface.co/datasets/HuggingFaceH4/mt_bench_prompts, https://huggingface.co/datasets/ai4bharat/human-eval and other sources
Mainly used to evalaution on written tasks through LLM JUDGE
https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/README.md
|
tyzhu/eval_tag_nq_dev_v2 | ---
dataset_info:
features:
- name: question
dtype: string
- name: title
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
- name: answers
struct:
- name: answer_start
sequence: 'null'
- name: text
sequence: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 2372
num_examples: 10
- name: validation
num_bytes: 1672810
num_examples: 6515
download_size: 937279
dataset_size: 1675182
---
# Dataset Card for "eval_tag_nq_dev_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kunishou/oasst2-135k-ja | ---
license: apache-2.0
language:
- ja
---
**Update:**
- 2023/12/25
oasst2-135k-jaをチャット形式に変換した[oasst2-chat-68k-ja](https://huggingface.co/datasets/kunishou/oasst2-chat-68k-ja)を公開しました。
This dataset was created by automatically translating "OpenAssistant/oasst2" into Japanese by DeepL.
"OpenAssistant/oasst2" を DeepL翻訳を用いて日本語に自動翻訳したデータセットになります。
以下のコードを用いることで、 Instruction と Output （prompterの命令とassistantの回答）の形式に変換することができます。
ファインチューニングで使用する場合はこちらのコードで変換して下さい（変換には5分程度かかります）。
変換コード参考
https://github.com/h2oai/h2o-llmstudio/blob/5ebfd3879e226b4e1afd0a0b45eb632e60412129/app_utils/utils.py#L1888
```python
pip install datasets
```
```python
from datasets import load_dataset
import pandas as pd
import os
import json
# oasst2のオリジナルデータのロード
ds = load_dataset("OpenAssistant/oasst2")
train = ds["train"].to_pandas()
val = ds["validation"].to_pandas()
df_origin = pd.concat([train, val], axis=0).reset_index(drop=True)
# oasst1日本語翻訳データの読み込み
df_ja = load_dataset("kunishou/oasst2-135k-ja").to_pandas()
# oasst2のオリジナルデータと日本語翻訳データのマージ
df = pd.merge(df_origin, df_ja[["message_id", "text_ja"]], on="message_id", how="left").copy()
df["text"] = df["text_ja"]
df_assistant = df[(df.role == "assistant")].copy()
df_prompter = df[(df.role == "prompter")].copy()
df_prompter = df_prompter.set_index("message_id")
df_assistant["output"] = df_assistant["text"].values
inputs = []
parent_ids = []
for _, row in df_assistant.iterrows():
input = df_prompter.loc[row.parent_id]
inputs.append(input.text)
parent_ids.append(input.parent_id)
df_assistant["instruction"] = inputs
df_assistant["parent_id"] = parent_ids
df_assistant = df_assistant[
["instruction", "output", "message_id", "parent_id", "lang", "rank"]
].rename(columns={"message_id": "id"})
# これ以下でjsonファイルへ書き出し---------------
learn_datas = []
input_list = []
for n in range(len(df_assistant)):
learn_data = {
"instruction": str(df_assistant.iloc[n, 0]),
"input": "",
"output": ""
}
input_list.append(df_assistant.iloc[n, 0])
learn_data["input"] = ""
learn_data["output"] = str(df_assistant.iloc[n, 1])
learn_datas.append(learn_data)
json_learn_data = json.dumps(learn_datas, indent=4, ensure_ascii=False)
with open('oasst2_ja_converted.json', 'w', encoding="utf-8") as f:
f.write(json_learn_data)
```
OpenAssistant/oasst2
https://huggingface.co/datasets/OpenAssistant/oasst2 |
KaioSan/Stolas.zip | ---
license: mit
---
|
bigscience-data/roots_ca_parlament_parla | ---
language: ca
license: cc-by-4.0
extra_gated_prompt: 'By accessing this dataset, you agree to abide by the BigScience
Ethical Charter. The charter can be found at:
https://hf.co/spaces/bigscience/ethical-charter'
extra_gated_fields:
I have read and agree to abide by the BigScience Ethical Charter: checkbox
---
ROOTS Subset: roots_ca_parlament_parla
# parlament_parla
- Dataset uid: `parlament_parla`
### Description
### Homepage
### Licensing
### Speaker Locations
### Sizes
- 0.0000 % of total
- 0.0000 % of ca
### BigScience processing steps
#### Filters applied to: ca
- dedup_document
- dedup_template_soft
- filter_remove_empty_docs
- filter_small_docs_bytes_1024
|
alexandrainst/m_truthfulqa | ---
configs:
- config_name: ar
data_files:
- split: val
path: data/ar/val.jsonl
- config_name: bn
data_files:
- split: val
path: data/bn/val.jsonl
- config_name: ca
data_files:
- split: val
path: data/ca/val.jsonl
- config_name: da
data_files:
- split: val
path: data/da/val.jsonl
- config_name: de
data_files:
- split: val
path: data/de/val.jsonl
- config_name: es
data_files:
- split: val
path: data/es/val.jsonl
- config_name: eu
data_files:
- split: val
path: data/eu/val.jsonl
- config_name: fr
data_files:
- split: val
path: data/fr/val.jsonl
- config_name: gu
data_files:
- split: val
path: data/gu/val.jsonl
- config_name: hi
data_files:
- split: val
path: data/hi/val.jsonl
- config_name: hr
data_files:
- split: val
path: data/hr/val.jsonl
- config_name: hu
data_files:
- split: val
path: data/hu/val.jsonl
- config_name: hy
data_files:
- split: val
path: data/hy/val.jsonl
- config_name: id
data_files:
- split: val
path: data/id/val.jsonl
- config_name: it
data_files:
- split: val
path: data/it/val.jsonl
- config_name: kn
data_files:
- split: val
path: data/kn/val.jsonl
- config_name: ml
data_files:
- split: val
path: data/ml/val.jsonl
- config_name: mr
data_files:
- split: val
path: data/mr/val.jsonl
- config_name: ne
data_files:
- split: val
path: data/ne/val.jsonl
- config_name: nl
data_files:
- split: val
path: data/nl/val.jsonl
- config_name: pt
data_files:
- split: val
path: data/pt/val.jsonl
- config_name: ro
data_files:
- split: val
path: data/ro/val.jsonl
- config_name: ru
data_files:
- split: val
path: data/ru/val.jsonl
- config_name: sk
data_files:
- split: val
path: data/sk/val.jsonl
- config_name: sr
data_files:
- split: val
path: data/sr/val.jsonl
- config_name: sv
data_files:
- split: val
path: data/sv/val.jsonl
- config_name: ta
data_files:
- split: val
path: data/ta/val.jsonl
- config_name: te
data_files:
- split: val
path: data/te/val.jsonl
- config_name: uk
data_files:
- split: val
path: data/uk/val.jsonl
- config_name: vi
data_files:
- split: val
path: data/vi/val.jsonl
- config_name: zh
data_files:
- split: val
path: data/zh/val.jsonl
license: cc-by-nc-4.0
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
size_categories:
- 10K<n<100K
language:
- ar
- bn
- ca
- da
- de
- es
- eu
- fr
- gu
- hi
- hr
- hu
- hy
- id
- it
- kn
- ml
- mr
- ne
- nl
- pt
- ro
- ru
- sk
- sr
- sv
- ta
- te
- uk
- vi
- zh
---
# Multilingual TruthfulQA
## Dataset Summary
This dataset is a machine translated version of the [TruthfulQA dataset](https://huggingface.co/datasets/truthful_qa), translated using GPT-3.5-turbo. This dataset was created by the University of Oregon, and was originally uploaded to [this Github repository](https://github.com/nlp-uoregon/mlmm-evaluation).
## Citation
If you use this dataset in your work, please cite the following paper:
```bibtex
@article{dac2023okapi,
title={Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback},
author={Dac Lai, Viet and Van Nguyen, Chien and Ngo, Nghia Trung and Nguyen, Thuat and Dernoncourt, Franck and Rossi, Ryan A and Nguyen, Thien Huu},
journal={arXiv e-prints},
pages={arXiv--2307},
year={2023}
}
``` |
one-sec-cv12/chunk_202 | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
splits:
- name: train
num_bytes: 22293605232.375
num_examples: 232109
download_size: 20517956740
dataset_size: 22293605232.375
---
# Dataset Card for "chunk_202"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
davanstrien/autotrain-data-wikiart-sample | Invalid username or password. |
mesolitica/malaya-speech-malay-stt | ---
dataset_info:
features:
- name: filename
dtype:
audio:
sampling_rate: 16000
- name: Y
dtype: string
splits:
- name: train
num_bytes: 53095303942.242
num_examples: 1635599
download_size: 53282183764
dataset_size: 53095303942.242
---
# Malaya-Speech Speech-to-Text dataset
This dataset combined from semisupervised Google Speech-to-Text and private datasets.
Processing script https://github.com/mesolitica/malaya-speech/blob/master/pretrained-model/prepare-stt/prepare-malay-stt-train.ipynb
This repository is to centralize the dataset for https://malaya-speech.readthedocs.io/ |
pravsels/ManimML_helblazer811_code | ---
dataset_info:
features:
- name: file_path
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 505884
num_examples: 148
download_size: 162439
dataset_size: 505884
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
iix/mini_coco_linux | ---
license: mit
task_categories:
- text-classification
- text-generation
language:
- en
tags:
- code
pretty_name: '*'
size_categories:
- 0.001M<n<0.0011M
---
# mini coco dataset files
# Required dependencies
```
OpenCV (cv2)
matplotlib
ipywidgets
```
# img_data.psv
Extract of the coco dataset containing the following labels: ```["airplane", "backpack", "cell phone", "handbag", "suitcase", "knife", "laptop", "car"]``` (300 of each)
```
Structured as follows:
| Field | Description |
| --------------- | --------------------------------------------------------------------------------------------------- |
| file_name | Name of image file (.png) |
| height | Image height prior to padding |
| width | Image width prior to padding |
| annotations | Array of boundary box array, label pairs. Bbox arrays are of the form [x_min, y_min, width, height] |
1.09k rows
```
# /data (folder)
This directory contains a selection of zero-padded COCO images that correspond to img_data.parquet, image names are of the following format:
```
xxxxxx.png
```
# display_boundary.py
Allows images to be viewed with their boundary boxes, don't need to pay attention to how it works.
```
- Intended to run in tandem with jupyter notebook.
- Takes img_name.png as input, inspect img_data.psv or /data for image names.
```
If you have any questions or issues, feel free to keep them to yourself. |
MelioAI/safety-qa-sample | ---
license: cc-by-nc-4.0
dataset_info:
features:
- name: instruction
dtype: string
- name: response
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 219061.38757718852
num_examples: 500
- name: valid
num_bytes: 43812.2775154377
num_examples: 100
- name: test
num_bytes: 43812.2775154377
num_examples: 100
download_size: 197329
dataset_size: 306685.9426080639
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
This is a very small sample from PKU-Alignment/PKU-SafeRLHF-10K that has been processed for QA. |
open-llm-leaderboard/details_aisquared__chopt-1_3b | ---
pretty_name: Evaluation run of aisquared/chopt-1_3b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [aisquared/chopt-1_3b](https://huggingface.co/aisquared/chopt-1_3b) 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_aisquared__chopt-1_3b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-25T02:11:14.117719](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__chopt-1_3b/blob/main/results_2023-10-25T02-11-14.117719.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\": 0.002936241610738255,\n\
\ \"em_stderr\": 0.0005541113054710093,\n \"f1\": 0.046667365771812144,\n\
\ \"f1_stderr\": 0.0012971244615236355,\n \"acc\": 0.2912391475927388,\n\
\ \"acc_stderr\": 0.006929989132220124\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.002936241610738255,\n \"em_stderr\": 0.0005541113054710093,\n\
\ \"f1\": 0.046667365771812144,\n \"f1_stderr\": 0.0012971244615236355\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5824782951854776,\n\
\ \"acc_stderr\": 0.013859978264440248\n }\n}\n```"
repo_url: https://huggingface.co/aisquared/chopt-1_3b
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_19T14_44_06.685040
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_25T02_11_14.117719
path:
- '**/details_harness|drop|3_2023-10-25T02-11-14.117719.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-25T02-11-14.117719.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_25T02_11_14.117719
path:
- '**/details_harness|gsm8k|5_2023-10-25T02-11-14.117719.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-25T02-11-14.117719.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:44:06.685040.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:44:06.685040.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:44:06.685040.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_25T02_11_14.117719
path:
- '**/details_harness|winogrande|5_2023-10-25T02-11-14.117719.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-25T02-11-14.117719.parquet'
- config_name: results
data_files:
- split: 2023_07_19T14_44_06.685040
path:
- results_2023-07-19T14:44:06.685040.parquet
- split: 2023_10_25T02_11_14.117719
path:
- results_2023-10-25T02-11-14.117719.parquet
- split: latest
path:
- results_2023-10-25T02-11-14.117719.parquet
---
# Dataset Card for Evaluation run of aisquared/chopt-1_3b
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/aisquared/chopt-1_3b
- **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 [aisquared/chopt-1_3b](https://huggingface.co/aisquared/chopt-1_3b) 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_aisquared__chopt-1_3b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-25T02:11:14.117719](https://huggingface.co/datasets/open-llm-leaderboard/details_aisquared__chopt-1_3b/blob/main/results_2023-10-25T02-11-14.117719.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": 0.002936241610738255,
"em_stderr": 0.0005541113054710093,
"f1": 0.046667365771812144,
"f1_stderr": 0.0012971244615236355,
"acc": 0.2912391475927388,
"acc_stderr": 0.006929989132220124
},
"harness|drop|3": {
"em": 0.002936241610738255,
"em_stderr": 0.0005541113054710093,
"f1": 0.046667365771812144,
"f1_stderr": 0.0012971244615236355
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5824782951854776,
"acc_stderr": 0.013859978264440248
}
}
```
### 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] |
oumo-os/ugalang_0 | ---
language: en
license: mit
tags:
- translation
- east-african-languages
- english
- bible-texts
datasets:
- name: ugalang_0
description: >
The ugalang_0 dataset contains Bible texts translated into East African
languages, including English.
It can be used for various translation tasks and language-related research
in the context of East African languages.
Languages included in the dataset:
- Daasanach
- Masaaba
- Rendille
- Ganda
- Aringa
- Kakwa
- Lugbara
- Talinga-Bwisi
- Samburu
- Lango
- Rundi
- Swahili
- Ateso
- Somali
- English
- Chidigo
- Kinyarwanda
- Gwere
- Acholi
- Kumam
- Jopadhola
- Keliko
- Suba
- Gungu
- Soga
- Nyankore
- Kipfokomo
- Ng'akarimojong
- Nyole
- Kiswahili
- Alur English
task_categories:
- machine-translation
- natural-language-understanding
- multilingual
languages:
- Daasanach
- Masaaba
- Rendille
- Ganda
- Aringa
- Kakwa
- Lugbara
- Talinga-Bwisi
- Samburu
- Lango
- Rundi
- Swahili
- Ateso
- Somali
- English
- Chidigo
- Kinyarwanda
- Gwere
- Acholi
- Kumam
- Jopadhola
- Keliko
- Suba
- Gungu
- Soga
- Nyankore
- Kipfokomo
- Ng'akarimojong
- Nyole
- Kiswahili
- Alur English
licenses:
- MIT
size_in_bytes: <size_in_bytes>
download_size_in_bytes: <download_size_in_bytes>
task_ids:
- machine-translation
- language-modeling
huggingface_hub:
- repository: <link_to_huggingface_hub_repository>
commit: <commit_sha>
---
The ugalang_0 dataset contains Bible texts translated into East African languages, including English. It can be used for various translation tasks and language-related research in the context of East African languages.
## Dataset Details
- Languages: Daasanach, Masaaba, Rendille, Ganda, Aringa, Kakwa, Lugbara, Talinga-Bwisi, Samburu, Lango, Rundi, Swahili, Ateso, Somali, English, Chidigo, Kinyarwanda, Gwere, Acholi, Kumam, Jopadhola, Keliko, Suba, Gungu, Soga, Nyankore, Kipfokomo, Ng'akarimojong, Nyole, Kiswahili, Alur English
- License: MIT
## Dataset Preparation
The dataset was created by collecting Bible texts translated into various East African languages, including English. The texts were obtained from open-source sources with permission to use for research purposes. |
LimYeri/LeetCode_YT_CC_CoT_Summary | ---
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- text-classification
- text-generation
pretty_name: LeetCode Information & YouTube Captions with CoT Summaries
tags:
- code
dataset_info:
features:
- name: cc_content
dtype: string
- name: id
dtype: int64
- name: thumbnail
dtype: string
- name: title
dtype: string
- name: question_content
dtype: string
- name: java
dtype: string
- name: c++
dtype: string
- name: python
dtype: string
- name: javascript
dtype: string
- name: title_slug
dtype: string
- name: tag
dtype: string
- name: level
dtype: string
- name: success_rate
dtype: float64
- name: total_submission
dtype: float64
- name: total_accepted
dtype: float64
- name: question_likes
dtype: float64
- name: question_dislikes
dtype: float64
- name: question_hints
dtype: string
- name: similar_question_ids
dtype: string
- name: num_tokens
dtype: int64
- name: Summary
dtype: string
splits:
- name: train
num_bytes: 522714622
num_examples: 17053
download_size: 144407580
dataset_size: 522714622
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
**LeetCode Information & YouTube Captions with CoT Summaries**
Use this data(as a team) -> [kreimben/leetcode_with_youtube_captions](https://huggingface.co/datasets/kreimben/leetcode_with_youtube_captions)
Calculate the number of tokens in ['cc_content'] using "tiktoken" -> new column ['num_token']
The original ['cc_content'] column had tokens that were too long and contained a lot of repetition, which necessitated summarization.
Consequently, our team (Project Team: CodeMind) used a commercialized LLM to summarize the ['cc_content'] column data using the Chain of Thought (CoT) technique. -> **new column ['Summary']** |
BByrneLab/OKVQA_FLMR_preprocessed_GoogleSearch_passages | ---
dataset_info:
features:
- name: passage_content
dtype: string
- name: passage_id
dtype: string
splits:
- name: train_passages
num_bytes: 42054675
num_examples: 112724
- name: valid_passages
num_bytes: 61645843
num_examples: 168306
- name: test_passages
num_bytes: 61645843
num_examples: 168306
download_size: 98987092
dataset_size: 165346361
configs:
- config_name: default
data_files:
- split: train_passages
path: data/train_passages-*
- split: valid_passages
path: data/valid_passages-*
- split: test_passages
path: data/test_passages-*
---
|
philschmid/dolly-15k-oai-style | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 12278400
num_examples: 15011
download_size: 7243728
dataset_size: 12278400
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolly-15k-oai-style"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vsokolovskii/slue | ---
license: cc0-1.0
---
|
wtcherr/diffusiondb_2m_first_5k_canny | ---
dataset_info:
features:
- name: image
dtype: image
- name: guide
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3099092166.0
num_examples: 5000
download_size: 3372636682
dataset_size: 3099092166.0
---
# Dataset Card for "diffusiondb_2m_first_5k_canny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
edwardgiamphy/Noisy-MSMARCO-Passage-Ranking | ---
license: cc-by-4.0
---
This link gathers 72 noisy versions of the MS-Marco-Passage Ranking dataset consisting of three noise types (insertion, deletion, substitution), two different distributions of errors in the text (Batch 1 where errors are distributed in few words in the text and Batch2 where errors are more evenly spread out between words) and 12 different intensities of noise (CER varying from 3% to 36% with intervals of 3%).
The exact dataset that has been used is the MS-Marco-passagetest2020-top1000. The original dataset is available at https://msmarco.blob.core.windows.net/msmarcoranking/msmarco-passagetest2020-top1000.tsv.gz.
This dataset has been built using the test set of the dataset MS MARCO Passage ranking from the paper @article{bajaj2016ms,
title={Ms marco: A human generated machine reading comprehension dataset},
author={Bajaj, Payal and Campos, Daniel and Craswell, Nick and Deng, Li and Gao, Jianfeng and Liu, Xiaodong and Majumder, Rangan and McNamara, Andrew and Mitra, Bhaskar and Nguyen, Tri and others},
journal={arXiv preprint arXiv:1611.09268},
year={2016}
}.
This dataset has been built using the library nlpaug (https://github.com/makcedward/nlpaug) to inject noise augmentations in the dataset's text. |
CyberHarem/wo_class_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of wo_class/空母ヲ級 (Kantai Collection)
This is the dataset of wo_class/空母ヲ級 (Kantai Collection), containing 11 images and their tags.
The core tags of this character are `long_hair, pale_skin, white_hair, blue_eyes, glowing_eyes, hat, breasts, aqua_eyes, grey_hair, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:----------|:---------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 11 | 20.35 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wo_class_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 11 | 10.10 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wo_class_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 24 | 20.96 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wo_class_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 11 | 17.24 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wo_class_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 24 | 32.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/wo_class_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/wo_class_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 | 11 |  |  |  |  |  | 1girl, abyssal_ship, bodysuit, cape, solo, looking_at_viewer, glowing, black_gloves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | abyssal_ship | bodysuit | cape | solo | looking_at_viewer | glowing | black_gloves |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------------|:-----------|:-------|:-------|:--------------------|:----------|:---------------|
| 0 | 11 |  |  |  |  |  | X | X | X | X | X | X | X | X |
|
dlee1994yk/taxi_dataset | ---
dataset_info:
features:
- name: key
dtype: string
- name: pickup_datetime
dtype: string
- name: pickup_longitude
dtype: float64
- name: pickup_latitude
dtype: float64
- name: dropoff_longitude
dtype: float64
- name: dropoff_latitude
dtype: float64
- name: passenger_count
dtype: int64
splits:
- name: test
num_bytes: 977751
num_examples: 9914
download_size: 521219
dataset_size: 977751
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
|
deepinv/images | ---
license: bsd-3-clause
---
|
Seanxh/twitter_dataset_1713214191 | ---
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: 183324
num_examples: 429
download_size: 64867
dataset_size: 183324
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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