datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
ml4pubmed/pubmed-text-classification-cased | ---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- pubmed
size_categories:
- 1M<n<10M
source_datasets: pubmed
---
# ml4pubmed/pubmed-text-classification-cased
A parsed/cleaned version of the source data retaining case. |
Kingslayer5437/BGL | ---
license: gpl-3.0
---
|
thesudio/3DPack | ---
license: unknown
---
|
zh-tw-llm-dv-dv/zh-tw-llm-dev-sample-ta8k-f6dd50-embeddings-tr_alp-61d3e1-c2048 | ---
dataset_info:
dataset_size: 453739.0
download_size: 189056
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
- dtype: string
name: preview
splits:
- name: train
num_bytes: 453739.0
num_examples: 300
---
# zh-tw-llm-dev-sample-ta8k-f6dd50-embeddings-tr_alp-61d3e1-c2048
This dataset is a part of the `zh-tw-llm-dev` project.
* Tokenizer: `zh-tw-llm-dev-sample-tokenizer-a8k-f6dd50`
* Built with: `translations`, `alpaca`
* Rows: `300`
* Max length: `2048`
* Full config:
```json
{"build_with": ["translations", "alpaca"], "preview_length": 256, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "rows_limit": 100}, "alpaca_settings": {"source_dataset": "zetavg/traditional-chinese-alpaca-en-align", "template": "short", "rows_limit": 100}}
``` |
tyzhu/squad_qa_title_v5_full_random_permute_1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 4293156.8345323745
num_examples: 2875
- name: validation
num_bytes: 353148
num_examples: 300
download_size: 1183249
dataset_size: 4646304.8345323745
---
# Dataset Card for "squad_qa_title_v5_full_random_permute_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/data-standardized_cluster_19_alpaca | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 8924497
num_examples: 4276
download_size: 3801177
dataset_size: 8924497
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "data-standardized_cluster_19_alpaca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
amu-cai/pl-asr-bigos-v2 | ---
annotations_creators:
- crowdsourced
- expert-generated
- other
- machine-generated
language:
- pl
language_creators:
- crowdsourced
- expert-generated
- other
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: pl-asr-bigos
size_categories:
- 10K<n<100K
source_datasets:
- original
- extended|multilingual_librispeech
- extended|common_voice
- extended|minds14
- extended|fleurs
tags:
- benchmark
- polish
- asr
- speech
- dataset
- audio
task_categories:
- automatic-speech-recognition
task_ids: []
extra_gated_prompt: |-
Original datasets used for curation of BIGOS have specific terms of usage that must be understood and agreed to before use. Below are the links to the license terms and datasets the specific license type applies to:
* [Creative Commons 0](https://creativecommons.org/share-your-work/public-domain/cc0) which applies to [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0)
* [Creative Commons By Attribution Share Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/), which applies to [Clarin Cyfry](https://clarin-pl.eu/dspace/handle/11321/317), [Azon acoustic speech resources corpus](https://zasobynauki.pl/zasoby/korpus-nagran-probek-mowy-do-celow-budowy-modeli-akustycznych-dla-automatycznego-rozpoznawania-mowy,53293/).
* [Creative Commons By Attribution 3.0](https://creativecommons.org/licenses/by/3.0/), which applies to [CLARIN Mobile database](https://clarin-pl.eu/dspace/handle/11321/237), [CLARIN Studio database](https://clarin-pl.eu/dspace/handle/11321/236), [PELCRA Spelling and Numbers Voice Database](http://pelcra.pl/new/snuv) and [FLEURS dataset](https://huggingface.co/datasets/google/fleurs)
* [Creative Commons By Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), which applies to [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) and [Poly AI Minds 14](https://huggingface.co/datasets/PolyAI/minds14)
* [Proprietiary License of Munich AI Labs dataset](https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset)
* Public domain mark, which applies to [PWR datasets](https://www.ii.pwr.edu.pl/~sas/ASR/)
To use selected dataset, you also need to fill in the access forms on the specific datasets pages:
* Common Voice: https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0
extra_gated_fields:
I hereby confirm that I have read and accepted the license terms of datasets comprising BIGOS corpora: checkbox
I hereby confirm that I have registered on the original Common Voice page and agree to not attempt to determine the identity of speakers in the Common Voice dataset: checkbox
---
# Dataset Card for Polish ASR BIGOS corpora
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/amu-cai/pl-asr-bigos-v2
- **Repository:** https://github.com/goodmike31/pl-asr-bigos-tools
- **Paper:** https://annals-csis.org/proceedings/2023/drp/1609.html
- **Leaderboard:** https://huggingface.co/spaces/michaljunczyk/pl-asr-bigos-benchmark
- **Point of Contact:** michal.junczyk@amu.edu.pl
### Dataset Summary
The BIGOS (Benchmark Intended Grouping of Open Speech) corpora aims at simplifying the access and use of publicly available ASR speech datasets for Polish.<br>
### Supported Tasks and Leaderboards
* Open Polish ASR challenge [PolEval](http://poleval.pl/) using BIGOS V2 and [PELCRA for BIGOS](https://huggingface.co/datasets/pelcra/pl-asr-pelcra-for-bigos) datasets
* Evaluation of 3 commercial and 5 freely available on [BIGOS V1](https://huggingface.co/datasets/michaljunczyk/pl-asr-bigos) [(paper)](https://annals-csis.org/proceedings/2023/drp/1609.html).
Continous benchmark and leaderboard of PL ASR systems using BIGOS corpora is planned for 2024.<br>
### Languages
Polish
## Dataset Structure
The datasets consist of audio recordings in the WAV format with corresponding metadata.<br>
The audio and metadata can be used in a raw format (TSV) or via the Hugging Face datasets library.<br>
References for the test split will only become available after the completion of the 2024 PolEval challenge.<br>
### Data Instances
The train set consists of 82 025 samples.
The dev set consists of 14 254 samples
The test set consists of 14 993 samples.
### Data Fields
Available fields:
* `audioname` - file identifier
* `split` - test, validation or train split
* `dataset` - source dataset identifier
* `audio` - binary representation of audio file
* `ref_orig` - original transcription of audio file
* `samplingrate_orig` - sampling rate of the original recording
* `sampling_rate` - sampling rate of recording in the release
* `audiopath_bigos` - relative filepath to audio file extracted from tar.gz archive
* `audiopath_local` - absolute filepath to audio file extracted with the build script
* `spk_sex_source` - sex of the speaker extracted from the source meta-data (N/A if not available)
* `spk_age_source` - age group of the speaker (in CommonVoice format) extracted from the source (N/A if not available)
<br><br>
### Data Splits
Train split contains recordings intendend for training.
Validation split contains recordings for validation during training procedure.
Test split contains recordings intended for evaluation only.
References for test split are not available until the completion of 23/24 PolEval challenge.
| Subset | train | validation | test |
| -------------------------- | ------ | ---------- | ----- |
| fair-mls-20 | 25 042 | 511 | 519 |
| google-fleurs-22 | 2 841 | 338 | 758 |
| mailabs-corpus_librivox-19 | 11 834 | 1 527 | 1 501 |
| mozilla-common_voice_15-23 | 19 119 | 8 895 | 8 896 |
| pjatk-clarin_studio-15 | 10 999 | 1 407 | 1 404 |
| pjatk-clarin_mobile-15 | 2 861 | 242 | 392 |
| polyai-minds14-21 | 462 | 47 | 53 |
| pwr-maleset-unk | 3 783 | 478 | 477 |
| pwr-shortwords-unk | 761 | 86 | 92 |
| pwr-viu-unk | 2 146 | 290 | 267 |
| pwr-azon_read-20 | 1 820 | 382 | 586 |
| pwr-azon_spont-20 | 357 | 51 | 48 |
## Dataset Creation
### Curation Rationale
[Polish ASR Speech Data Catalog](https://github.com/goodmike31/pl-asr-speech-data-survey) was used to identify suitable datasets which can be repurposed and included in the BIGOS corpora.<br>
The following mandatory criteria were considered:
* Dataset must be downloadable.
* The license must allow for free, noncommercial use.
* Transcriptions must be available and align with the recordings.
* The sampling rate of audio recordings must be at least 8 kHz.
* Audio encoding using a minimum of 16 bits per sample.
Recordings which either lacked transcriptions or were too short to be useful for training or evaluation were removed during curation.
### Source Data
12 datasets that meet the criteria were chosen as sources for the BIGOS dataset.
* The Common Voice dataset version 15 (mozilla-common_voice_15-23)
* The Multilingual LibriSpeech (MLS) dataset (fair-mls-20)
* The Clarin Studio Corpus (pjatk-clarin_studio-15)
* The Clarin Mobile Corpus (pjatk-clarin_mobile-15)
* The Jerzy Sas PWR datasets from Politechnika Wrocławska (pwr-viu-unk, pwr-shortwords-unk, pwr-maleset-unk). More info [here](https://www.ii.pwr.edu.pl/)
* The Munich-AI Labs Speech corpus (mailabs-corpus-librivox-19)
* The AZON Read and Spontaneous Speech Corpora (pwr-azon_spont-20, pwr-azon_read-20) More info [here](https://zasobynauki.pl/zasoby/korpus-nagran-probek-mowy-do-celow-budowy-modeli-akustycznych-dla-automatycznego-rozpoznawania-mowy)
* The Google FLEURS dataset (google-fleurs-22)
* The PolyAI minds14 dataset (polyai-minds14-21)
<br>
#### Initial Data Collection and Normalization
Source text and audio files were extracted and encoded in a unified format.<br>
Dataset-specific transcription norms are preserved, including punctuation and casing. <br>
In case of original dataset does not have test, dev, train splits provided, the splits were generated pseudorandomly during curation. <br>
<br>
#### Who are the source language producers?
1. Clarin corpora - Polish Japanese Academy of Technology
2. Common Voice - Mozilla foundation
3. Multlingual librispeech - Facebook AI research lab
4. Jerzy Sas and AZON datasets - Politechnika Wrocławska
5. Google - FLEURS
6. PolyAI London - Minds14
Please refer to the [BIGOS V1 paper](https://annals-csis.org/proceedings/2023/drp/1609.html) for more details.
### Annotations
#### Annotation process
Current release contains original transcriptions.
Manual transcriptions of subsets and release of diagnostic dataset are planned for subsequent releases.
#### Who are the annotators?
Depends on the source dataset.
### Personal and Sensitive Information
This corpus does not contain PII or Sensitive Information.
All IDs pf speakers are anonymized.
## Considerations for Using the Data
### Social Impact of Dataset
To be updated.
### Discussion of Biases
To be updated.
### Other Known Limitations
The dataset in the initial release contains only a subset of recordings from original datasets.
## Additional Information
### Dataset Curators
Original authors of the source datasets - please refer to [source-data](#source-data) for details.
Michał Junczyk (michal.junczyk@amu.edu.pl) - curator of BIGOS corpora.
### Licensing Information
The BIGOS corpora is available under [Creative Commons By Attribution Share Alike 4.0 license.](https://creativecommons.org/licenses/by-sa/4.0/)
Original datasets used for curation of BIGOS have specific terms of usage that must be understood and agreed to before use. Below are the links to the license terms and datasets the specific license type applies to:
* [Creative Commons 0](https://creativecommons.org/share-your-work/public-domain/cc0) which applies to [Common Voice](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0)
* [Creative Commons By Attribution Share Alike 4.0](https://creativecommons.org/licenses/by-sa/4.0/), which applies to [Clarin Cyfry](https://clarin-pl.eu/dspace/handle/11321/317), [Azon acoustic speech resources corpus](https://zasobynauki.pl/zasoby/korpus-nagran-probek-mowy-do-celow-budowy-modeli-akustycznych-dla-automatycznego-rozpoznawania-mowy,53293/).
* [Creative Commons By Attribution 3.0](https://creativecommons.org/licenses/by/3.0/), which applies to [CLARIN Mobile database](https://clarin-pl.eu/dspace/handle/11321/237), [CLARIN Studio database](https://clarin-pl.eu/dspace/handle/11321/236), [PELCRA Spelling and Numbers Voice Database](http://pelcra.pl/new/snuv) and [FLEURS dataset](https://huggingface.co/datasets/google/fleurs)
* [Creative Commons By Attribution 4.0](https://creativecommons.org/licenses/by/4.0/), which applies to [Multilingual Librispeech](https://huggingface.co/datasets/facebook/multilingual_librispeech) and [Poly AI Minds 14](https://huggingface.co/datasets/PolyAI/minds14)
* [Proprietiary License of Munich AI Labs dataset](https://www.caito.de/2019/01/03/the-m-ailabs-speech-dataset)
* Public domain mark, which applies to [PWR datasets](https://www.ii.pwr.edu.pl/~sas/ASR/)
### Citation Information
Please cite using [Bibtex](https://dblp.org/rec/conf/fedcsis/Junczyk23.html?view=bibtex)
### Contributions
Thanks to [@goodmike31](https://github.com/goodmike31) for adding this dataset. |
Asap7772/education_sft | ---
dataset_info:
features:
- name: model
dtype: string
- name: x
dtype: string
- name: y
dtype: string
splits:
- name: train
num_bytes: 466510.90476190473
num_examples: 302
- name: test
num_bytes: 52521.09523809524
num_examples: 34
download_size: 290472
dataset_size: 519032.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Dsender/antest | ---
license: creativeml-openrail-m
---
|
MartinLubenov/autotrain-data-big-data-chest | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: big-data-chest
## Dataset Description
This dataset has been automatically processed by AutoTrain for project big-data-chest.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<2090x1858 L PIL image>",
"target": 0
},
{
"image": "<1422x1152 L PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['NORMAL', 'PNEUMONIA'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 298 |
| valid | 198 |
|
tazarov/test | ---
language: en
license: mit
size_categories:
- n<1K
pretty_name: Chroma export of collection test
dataset_info:
features:
- name: id
dtype: string
- name: embedding
sequence: float32
- name: document
dtype: string
splits:
- name: train
num_bytes: 6533201
num_examples: 1000
download_size: 6978967
dataset_size: 6533201
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
x-chroma:
description: Chroma Dataset for collection test
collection: test
metadata: None
---
# Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
chabud-team/chabud-extra | ---
license: openrail
---
|
sam2ai/social_i_qa_1_9k | ---
license: apache-2.0
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answerA
dtype: string
- name: answerB
dtype: string
- name: answerC
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 2725
num_examples: 6
- name: validation
num_bytes: 3274
num_examples: 6
download_size: 14452
dataset_size: 5999
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
|
Asap7772/ultrafeedback_binarized_relabelled_ultrarm | ---
configs:
- config_name: default
data_files:
- split: train_prefs
path: data/train_prefs-*
- split: test_prefs
path: data/test_prefs-*
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
- name: reward_chosen
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- name: reward_rejected
dtype: float64
splits:
- name: train_prefs
num_bytes: 405566392
num_examples: 61135
- name: test_prefs
num_bytes: 13157585
num_examples: 2000
download_size: 235095739
dataset_size: 418723977
---
# Dataset Card for "ultrafeedback_binarized_relabelled_ultrarm"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
AdapterOcean/med_alpaca_standardized_cluster_45 | ---
dataset_info:
features:
- name: text
dtype: string
- name: conversation_id
dtype: int64
- name: embedding
sequence: float64
- name: cluster
dtype: int64
splits:
- name: train
num_bytes: 82543585
num_examples: 8300
download_size: 24176156
dataset_size: 82543585
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "med_alpaca_standardized_cluster_45"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aimona/eng-conversations_no-tokenizer | ---
dataset_info:
features:
- name: input
dtype: string
- name: instructions
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 647195713
num_examples: 30052
download_size: 247595314
dataset_size: 647195713
---
# Dataset Card for "eng-conversations_no-tokenizer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ChanceFocus/flare-tatqa | ---
dataset_info:
features:
- name: id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
- name: text
dtype: string
splits:
- name: test
num_bytes: 3510146
num_examples: 1668
download_size: 0
dataset_size: 3510146
---
# Dataset Card for "flare-tatqa"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
teneriffa/kowikitext-20240301 | ---
license: cc-by-sa-4.0
language:
- ko
---
https://github.com/lovit/kowikitext 에 있는 코드를 사용하여 만든 한국어 위키피디아(https://ko.wikipedia.org) 데이터셋입니다.
데이터셋의 데이터 원본은 https://dumps.wikimedia.org/kowiki/20240301/kowiki-20240301-pages-articles-multistream.xml.bz2 와 https://dumps.wikimedia.org/kowiki/20240301/kowiki-20240301-pages-articles-multistream-index.txt.bz2 입니다.
데이터에 대한 저작원은 한국어 위키피디아 저작권인 CC-BY-SA-4.0 이 동일하게 적용됩니다.
kowikipedia_20240301.train 은 8GB 로 매우 커서 zip 으로 압축했습니다. |
alwanrahmana/NER_10_Labels | ---
license: unknown
---
|
roszcz/maestro-base-v2 | ---
dataset_info:
features:
- name: notes
struct:
- name: end
sequence: float64
- name: pitch
sequence: int64
- name: start
sequence: float64
- name: velocity
sequence: int64
- name: control_changes
struct:
- name: number
sequence: int64
- name: time
sequence: float64
- name: value
sequence: int64
- name: source
dtype: string
splits:
- name: validation
num_bytes: 53035261.55642633
num_examples: 137
- name: test
num_bytes: 68520009.45611285
num_examples: 177
- name: train
num_bytes: 372408186.9874608
num_examples: 962
download_size: 141530448
dataset_size: 493963458.0
---
# Dataset Card for "maestro-base-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sproos/scifact-es | ---
configs:
- config_name: default
data_files:
- split: queries
path: data/queries-*
- split: corpus
path: data/corpus-*
dataset_info:
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_bytes: 139085
num_examples: 1109
- name: corpus
num_bytes: 9174934
num_examples: 5183
download_size: 76742
dataset_size: 9314019
---
# Dataset Card for "scifact-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
LawInformedAI/am_samoa_case_law | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 18933000
num_examples: 2171
download_size: 9873706
dataset_size: 18933000
---
# Dataset Card for "am_samoa_case_law_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
IlyaGusev/pippa_ru | ---
language:
- ru
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- conversational
pretty_name: PIPPA Russian
tags:
- not-for-all-audiences
- conversational
- roleplay
dataset_info:
- config_name: default
features:
- name: gpt_35_turbo_result
dtype: string
- name: gpt_35_turbo_explanation
dtype: string
- name: translation_model
dtype: string
- name: bot_name
dtype: string
- name: bot_definitions
dtype: string
- name: orig_bot_definitions
dtype: string
- name: bot_description
dtype: string
- name: orig_bot_description
dtype: string
- name: conversation
list:
- name: is_human
dtype: bool
- name: message
dtype: string
- name: orig_conversation
list:
- name: is_human
dtype: bool
- name: message
dtype: string
splits:
- name: train
num_bytes: 96828729
num_examples: 6624
download_size: 48761680
dataset_size: 96828729
---
Russian translation of [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) dataset.
|
silverliningeda/silverliningeda-dataset-test | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 160183
num_examples: 500
download_size: 3028
dataset_size: 160183
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "silverliningeda-dataset-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
nijatzeynalov/azerbaijani-multi-news | ---
extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects."
extra_gated_fields:
Name and Surname: text
Email: text
Purpose: text
I agree to use this dataset for non-commercial use ONLY: checkbox
license: creativeml-openrail-m
task_categories:
- summarization
language:
- az
pretty_name: Azerbaijani News Summary Dataset Card
---
# Azerbaijani News Summary Dataset Card
## Dataset Summary
I present __az-news-summary__, a comprehensive and diverse dataset comprising __143k (143,448)__ Azerbaijani news articles extracted using a set of carefully designed heuristics. The dataset covers common topics for news reports include war, government, politics, education, health, the environment, economy, business, fashion, entertainment, and sport, as well as quirky or unusual events.
The dataset is prepared for Abstractive/Extractive summarization tasks. It can also be used in other scopes like Text Generation, Title Generation and etc.
## Dataset Structure
One example from the dataset is given below in JSON format.
```json
{'id': 33885080,
'title': 'İsmayıllı silkələndi - Zəlzələ',
'summary': 'Avqustun 11-də İsmayıllı rayonu ərazisində zəlzələ baş verib',
'text': 'Azərbaycan milli elmlər akademiyası nəzdində respublika seysmoloji
xidmət mərkəzindən bildirilib ki, ilkin məlumatlara əsasən yeraltı təkanlar
yerli vaxtla saat 23:03:11-də pirquludan 11 kilometr qərbdə i̇smayıllı ərazisində
qeydə alınıb.ocağı 9 kilometr dərinlikdə yerləşən zəlzələ episentrdə 4 bal,
ətraf rayonlarda isə 3 bala qədər hiss olunub.'}
```
## Data Fields
- `id`: ID of the news.
- `title`: The title of the news.
- `summary`: The summary of the news.
- `text`: The body of the news.
## Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts are white space based.
| Dataset Split | Number of Instances | Size (MB) |
| ------------- | --------------------|:----------------------|
| Train | 100,413 | 150 |
| Validation | 14,344 | 21.3 |
| Test | 28,691 | 42.8 |
## Usage
Usage is easy and takes only a few minutes. Firstly, you need to use install datasets library as follows:
```python
!pip install datasets
```
To load the dataset from the library, you need to pass the file name on the load_dataset() function. In this case:
```python
from datasets import load_dataset
dataset = load_dataset("nijatzeynalov/azerbaijani-multi-news")
```
## Dataset Curator
This dataset was curated by [Nijat Zeynalov](https://www.linkedin.com/in/nijat-zeynalov-064163142/)
# Citation Information
```bibtex
@misc {nijatzeynalov_2023,
author = { {NijatZeynalov} },
title = { azerbaijani-multi-news (Revision 2afa300) },
year = 2023,
url = { https://huggingface.co/datasets/nijatzeynalov/azerbaijani-multi-news },
doi = { 10.57967/hf/0312 },
publisher = { Hugging Face }
}
``` |
BhabhaAI/openhermes-2.5-hindi | ---
task_categories:
- text-generation
language:
- hi
size_categories:
- 100K<n<1M
---
## OpenHermes-2.5-Hindi
~600K rows Translated & filtered by Satpal Singh Rathore, Manav Manoj |
CyberHarem/takao_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of takao/高雄/高雄 (Kantai Collection)
This is the dataset of takao/高雄/高雄 (Kantai Collection), containing 500 images and their tags.
The core tags of this character are `black_hair, short_hair, red_eyes, breasts, large_breasts, hat, beret, blue_headwear`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 608.78 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takao_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 345.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takao_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1219 | 741.72 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takao_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 538.09 MiB | [Download](https://huggingface.co/datasets/CyberHarem/takao_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1219 | 1.02 GiB | [Download](https://huggingface.co/datasets/CyberHarem/takao_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/takao_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 | 6 |  |  |  |  |  | 1girl, blue_jacket, military_uniform, simple_background, solo, upper_body, white_background, looking_at_viewer, black_gloves, dated, long_sleeves, smile, white_ascot, one-hour_drawing_challenge, twitter_username |
| 1 | 9 |  |  |  |  |  | 1girl, black_thighhighs, garter_straps, military_uniform, solo, black_gloves, looking_at_viewer, skirt, smile, cannon, turret |
| 2 | 10 |  |  |  |  |  | 1girl, ascot, black_gloves, black_thighhighs, blue_skirt, garter_straps, long_sleeves, looking_at_viewer, military_uniform, miniskirt, solo, simple_background, white_background, blue_jacket, open_mouth, blush, cowboy_shot, smile, twitter_username |
| 3 | 5 |  |  |  |  |  | 1girl, black_gloves, black_thighhighs, garter_straps, military_uniform, simple_background, solo, white_background, ascot, miniskirt, looking_at_viewer, cowboy_shot, open_mouth |
| 4 | 8 |  |  |  |  |  | 1girl, ass, black_gloves, black_thighhighs, garter_straps, long_sleeves, military_uniform, solo, black_panties, blush, looking_at_viewer, looking_back, simple_background, white_background, from_behind, blue_skirt, cowboy_shot, blue_jacket, miniskirt, open_mouth |
| 5 | 5 |  |  |  |  |  | 1girl, black_bra, black_panties, black_thighhighs, blush, cleavage, collarbone, navel, solo, underwear_only, looking_at_viewer, simple_background, skindentation, white_background, sitting, garter_belt, lace-trimmed_bra |
| 6 | 5 |  |  |  |  |  | 1girl, big_belly, blush, fat, huge_breasts, solo, thick_thighs, plump, black_gloves, black_thighhighs, thick_arms, open_mouth |
| 7 | 14 |  |  |  |  |  | 1girl, detached_collar, fake_animal_ears, playboy_bunny, rabbit_ears, solo, looking_at_viewer, wrist_cuffs, cleavage, strapless_leotard, blue_leotard, bowtie, white_background, black_pantyhose, black_thighhighs, blush, simple_background, ascot, high_heels, rabbit_tail |
| 8 | 14 |  |  |  |  |  | 1girl, blush, looking_at_viewer, solo, simple_background, white_background, cleavage, navel, blue_bikini, collarbone, gloves, smile |
| 9 | 7 |  |  |  |  |  | 1girl, day, blue_bikini, looking_at_viewer, solo, blue_sky, cloud, ocean, outdoors, navel, beach, cleavage, cowboy_shot |
| 10 | 6 |  |  |  |  |  | smile, 2girls, blonde_hair, looking_at_viewer, navel, adapted_costume, blue_bikini, blush, cleavage, breast_press |
| 11 | 10 |  |  |  |  |  | 1boy, 1girl, hetero, solo_focus, penis, pussy, blush, navel, nipples, mosaic_censoring, open_mouth, sex, girl_on_top, thighhighs, vaginal, cowgirl_position, nude, spread_legs, sweat, black_gloves, erection, looking_at_viewer, open_clothes |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blue_jacket | military_uniform | simple_background | solo | upper_body | white_background | looking_at_viewer | black_gloves | dated | long_sleeves | smile | white_ascot | one-hour_drawing_challenge | twitter_username | black_thighhighs | garter_straps | skirt | cannon | turret | ascot | blue_skirt | miniskirt | open_mouth | blush | cowboy_shot | ass | black_panties | looking_back | from_behind | black_bra | cleavage | collarbone | navel | underwear_only | skindentation | sitting | garter_belt | lace-trimmed_bra | big_belly | fat | huge_breasts | thick_thighs | plump | thick_arms | detached_collar | fake_animal_ears | playboy_bunny | rabbit_ears | wrist_cuffs | strapless_leotard | blue_leotard | bowtie | black_pantyhose | high_heels | rabbit_tail | blue_bikini | gloves | day | blue_sky | cloud | ocean | outdoors | beach | 2girls | blonde_hair | adapted_costume | breast_press | 1boy | hetero | solo_focus | penis | pussy | nipples | mosaic_censoring | sex | girl_on_top | thighhighs | vaginal | cowgirl_position | nude | spread_legs | sweat | erection | open_clothes |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------------|:-------------------|:--------------------|:-------|:-------------|:-------------------|:--------------------|:---------------|:--------|:---------------|:--------|:--------------|:-----------------------------|:-------------------|:-------------------|:----------------|:--------|:---------|:---------|:--------|:-------------|:------------|:-------------|:--------|:--------------|:------|:----------------|:---------------|:--------------|:------------|:-----------|:-------------|:--------|:-----------------|:----------------|:----------|:--------------|:-------------------|:------------|:------|:---------------|:---------------|:--------|:-------------|:------------------|:-------------------|:----------------|:--------------|:--------------|:--------------------|:---------------|:---------|:------------------|:-------------|:--------------|:--------------|:---------|:------|:-----------|:--------|:--------|:-----------|:--------|:---------|:--------------|:------------------|:---------------|:-------|:---------|:-------------|:--------|:--------|:----------|:-------------------|:------|:--------------|:-------------|:----------|:-------------------|:-------|:--------------|:--------|:-----------|:---------------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 9 |  |  |  |  |  | X | | X | | X | | | X | X | | | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | 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 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 8 |  |  |  |  |  | X | X | X | X | X | | X | X | X | | X | | | | | X | X | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | X | X | | X | X | | | | | | | | X | | | | | | | | | X | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | | | | X | | | | X | | | | | | | X | | | | | | | | X | X | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 14 |  |  |  |  |  | X | | | X | X | | X | X | | | | | | | | X | | | | | X | | | | X | | | | | | | X | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 14 |  |  |  |  |  | X | | | X | X | | X | X | | | | X | | | | | | | | | | | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 7 |  |  |  |  |  | X | | | | X | | | X | | | | | | | | | | | | | | | | | | X | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 10 | 6 |  |  |  |  |  | | | | | | | | X | | | | X | | | | | | | | | | | | | X | | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | |
| 11 | 10 |  |  |  |  |  | X | | | | | | | X | X | | | | | | | | | | | | | | | X | X | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
Nexusflow/ClimateAPIBenchmark | ---
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
splits:
- name: train
num_bytes: 11426
num_examples: 47
download_size: 5104
dataset_size: 11426
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-source-metrics/kibana | Invalid username or password. |
CanariaView/GlobalCopperSupplyForecastingDataset | ---
task_categories:
- time-series-forecasting
language:
- en
- ko
tags:
- mining
- LSTM
- TimeSeries
- CanariaView
---
# CanariaView Global Copper Supply Forecasting Dataset
## Description
This dataset encompasses economic and industrial indicators vital for constructing a copper supply forecasting model.
Coverage Period: Monthly data from January 2000 to March 2023, encompassing a total of 279 months.
Column Descriptions and Sources:
- `Copper price`: MacroTrends
- `Cash Costs (Antofagasta's Pure Mining Costs)`: Antofagasta Annual Report
- `Transport (Antofagasta's Transportation Cost)`: Antofagasta Annual Report
- `Stock (LME Copper Stock)`: MacroMicro
- `Oil Price`: Source - EIA
- `M_GDP (Chile Copper Mining GDP)`: Banco Central de Chile
Preprocessing Methodology and Data Collection Details:
- Comprehensive analysis of data structure followed by essential preprocessing.
- Appropriate handling of missing values.
- Daily (e.g., Copper price, Oil Price) and quarterly data (e.g., Cash Costs, Transport, M_GDP) uniformly expanded to a monthly timescale for consistency.
- The Antofagasta annual report was available from the year 2000, hence the data collection started from 2000.
## 한국어 설명
본 데이터셋은 구리 공급 예측 모델 구축을 위한 경제지표 및 산업지표로 구성되었습니다.
기간: 2000년 1월~2023년 3월(월별), 총 279개월.
컬럼 설명 및 출처:
- `Copper price (구리 가격)`: MacroTrends
- `Cash Costs (Antofagasta 순수채굴비용)`: Antofagasta Annual Report
- `Transport (Antofagasta 운송비)`: Antofagasta Annual Report
- `Stock (런던금속거래소 구리 재고량)`: MacroMicro
- `Oil Price (원유 가격)`: EIA
- `M_GDP (칠레 구리 채굴 GDP)`: Banco Central de Chile
데이터 전처리 및 수집 방법:
- 데이터 구조 분석 및 전처리 과정 수행.
- 결측치 처리.
- 일별 자료 (구리 가격, 원유 가격), 분기별 자료 (Cash Costs, Transport, M_GDP)는 월별 데이터로의 확장을 통해 일관된 시계열 데이터로 통합.
- 안토파가스타 관련 데이터가 2000년부터 확보가 가능하여 2000년 부터 수집함. |
andrewatef/newPRO | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 262077583
num_examples: 860295
download_size: 63827792
dataset_size: 262077583
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_Radiantloom__radintloom-mistral-7b-fusion | ---
pretty_name: Evaluation run of Radiantloom/radintloom-mistral-7b-fusion
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Radiantloom/radintloom-mistral-7b-fusion](https://huggingface.co/Radiantloom/radintloom-mistral-7b-fusion)\
\ 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_Radiantloom__radintloom-mistral-7b-fusion\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-19T11:01:46.934466](https://huggingface.co/datasets/open-llm-leaderboard/details_Radiantloom__radintloom-mistral-7b-fusion/blob/main/results_2024-02-19T11-01-46.934466.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\": 0.6290447809893215,\n\
\ \"acc_stderr\": 0.03201542770638297,\n \"acc_norm\": 0.6410101323791937,\n\
\ \"acc_norm_stderr\": 0.032887693370060485,\n \"mc1\": 0.3182374541003672,\n\
\ \"mc1_stderr\": 0.016305988648920616,\n \"mc2\": 0.47189384202061885,\n\
\ \"mc2_stderr\": 0.015093095614046564\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5716723549488054,\n \"acc_stderr\": 0.014460496367599013,\n\
\ \"acc_norm\": 0.6203071672354948,\n \"acc_norm_stderr\": 0.01418211986697487\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6246763592909779,\n\
\ \"acc_stderr\": 0.004832167854501644,\n \"acc_norm\": 0.8226448914558853,\n\
\ \"acc_norm_stderr\": 0.00381188307091126\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411022,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411022\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5777777777777777,\n\
\ \"acc_stderr\": 0.04266763404099582,\n \"acc_norm\": 0.5777777777777777,\n\
\ \"acc_norm_stderr\": 0.04266763404099582\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6776315789473685,\n \"acc_stderr\": 0.038035102483515854,\n\
\ \"acc_norm\": 0.6776315789473685,\n \"acc_norm_stderr\": 0.038035102483515854\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\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708052,\n\
\ \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708052\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7291666666666666,\n\
\ \"acc_stderr\": 0.03716177437566017,\n \"acc_norm\": 0.7291666666666666,\n\
\ \"acc_norm_stderr\": 0.03716177437566017\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.48,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.05021167315686779,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6358381502890174,\n\
\ \"acc_stderr\": 0.03669072477416907,\n \"acc_norm\": 0.6358381502890174,\n\
\ \"acc_norm_stderr\": 0.03669072477416907\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3333333333333333,\n \"acc_stderr\": 0.04690650298201942,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04690650298201942\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.78,\n \"acc_stderr\": 0.04163331998932261,\n \"acc_norm\": 0.78,\n\
\ \"acc_norm_stderr\": 0.04163331998932261\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5957446808510638,\n \"acc_stderr\": 0.032081157507886836,\n\
\ \"acc_norm\": 0.5957446808510638,\n \"acc_norm_stderr\": 0.032081157507886836\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\": 0.5172413793103449,\n \"acc_stderr\": 0.04164188720169375,\n\
\ \"acc_norm\": 0.5172413793103449,\n \"acc_norm_stderr\": 0.04164188720169375\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4523809523809524,\n \"acc_stderr\": 0.02563425811555496,\n \"\
acc_norm\": 0.4523809523809524,\n \"acc_norm_stderr\": 0.02563425811555496\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.47619047619047616,\n\
\ \"acc_stderr\": 0.04467062628403273,\n \"acc_norm\": 0.47619047619047616,\n\
\ \"acc_norm_stderr\": 0.04467062628403273\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-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\"\
: 0.5073891625615764,\n \"acc_stderr\": 0.035176035403610105,\n \"\
acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.035176035403610105\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\"\
: 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7636363636363637,\n \"acc_stderr\": 0.03317505930009182,\n\
\ \"acc_norm\": 0.7636363636363637,\n \"acc_norm_stderr\": 0.03317505930009182\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.028869778460267042,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.028869778460267042\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.021500249576033456,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.021500249576033456\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6461538461538462,\n \"acc_stderr\": 0.024243783994062157,\n\
\ \"acc_norm\": 0.6461538461538462,\n \"acc_norm_stderr\": 0.024243783994062157\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251976,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251976\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6848739495798319,\n \"acc_stderr\": 0.030176808288974337,\n\
\ \"acc_norm\": 0.6848739495798319,\n \"acc_norm_stderr\": 0.030176808288974337\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658752,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658752\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8366972477064221,\n \"acc_stderr\": 0.015848255806501562,\n \"\
acc_norm\": 0.8366972477064221,\n \"acc_norm_stderr\": 0.015848255806501562\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5370370370370371,\n \"acc_stderr\": 0.03400603625538271,\n \"\
acc_norm\": 0.5370370370370371,\n \"acc_norm_stderr\": 0.03400603625538271\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8333333333333334,\n \"acc_stderr\": 0.026156867523931045,\n \"\
acc_norm\": 0.8333333333333334,\n \"acc_norm_stderr\": 0.026156867523931045\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7805907172995781,\n \"acc_stderr\": 0.026939106581553945,\n \
\ \"acc_norm\": 0.7805907172995781,\n \"acc_norm_stderr\": 0.026939106581553945\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7040358744394619,\n\
\ \"acc_stderr\": 0.03063659134869981,\n \"acc_norm\": 0.7040358744394619,\n\
\ \"acc_norm_stderr\": 0.03063659134869981\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8015267175572519,\n \"acc_stderr\": 0.03498149385462472,\n\
\ \"acc_norm\": 0.8015267175572519,\n \"acc_norm_stderr\": 0.03498149385462472\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8099173553719008,\n \"acc_stderr\": 0.03581796951709282,\n \"\
acc_norm\": 0.8099173553719008,\n \"acc_norm_stderr\": 0.03581796951709282\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7685185185185185,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.7685185185185185,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7239263803680982,\n \"acc_stderr\": 0.03512385283705048,\n\
\ \"acc_norm\": 0.7239263803680982,\n \"acc_norm_stderr\": 0.03512385283705048\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8760683760683761,\n\
\ \"acc_stderr\": 0.021586494001281376,\n \"acc_norm\": 0.8760683760683761,\n\
\ \"acc_norm_stderr\": 0.021586494001281376\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.68,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.68,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993445,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993445\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7254335260115607,\n \"acc_stderr\": 0.02402774515526502,\n\
\ \"acc_norm\": 0.7254335260115607,\n \"acc_norm_stderr\": 0.02402774515526502\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.264804469273743,\n\
\ \"acc_stderr\": 0.014756906483260664,\n \"acc_norm\": 0.264804469273743,\n\
\ \"acc_norm_stderr\": 0.014756906483260664\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7287581699346405,\n \"acc_stderr\": 0.025457756696667874,\n\
\ \"acc_norm\": 0.7287581699346405,\n \"acc_norm_stderr\": 0.025457756696667874\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7202572347266881,\n\
\ \"acc_stderr\": 0.025494259350694912,\n \"acc_norm\": 0.7202572347266881,\n\
\ \"acc_norm_stderr\": 0.025494259350694912\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7160493827160493,\n \"acc_stderr\": 0.025089478523765134,\n\
\ \"acc_norm\": 0.7160493827160493,\n \"acc_norm_stderr\": 0.025089478523765134\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666904,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666904\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47979139504563234,\n\
\ \"acc_stderr\": 0.012759801427767562,\n \"acc_norm\": 0.47979139504563234,\n\
\ \"acc_norm_stderr\": 0.012759801427767562\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\
\ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6568627450980392,\n \"acc_stderr\": 0.01920660684882536,\n \
\ \"acc_norm\": 0.6568627450980392,\n \"acc_norm_stderr\": 0.01920660684882536\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7306122448979592,\n \"acc_stderr\": 0.02840125202902294,\n\
\ \"acc_norm\": 0.7306122448979592,\n \"acc_norm_stderr\": 0.02840125202902294\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.85,\n \"acc_stderr\": 0.03588702812826371,\n \
\ \"acc_norm\": 0.85,\n \"acc_norm_stderr\": 0.03588702812826371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5180722891566265,\n\
\ \"acc_stderr\": 0.03889951252827216,\n \"acc_norm\": 0.5180722891566265,\n\
\ \"acc_norm_stderr\": 0.03889951252827216\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8128654970760234,\n \"acc_stderr\": 0.02991312723236804,\n\
\ \"acc_norm\": 0.8128654970760234,\n \"acc_norm_stderr\": 0.02991312723236804\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3182374541003672,\n\
\ \"mc1_stderr\": 0.016305988648920616,\n \"mc2\": 0.47189384202061885,\n\
\ \"mc2_stderr\": 0.015093095614046564\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7987371744277821,\n \"acc_stderr\": 0.01126851997157768\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/Radiantloom/radintloom-mistral-7b-fusion
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|arc:challenge|25_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|gsm8k|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hellaswag|10_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-19T11-01-46.934466.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-19T11-01-46.934466.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- '**/details_harness|winogrande|5_2024-02-19T11-01-46.934466.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-19T11-01-46.934466.parquet'
- config_name: results
data_files:
- split: 2024_02_19T11_01_46.934466
path:
- results_2024-02-19T11-01-46.934466.parquet
- split: latest
path:
- results_2024-02-19T11-01-46.934466.parquet
---
# Dataset Card for Evaluation run of Radiantloom/radintloom-mistral-7b-fusion
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Radiantloom/radintloom-mistral-7b-fusion](https://huggingface.co/Radiantloom/radintloom-mistral-7b-fusion) 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_Radiantloom__radintloom-mistral-7b-fusion",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-19T11:01:46.934466](https://huggingface.co/datasets/open-llm-leaderboard/details_Radiantloom__radintloom-mistral-7b-fusion/blob/main/results_2024-02-19T11-01-46.934466.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": 0.6290447809893215,
"acc_stderr": 0.03201542770638297,
"acc_norm": 0.6410101323791937,
"acc_norm_stderr": 0.032887693370060485,
"mc1": 0.3182374541003672,
"mc1_stderr": 0.016305988648920616,
"mc2": 0.47189384202061885,
"mc2_stderr": 0.015093095614046564
},
"harness|arc:challenge|25": {
"acc": 0.5716723549488054,
"acc_stderr": 0.014460496367599013,
"acc_norm": 0.6203071672354948,
"acc_norm_stderr": 0.01418211986697487
},
"harness|hellaswag|10": {
"acc": 0.6246763592909779,
"acc_stderr": 0.004832167854501644,
"acc_norm": 0.8226448914558853,
"acc_norm_stderr": 0.00381188307091126
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.35,
"acc_stderr": 0.04793724854411022,
"acc_norm": 0.35,
"acc_norm_stderr": 0.04793724854411022
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5777777777777777,
"acc_stderr": 0.04266763404099582,
"acc_norm": 0.5777777777777777,
"acc_norm_stderr": 0.04266763404099582
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6776315789473685,
"acc_stderr": 0.038035102483515854,
"acc_norm": 0.6776315789473685,
"acc_norm_stderr": 0.038035102483515854
},
"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": 0.7396226415094339,
"acc_stderr": 0.027008766090708052,
"acc_norm": 0.7396226415094339,
"acc_norm_stderr": 0.027008766090708052
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7291666666666666,
"acc_stderr": 0.03716177437566017,
"acc_norm": 0.7291666666666666,
"acc_norm_stderr": 0.03716177437566017
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.05021167315686779,
"acc_norm": 0.52,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6358381502890174,
"acc_stderr": 0.03669072477416907,
"acc_norm": 0.6358381502890174,
"acc_norm_stderr": 0.03669072477416907
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.04690650298201942,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.04690650298201942
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.78,
"acc_stderr": 0.04163331998932261,
"acc_norm": 0.78,
"acc_norm_stderr": 0.04163331998932261
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5957446808510638,
"acc_stderr": 0.032081157507886836,
"acc_norm": 0.5957446808510638,
"acc_norm_stderr": 0.032081157507886836
},
"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": 0.5172413793103449,
"acc_stderr": 0.04164188720169375,
"acc_norm": 0.5172413793103449,
"acc_norm_stderr": 0.04164188720169375
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4523809523809524,
"acc_stderr": 0.02563425811555496,
"acc_norm": 0.4523809523809524,
"acc_norm_stderr": 0.02563425811555496
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.47619047619047616,
"acc_stderr": 0.04467062628403273,
"acc_norm": 0.47619047619047616,
"acc_norm_stderr": 0.04467062628403273
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"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": 0.5073891625615764,
"acc_stderr": 0.035176035403610105,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.035176035403610105
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7636363636363637,
"acc_stderr": 0.03317505930009182,
"acc_norm": 0.7636363636363637,
"acc_norm_stderr": 0.03317505930009182
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.028869778460267042,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.028869778460267042
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
"acc_stderr": 0.021500249576033456,
"acc_norm": 0.9015544041450777,
"acc_norm_stderr": 0.021500249576033456
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6461538461538462,
"acc_stderr": 0.024243783994062157,
"acc_norm": 0.6461538461538462,
"acc_norm_stderr": 0.024243783994062157
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.3592592592592593,
"acc_stderr": 0.029252905927251976,
"acc_norm": 0.3592592592592593,
"acc_norm_stderr": 0.029252905927251976
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6848739495798319,
"acc_stderr": 0.030176808288974337,
"acc_norm": 0.6848739495798319,
"acc_norm_stderr": 0.030176808288974337
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.32450331125827814,
"acc_stderr": 0.03822746937658752,
"acc_norm": 0.32450331125827814,
"acc_norm_stderr": 0.03822746937658752
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8366972477064221,
"acc_stderr": 0.015848255806501562,
"acc_norm": 0.8366972477064221,
"acc_norm_stderr": 0.015848255806501562
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5370370370370371,
"acc_stderr": 0.03400603625538271,
"acc_norm": 0.5370370370370371,
"acc_norm_stderr": 0.03400603625538271
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8333333333333334,
"acc_stderr": 0.026156867523931045,
"acc_norm": 0.8333333333333334,
"acc_norm_stderr": 0.026156867523931045
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7805907172995781,
"acc_stderr": 0.026939106581553945,
"acc_norm": 0.7805907172995781,
"acc_norm_stderr": 0.026939106581553945
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7040358744394619,
"acc_stderr": 0.03063659134869981,
"acc_norm": 0.7040358744394619,
"acc_norm_stderr": 0.03063659134869981
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8015267175572519,
"acc_stderr": 0.03498149385462472,
"acc_norm": 0.8015267175572519,
"acc_norm_stderr": 0.03498149385462472
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8099173553719008,
"acc_stderr": 0.03581796951709282,
"acc_norm": 0.8099173553719008,
"acc_norm_stderr": 0.03581796951709282
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7685185185185185,
"acc_stderr": 0.04077494709252626,
"acc_norm": 0.7685185185185185,
"acc_norm_stderr": 0.04077494709252626
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7239263803680982,
"acc_stderr": 0.03512385283705048,
"acc_norm": 0.7239263803680982,
"acc_norm_stderr": 0.03512385283705048
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.03916667762822584,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8760683760683761,
"acc_stderr": 0.021586494001281376,
"acc_norm": 0.8760683760683761,
"acc_norm_stderr": 0.021586494001281376
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993445,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993445
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7254335260115607,
"acc_stderr": 0.02402774515526502,
"acc_norm": 0.7254335260115607,
"acc_norm_stderr": 0.02402774515526502
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.264804469273743,
"acc_stderr": 0.014756906483260664,
"acc_norm": 0.264804469273743,
"acc_norm_stderr": 0.014756906483260664
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7287581699346405,
"acc_stderr": 0.025457756696667874,
"acc_norm": 0.7287581699346405,
"acc_norm_stderr": 0.025457756696667874
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7202572347266881,
"acc_stderr": 0.025494259350694912,
"acc_norm": 0.7202572347266881,
"acc_norm_stderr": 0.025494259350694912
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7160493827160493,
"acc_stderr": 0.025089478523765134,
"acc_norm": 0.7160493827160493,
"acc_norm_stderr": 0.025089478523765134
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.029658235097666904,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.029658235097666904
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47979139504563234,
"acc_stderr": 0.012759801427767562,
"acc_norm": 0.47979139504563234,
"acc_norm_stderr": 0.012759801427767562
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6397058823529411,
"acc_stderr": 0.029163128570670733,
"acc_norm": 0.6397058823529411,
"acc_norm_stderr": 0.029163128570670733
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6568627450980392,
"acc_stderr": 0.01920660684882536,
"acc_norm": 0.6568627450980392,
"acc_norm_stderr": 0.01920660684882536
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6363636363636364,
"acc_stderr": 0.04607582090719976,
"acc_norm": 0.6363636363636364,
"acc_norm_stderr": 0.04607582090719976
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7306122448979592,
"acc_stderr": 0.02840125202902294,
"acc_norm": 0.7306122448979592,
"acc_norm_stderr": 0.02840125202902294
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.02650859065623327,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.02650859065623327
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.85,
"acc_stderr": 0.03588702812826371,
"acc_norm": 0.85,
"acc_norm_stderr": 0.03588702812826371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5180722891566265,
"acc_stderr": 0.03889951252827216,
"acc_norm": 0.5180722891566265,
"acc_norm_stderr": 0.03889951252827216
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8128654970760234,
"acc_stderr": 0.02991312723236804,
"acc_norm": 0.8128654970760234,
"acc_norm_stderr": 0.02991312723236804
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3182374541003672,
"mc1_stderr": 0.016305988648920616,
"mc2": 0.47189384202061885,
"mc2_stderr": 0.015093095614046564
},
"harness|winogrande|5": {
"acc": 0.7987371744277821,
"acc_stderr": 0.01126851997157768
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
AISE-TUDelft/ML4SE23_G1_HumanEval-SCoT | ---
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: HumanEval dataset enhanced with Structured-Chain-of-Thought
size_categories:
- n<1K
---
# ML4SE23_G1_HumanEval-SCoT
HumanEval dataset enhanced with Structured-Chain-of-Thought |
audioshake/jam-alt | ---
task_categories:
- automatic-speech-recognition
multilinguality:
- multilingual
language:
- en
- fr
- de
- es
tags:
- music
- lyrics
- evaluation
- benchmark
- transcription
pretty_name: 'JamALT: A Formatting-Aware Lyrics Transcription Benchmark'
paperswithcode_id: jam-alt
---
# JamALT: A Formatting-Aware Lyrics Transcription Benchmark
## Dataset description
* **Project page:** https://audioshake.github.io/jam-alt/
* **Source code:** https://github.com/audioshake/alt-eval
* **Paper:** https://arxiv.org/abs/2311.13987
JamALT is a revision of the [JamendoLyrics](https://github.com/f90/jamendolyrics) dataset (80 songs in 4 languages), adapted for use as an automatic lyrics transcription (ALT) benchmark.
The lyrics have been revised according to the newly compiled [annotation guidelines](GUIDELINES.md), which include rules about spelling, punctuation, and formatting.
The audio is identical to the JamendoLyrics dataset.
However, only 79 songs are included, as one of the 20 French songs (`La_Fin_des_Temps_-_BuzzBonBon`) has been removed due to concerns about potentially harmful content.
**Note:** The dataset is not time-aligned as it does not easily map to the timestamps from JamendoLyrics. To evaluate automatic lyrics alignment (ALA), please use JamendoLyrics directly.
See the [project website](https://audioshake.github.io/jam-alt/) for details.
## Loading the data
```python
from datasets import load_dataset
dataset = load_dataset("audioshake/jam-alt")["test"]
```
A subset is defined for each language (`en`, `fr`, `de`, `es`);
for example, use `load_dataset("audioshake/jam-alt", "es")` to load only the Spanish songs.
By default, the dataset comes with audio. To skip loading the audio, use `with_audio=False`.
To control how the audio is decoded, cast the `audio` column using `dataset.cast_column("audio", datasets.Audio(...))`.
Useful arguments to `datasets.Audio()` are:
- `sampling_rate` and `mono=True` to control the sampling rate and number of channels.
- `decode=False` to skip decoding the audio and just get the MP3 file paths.
## Running the benchmark
The evaluation is implemented in our [`alt-eval` package](https://github.com/audioshake/alt-eval):
```python
from datasets import load_dataset
from alt_eval import compute_metrics
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
# transcriptions: list[str]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
```
For example, the following code can be used to evaluate Whisper:
```python
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0")["test"]
dataset = dataset.cast_column("audio", datasets.Audio(decode=False)) # Get the raw audio file, let Whisper decode it
model = whisper.load_model("tiny")
transcriptions = [
"\n".join(s["text"].strip() for s in model.transcribe(a["path"])["segments"])
for a in dataset["audio"]
]
compute_metrics(dataset["text"], transcriptions, languages=dataset["language"])
```
Alternatively, if you already have transcriptions, you might prefer to skip loading the audio:
```python
dataset = load_dataset("audioshake/jam-alt", revision="v1.0.0", with_audio=False)["test"]
```
## Citation
When using the benchmark, please cite [our paper](https://arxiv.org/abs/2311.13987) as well as the original [JamendoLyrics paper](https://arxiv.org/abs/2306.07744):
```bibtex
@misc{cifka-2023-jam-alt,
author = {Ond\v{r}ej C\'ifka and
Constantinos Dimitriou and
{Cheng-i} Wang and
Hendrik Schreiber and
Luke Miner and
Fabian-Robert St\"oter},
title = {{Jam-ALT}: A Formatting-Aware Lyrics Transcription Benchmark},
eprint = {arXiv:2311.13987},
year = 2023
}
@inproceedings{durand-2023-contrastive,
author={Durand, Simon and Stoller, Daniel and Ewert, Sebastian},
booktitle={2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages},
year={2023},
pages={1-5},
address={Rhodes Island, Greece},
doi={10.1109/ICASSP49357.2023.10096725}
}
``` |
Adun/isuzu-ds-test | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 3104769.0
num_examples: 14
download_size: 3050513
dataset_size: 3104769.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lara-martin/Scifi_TV_Shows | ---
license: cc-by-4.0
task_categories:
- text-generation
- text2text-generation
language:
- en
tags:
- story
- storytelling
- creative
- summaries
- TV
- scifi
pretty_name: Scifi TV Shows
size_categories:
- 100K<n<1M
---
# Dataset Card for Science Fiction TV Show Plots Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Format](#format)
- [Using the Dataset with Hugging Face](#call-scifi)
- [Original Dataset Structure](#dataset-structure)
- [Files in _OriginalStoriesSeparated_ Directory](#original-stories)
- [Additional Information](#additional-information)
- [Citation](#citation)
- [Licensing](#licensing)
## Dataset Description
A collection of long-running (80+ episodes) science fiction TV show plot synopses, scraped from Fandom.com wikis. Collected Nov 2017. Each episode is considered a "story".
Contains plot summaries from:
- Babylon 5 (https://babylon5.fandom.com/wiki/Main_Page) - 84 stories
- Doctor Who (https://tardis.fandom.com/wiki/Doctor_Who_Wiki) - 311 stories
- Doctor Who spin-offs - 95 stories
- Farscape (https://farscape.fandom.com/wiki/Farscape_Encyclopedia_Project:Main_Page) - 90 stories
- Fringe (https://fringe.fandom.com/wiki/FringeWiki) - 87 stories
- Futurama (https://futurama.fandom.com/wiki/Futurama_Wiki) - 87 stories
- Stargate (https://stargate.fandom.com/wiki/Stargate_Wiki) - 351 stories
- Star Trek (https://memory-alpha.fandom.com/wiki/Star_Trek) - 701 stories
- Star Wars books (https://starwars.fandom.com/wiki/Main_Page) - 205 stories, each book is a story
- Star Wars Rebels (https://starwarsrebels.fandom.com/wiki/Main_page) - 65 stories
- X-Files (https://x-files.fandom.com/wiki/Main_Page) - 200 stories
Total: 2276 stories
Dataset is "eventified" and generalized (see LJ Martin, P Ammanabrolu, X Wang, W Hancock, S Singh, B Harrison, and MO Riedl. Event Representations for Automated Story Generation with Deep Neural Nets, Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 2018. for details on these processes.) and split into train-test-validation sets—separated by story so that full stories will stay together—for converting events into full sentences.
---
### Format
| Dataset Split | Number of Stories in Split | Number of Sentences in Split |
| ------------- |--------------------------- |----------------------------- |
| Train | 1737 | 257,108 |
| Validation | 194 | 32,855 |
| Test | 450 | 30,938 |
#### Using the Dataset with Hugging Face
```
from datasets import load_dataset
#download and load the data
dataset = load_dataset('lara-martin/Scifi_TV_Shows')
#you can then get the individual splits
train = dataset['train']
test = dataset['test']
validation = dataset['validation']
```
Each split has 7 attributes (explained in more detail in the next section):
```
>>> print(train)
Dataset({
features: ['story_num', 'story_line', 'event', 'gen_event', 'sent', 'gen_sent', 'entities'],
num_rows: 257108
})
```
---
## Original Dataset Structure
* File names: scifi-val.txt, scifi-test.txt, & scifi-train.txt
* Each sentence of the stories are split into smaller sentences and the events are extracted.
* Each line of the file contains information about a single sentence, delimited by "|||". Each line contains, in order:
* The story number
* The line number (within the story)
* 5-tuple events in a list (subject, verb, direct object, modifier noun, preposition); e.g.,
``
[[u'Voyager', u'run', 'EmptyParameter', u'deuterium', u'out'], [u'Voyager', u'force', u'go', 'EmptyParameter', 'EmptyParameter'], [u'Voyager', u'go', 'EmptyParameter', u'mode', u'into']]
``
* generalized 5-tuple events in a list; events are generalized using WordNet and VerbNet; e.g.,
``
[['<VESSEL>0', 'function-105.2.1', 'EmptyParameter', "Synset('atom.n.01')", u'out'], ['<VESSEL>0', 'urge-58.1-1', u'escape-51.1-1', 'EmptyParameter', 'EmptyParameter'], ['<VESSEL>0', u'escape-51.1-1', 'EmptyParameter', "Synset('statistic.n.01')", u'into']]
``
* original sentence (These sentences are split to contain fewer events per sentence. For the full original sentence, see the OriginalStoriesSeparated directory.); e.g.,
``
The USS Voyager is running out of deuterium as a fuel and is forced to go into Gray mode.
``
* generalized sentence; only nouns are generalized (using WordNet); e.g.,
``
the <VESSEL>0 is running out of Synset('atom.n.01') as a Synset('matter.n.03') and is forced to go into Synset('horse.n.01') Synset('statistic.n.01').
``
* a dictionary of numbered entities by tag within the _entire story_ (e.g. the second entity in the "<ORGANIZATION>" list in the dictionary would be <ORGANIZATION>1 in the story above—index starts at 0); e.g.,
``
{'<ORGANIZATION>': ['seven of nine', 'silver blood'], '<LOCATION>': ['sickbay', 'astrometrics', 'paris', 'cavern', 'vorik', 'caves'], '<DATE>': ['an hour ago', 'now'], '<MISC>': ['selected works', 'demon class', 'electromagnetic', 'parises', 'mimetic'], '<DURATION>': ['less than a week', 'the past four years', 'thirty seconds', 'an hour', 'two hours'], '<NUMBER>': ['two', 'dozen', '14', '15'], '<ORDINAL>': ['first'], '<PERSON>': ['tom paris', 'harry kim', 'captain kathryn janeway', 'tuvok', 'chakotay', 'jirex', 'neelix', 'the doctor', 'seven', 'ensign kashimuro nozawa', 'green', 'lt jg elanna torres', 'ensign vorik'], '<VESSEL>': ['uss voyager', 'starfleet']}
``
### Files in _OriginalStoriesSeparated_ Directory
* Contains unedited, unparsed original stories scraped from the respective Fandom wikis.
* Each line is a story with sentences space-separated. After each story, there is a <EOS> tag on a new line.
* There is one file for each of the 11 domains listed above.
* These are currently not set up to be called through the Hugging Face API and must be extracted from the zip directly.
---
## Additional Information
### Citation
```
@inproceedings{Ammanabrolu2020AAAI,
title={Story Realization: Expanding Plot Events into Sentences},
author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl},
journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020},
volume={34},
number={05},
url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
}
```
---
### Licensing
The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/ |
zefang-liu/phishing-email-dataset | ---
license: lgpl-3.0
language:
- en
task_categories:
- text-classification
size_categories:
- 10K<n<100K
---
# Phishing Email Dataset
This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the [GNU Lesser General Public License 3.0](https://www.gnu.org/licenses/lgpl-3.0.html). The dataset was originally created by the user '[Cyber Cop](https://www.kaggle.com/subhajournal)' on Kaggle. For complete details, including licensing and usage information, please visit the [original Kaggle page](https://www.kaggle.com/datasets/subhajournal/phishingemails).
|
firiyuu77/apeiron-llama2-1k | ---
dataset_info:
features:
- name: text
dtype: string
- name: concept
dtype: string
splits:
- name: train
num_bytes: 211437
num_examples: 1000
download_size: 70724
dataset_size: 211437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "apeiron-llama2-1k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
alvarobartt/evol-instruct-sample | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 5022
num_examples: 14
download_size: 5388
dataset_size: 5022
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
FastFit/massive_fr_60 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 747069
num_examples: 11514
- name: validation
num_bytes: 130664
num_examples: 2033
- name: test
num_bytes: 191252
num_examples: 2974
download_size: 443437
dataset_size: 1068985
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
oneyedjack/cegid_test | ---
license: apache-2.0
---
|
July24/P_Data_0_1 | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 342012
num_examples: 1930
- name: test
num_bytes: 92578
num_examples: 463
download_size: 253013
dataset_size: 434590
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
bigbio/hprd50 |
---
language:
- en
bigbio_language:
- English
license: unknown
multilinguality: monolingual
bigbio_license_shortname: UNKNOWN
pretty_name: HPRD50
homepage:
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- RELATION_EXTRACTION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for HPRD50
## Dataset Description
- **Homepage:**
- **Pubmed:** True
- **Public:** True
- **Tasks:** RE,NER
HPRD50 is a dataset of randomly selected, hand-annotated abstracts of biomedical papers
referenced by the Human Protein Reference Database (HPRD). It is parsed in XML format,
splitting each abstract into sentences, and in each sentence there may be entities and
interactions between those entities. In this particular dataset, entities are all
proteins and interactions are thus protein-protein interactions.
Moreover, all entities are normalized to the HPRD database. These normalized terms are
stored in each entity's 'type' attribute in the source XML. This means the dataset can
determine e.g. that "Janus kinase 2" and "Jak2" are referencing the same normalized
entity.
Because the dataset contains entities and relations, it is suitable for Named Entity
Recognition and Relation Extraction.
## Citation Information
```
@article{fundel2007relex,
title={RelEx—Relation extraction using dependency parse trees},
author={Fundel, Katrin and K{"u}ffner, Robert and Zimmer, Ralf},
journal={Bioinformatics},
volume={23},
number={3},
pages={365--371},
year={2007},
publisher={Oxford University Press}
}
```
|
open-llm-leaderboard/details_bigscience__bloomz-7b1 | ---
pretty_name: Evaluation run of bigscience/bloomz-7b1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [bigscience/bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1) 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 10 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_bigscience__bloomz-7b1\"\
,\n\t\"harness_gsm8k_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese\
\ are the [latest results from run 2023-12-04T12:56:49.944014](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-7b1/blob/main/results_2023-12-04T12-56-49.944014.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\": 0.000758150113722517,\n\
\ \"acc_stderr\": 0.0007581501137225419\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.000758150113722517,\n \"acc_stderr\": 0.0007581501137225419\n\
\ }\n}\n```"
repo_url: https://huggingface.co/bigscience/bloomz-7b1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|arc:challenge|25_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|arc:challenge|25_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_22T17_52_30.288263
path:
- '**/details_harness|drop|3_2023-09-22T17-52-30.288263.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-22T17-52-30.288263.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_22T17_52_30.288263
path:
- '**/details_harness|gsm8k|5_2023-09-22T17-52-30.288263.parquet'
- split: 2023_12_03T14_53_17.113107
path:
- '**/details_harness|gsm8k|5_2023-12-03T14-53-17.113107.parquet'
- split: 2023_12_03T15_55_50.672449
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-55-50.672449.parquet'
- split: 2023_12_03T15_56_16.405841
path:
- '**/details_harness|gsm8k|5_2023-12-03T15-56-16.405841.parquet'
- split: 2023_12_04T09_46_15.159375
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-46-15.159375.parquet'
- split: 2023_12_04T09_46_26.874047
path:
- '**/details_harness|gsm8k|5_2023-12-04T09-46-26.874047.parquet'
- split: 2023_12_04T12_56_20.274289
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-56-20.274289.parquet'
- split: 2023_12_04T12_56_49.944014
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-56-49.944014.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-12-04T12-56-49.944014.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hellaswag|10_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hellaswag|10_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T10:10:08.875186.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T11:29:59.333088.parquet'
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- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T11:29:59.333088.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_anatomy_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_astronomy_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_business_ethics_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_college_biology_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_computer_security_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_formal_logic_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_global_facts_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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path:
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data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
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path:
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path:
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- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_22T10_10_08.875186
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T10:10:08.875186.parquet'
- split: 2023_08_22T11_29_59.333088
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T11:29:59.333088.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-22T11:29:59.333088.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_22T17_52_30.288263
path:
- '**/details_harness|winogrande|5_2023-09-22T17-52-30.288263.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-22T17-52-30.288263.parquet'
- config_name: results
data_files:
- split: 2023_09_22T17_52_30.288263
path:
- results_2023-09-22T17-52-30.288263.parquet
- split: 2023_12_03T14_53_17.113107
path:
- results_2023-12-03T14-53-17.113107.parquet
- split: 2023_12_03T15_55_50.672449
path:
- results_2023-12-03T15-55-50.672449.parquet
- split: 2023_12_03T15_56_16.405841
path:
- results_2023-12-03T15-56-16.405841.parquet
- split: 2023_12_04T09_46_15.159375
path:
- results_2023-12-04T09-46-15.159375.parquet
- split: 2023_12_04T09_46_26.874047
path:
- results_2023-12-04T09-46-26.874047.parquet
- split: 2023_12_04T12_56_20.274289
path:
- results_2023-12-04T12-56-20.274289.parquet
- split: 2023_12_04T12_56_49.944014
path:
- results_2023-12-04T12-56-49.944014.parquet
- split: latest
path:
- results_2023-12-04T12-56-49.944014.parquet
---
# Dataset Card for Evaluation run of bigscience/bloomz-7b1
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/bigscience/bloomz-7b1
- **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 [bigscience/bloomz-7b1](https://huggingface.co/bigscience/bloomz-7b1) 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 10 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_bigscience__bloomz-7b1",
"harness_gsm8k_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-12-04T12:56:49.944014](https://huggingface.co/datasets/open-llm-leaderboard/details_bigscience__bloomz-7b1/blob/main/results_2023-12-04T12-56-49.944014.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": 0.000758150113722517,
"acc_stderr": 0.0007581501137225419
},
"harness|gsm8k|5": {
"acc": 0.000758150113722517,
"acc_stderr": 0.0007581501137225419
}
}
```
### 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] |
TinyPixel/dolphin-1 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3444101134
num_examples: 2000000
download_size: 1869639144
dataset_size: 3444101134
---
# Dataset Card for "dolphin-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
irds/lotte_writing_dev_search | ---
pretty_name: '`lotte/writing/dev/search`'
viewer: false
source_datasets: ['irds/lotte_writing_dev']
task_categories:
- text-retrieval
---
# Dataset Card for `lotte/writing/dev/search`
The `lotte/writing/dev/search` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/lotte#lotte/writing/dev/search).
# Data
This dataset provides:
- `queries` (i.e., topics); count=497
- `qrels`: (relevance assessments); count=1,287
- For `docs`, use [`irds/lotte_writing_dev`](https://huggingface.co/datasets/irds/lotte_writing_dev)
## Usage
```python
from datasets import load_dataset
queries = load_dataset('irds/lotte_writing_dev_search', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/lotte_writing_dev_search', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@article{Santhanam2021ColBERTv2,
title = "ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction",
author = "Keshav Santhanam and Omar Khattab and Jon Saad-Falcon and Christopher Potts and Matei Zaharia",
journal= "arXiv preprint arXiv:2112.01488",
year = "2021",
url = "https://arxiv.org/abs/2112.01488"
}
```
|
Mousaicv/gpt4_reward_train | ---
license: apache-2.0
---
|
irds/wikiclir_uk | ---
pretty_name: '`wikiclir/uk`'
viewer: false
source_datasets: []
task_categories:
- text-retrieval
---
# Dataset Card for `wikiclir/uk`
The `wikiclir/uk` dataset, provided by the [ir-datasets](https://ir-datasets.com/) package.
For more information about the dataset, see the [documentation](https://ir-datasets.com/wikiclir#wikiclir/uk).
# Data
This dataset provides:
- `docs` (documents, i.e., the corpus); count=704,903
- `queries` (i.e., topics); count=348,222
- `qrels`: (relevance assessments); count=913,358
## Usage
```python
from datasets import load_dataset
docs = load_dataset('irds/wikiclir_uk', 'docs')
for record in docs:
record # {'doc_id': ..., 'title': ..., 'text': ...}
queries = load_dataset('irds/wikiclir_uk', 'queries')
for record in queries:
record # {'query_id': ..., 'text': ...}
qrels = load_dataset('irds/wikiclir_uk', 'qrels')
for record in qrels:
record # {'query_id': ..., 'doc_id': ..., 'relevance': ..., 'iteration': ...}
```
Note that calling `load_dataset` will download the dataset (or provide access instructions when it's not public) and make a copy of the
data in 🤗 Dataset format.
## Citation Information
```
@inproceedings{sasaki-etal-2018-cross,
title = "Cross-Lingual Learning-to-Rank with Shared Representations",
author = "Sasaki, Shota and
Sun, Shuo and
Schamoni, Shigehiko and
Duh, Kevin and
Inui, Kentaro",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2073",
doi = "10.18653/v1/N18-2073",
pages = "458--463"
}
```
|
dvilasuero/banking_with_vectors | ---
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': activate_my_card
'1': age_limit
'2': apple_pay_or_google_pay
'3': atm_support
'4': automatic_top_up
'5': balance_not_updated_after_bank_transfer
'6': balance_not_updated_after_cheque_or_cash_deposit
'7': beneficiary_not_allowed
'8': cancel_transfer
'9': card_about_to_expire
'10': card_acceptance
'11': card_arrival
'12': card_delivery_estimate
'13': card_linking
'14': card_not_working
'15': card_payment_fee_charged
'16': card_payment_not_recognised
'17': card_payment_wrong_exchange_rate
'18': card_swallowed
'19': cash_withdrawal_charge
'20': cash_withdrawal_not_recognised
'21': change_pin
'22': compromised_card
'23': contactless_not_working
'24': country_support
'25': declined_card_payment
'26': declined_cash_withdrawal
'27': declined_transfer
'28': direct_debit_payment_not_recognised
'29': disposable_card_limits
'30': edit_personal_details
'31': exchange_charge
'32': exchange_rate
'33': exchange_via_app
'34': extra_charge_on_statement
'35': failed_transfer
'36': fiat_currency_support
'37': get_disposable_virtual_card
'38': get_physical_card
'39': getting_spare_card
'40': getting_virtual_card
'41': lost_or_stolen_card
'42': lost_or_stolen_phone
'43': order_physical_card
'44': passcode_forgotten
'45': pending_card_payment
'46': pending_cash_withdrawal
'47': pending_top_up
'48': pending_transfer
'49': pin_blocked
'50': receiving_money
'51': Refund_not_showing_up
'52': request_refund
'53': reverted_card_payment?
'54': supported_cards_and_currencies
'55': terminate_account
'56': top_up_by_bank_transfer_charge
'57': top_up_by_card_charge
'58': top_up_by_cash_or_cheque
'59': top_up_failed
'60': top_up_limits
'61': top_up_reverted
'62': topping_up_by_card
'63': transaction_charged_twice
'64': transfer_fee_charged
'65': transfer_into_account
'66': transfer_not_received_by_recipient
'67': transfer_timing
'68': unable_to_verify_identity
'69': verify_my_identity
'70': verify_source_of_funds
'71': verify_top_up
'72': virtual_card_not_working
'73': visa_or_mastercard
'74': why_verify_identity
'75': wrong_amount_of_cash_received
'76': wrong_exchange_rate_for_cash_withdrawal
splits:
- name: test
num_bytes: 204010
num_examples: 3080
download_size: 89116
dataset_size: 204010
---
# Dataset Card for "banking_with_vectors"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rai-sandeep/dataset_format_actual_1 | ---
dataset_info:
features:
- name: task
dtype: string
- name: format
dtype: string
splits:
- name: train
num_bytes: 557
num_examples: 2
download_size: 3190
dataset_size: 557
---
# Dataset Card for "dataset_format_actual_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xiaomofa/metadata | ---
license: mit
---
|
open-llm-leaderboard/details_Devio__test-1400 | ---
pretty_name: Evaluation run of Devio/test-1400
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Devio/test-1400](https://huggingface.co/Devio/test-1400) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_Devio__test-1400\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-03T06:25:15.872451](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__test-1400/blob/main/results_2023-09-03T06%3A25%3A15.872451.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\": 0.29066385939253414,\n\
\ \"acc_stderr\": 0.032634153881095015,\n \"acc_norm\": 0.2942628467289629,\n\
\ \"acc_norm_stderr\": 0.03263364427629342,\n \"mc1\": 0.22766217870257038,\n\
\ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.3686966632375142,\n\
\ \"mc2_stderr\": 0.014163025545486835\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.35238907849829354,\n \"acc_stderr\": 0.013960142600598685,\n\
\ \"acc_norm\": 0.38139931740614336,\n \"acc_norm_stderr\": 0.014194389086685263\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.4785899223262298,\n\
\ \"acc_stderr\": 0.004985204766555062,\n \"acc_norm\": 0.6619199362676758,\n\
\ \"acc_norm_stderr\": 0.004720891597174716\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.24,\n \"acc_stderr\": 0.04292346959909283,\n \
\ \"acc_norm\": 0.24,\n \"acc_norm_stderr\": 0.04292346959909283\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.22962962962962963,\n\
\ \"acc_stderr\": 0.036333844140734636,\n \"acc_norm\": 0.22962962962962963,\n\
\ \"acc_norm_stderr\": 0.036333844140734636\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3684210526315789,\n \"acc_stderr\": 0.03925523381052932,\n\
\ \"acc_norm\": 0.3684210526315789,\n \"acc_norm_stderr\": 0.03925523381052932\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.21,\n\
\ \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.21,\n \
\ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.3169811320754717,\n \"acc_stderr\": 0.028637235639800935,\n\
\ \"acc_norm\": 0.3169811320754717,\n \"acc_norm_stderr\": 0.028637235639800935\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.24305555555555555,\n\
\ \"acc_stderr\": 0.03586879280080343,\n \"acc_norm\": 0.24305555555555555,\n\
\ \"acc_norm_stderr\": 0.03586879280080343\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \"acc_norm\"\
: 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3063583815028902,\n\
\ \"acc_stderr\": 0.03514942551267439,\n \"acc_norm\": 0.3063583815028902,\n\
\ \"acc_norm_stderr\": 0.03514942551267439\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.28,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.3446808510638298,\n \"acc_stderr\": 0.03106898596312215,\n\
\ \"acc_norm\": 0.3446808510638298,\n \"acc_norm_stderr\": 0.03106898596312215\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.23684210526315788,\n\
\ \"acc_stderr\": 0.039994238792813344,\n \"acc_norm\": 0.23684210526315788,\n\
\ \"acc_norm_stderr\": 0.039994238792813344\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.2620689655172414,\n \"acc_stderr\": 0.036646663372252565,\n\
\ \"acc_norm\": 0.2620689655172414,\n \"acc_norm_stderr\": 0.036646663372252565\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2857142857142857,\n \"acc_stderr\": 0.023266512213730564,\n \"\
acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.023266512213730564\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.31746031746031744,\n\
\ \"acc_stderr\": 0.04163453031302859,\n \"acc_norm\": 0.31746031746031744,\n\
\ \"acc_norm_stderr\": 0.04163453031302859\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\": 0.3225806451612903,\n\
\ \"acc_stderr\": 0.026593084516572274,\n \"acc_norm\": 0.3225806451612903,\n\
\ \"acc_norm_stderr\": 0.026593084516572274\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.2857142857142857,\n \"acc_stderr\": 0.0317852971064275,\n\
\ \"acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.0317852971064275\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.19,\n \"acc_stderr\": 0.03942772444036624,\n \"acc_norm\"\
: 0.19,\n \"acc_norm_stderr\": 0.03942772444036624\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.2606060606060606,\n \"acc_stderr\": 0.034277431758165236,\n\
\ \"acc_norm\": 0.2606060606060606,\n \"acc_norm_stderr\": 0.034277431758165236\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.3686868686868687,\n \"acc_stderr\": 0.034373055019806184,\n \"\
acc_norm\": 0.3686868686868687,\n \"acc_norm_stderr\": 0.034373055019806184\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.35233160621761656,\n \"acc_stderr\": 0.03447478286414359,\n\
\ \"acc_norm\": 0.35233160621761656,\n \"acc_norm_stderr\": 0.03447478286414359\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.36153846153846153,\n \"acc_stderr\": 0.02435958146539698,\n\
\ \"acc_norm\": 0.36153846153846153,\n \"acc_norm_stderr\": 0.02435958146539698\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.24074074074074073,\n \"acc_stderr\": 0.026067159222275805,\n \
\ \"acc_norm\": 0.24074074074074073,\n \"acc_norm_stderr\": 0.026067159222275805\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.33613445378151263,\n \"acc_stderr\": 0.03068473711513536,\n\
\ \"acc_norm\": 0.33613445378151263,\n \"acc_norm_stderr\": 0.03068473711513536\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.32450331125827814,\n \"acc_stderr\": 0.03822746937658754,\n \"\
acc_norm\": 0.32450331125827814,\n \"acc_norm_stderr\": 0.03822746937658754\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.3376146788990826,\n \"acc_stderr\": 0.0202752659866389,\n \"acc_norm\"\
: 0.3376146788990826,\n \"acc_norm_stderr\": 0.0202752659866389\n },\n\
\ \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\": 0.4074074074074074,\n\
\ \"acc_stderr\": 0.03350991604696043,\n \"acc_norm\": 0.4074074074074074,\n\
\ \"acc_norm_stderr\": 0.03350991604696043\n },\n \"harness|hendrycksTest-high_school_us_history|5\"\
: {\n \"acc\": 0.25980392156862747,\n \"acc_stderr\": 0.03077855467869326,\n\
\ \"acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.03077855467869326\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.19831223628691982,\n \"acc_stderr\": 0.025955020841621115,\n \
\ \"acc_norm\": 0.19831223628691982,\n \"acc_norm_stderr\": 0.025955020841621115\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.26905829596412556,\n\
\ \"acc_stderr\": 0.029763779406874972,\n \"acc_norm\": 0.26905829596412556,\n\
\ \"acc_norm_stderr\": 0.029763779406874972\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.31297709923664124,\n \"acc_stderr\": 0.04066962905677697,\n\
\ \"acc_norm\": 0.31297709923664124,\n \"acc_norm_stderr\": 0.04066962905677697\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.1322314049586777,\n \"acc_stderr\": 0.030922788320445784,\n \"\
acc_norm\": 0.1322314049586777,\n \"acc_norm_stderr\": 0.030922788320445784\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.2085889570552147,\n \"acc_stderr\": 0.03192193448934722,\n\
\ \"acc_norm\": 0.2085889570552147,\n \"acc_norm_stderr\": 0.03192193448934722\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.16071428571428573,\n\
\ \"acc_stderr\": 0.03485946096475741,\n \"acc_norm\": 0.16071428571428573,\n\
\ \"acc_norm_stderr\": 0.03485946096475741\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.4077669902912621,\n \"acc_stderr\": 0.048657775704107696,\n\
\ \"acc_norm\": 0.4077669902912621,\n \"acc_norm_stderr\": 0.048657775704107696\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.23504273504273504,\n\
\ \"acc_stderr\": 0.02777883590493543,\n \"acc_norm\": 0.23504273504273504,\n\
\ \"acc_norm_stderr\": 0.02777883590493543\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.24521072796934865,\n\
\ \"acc_stderr\": 0.015384352284543932,\n \"acc_norm\": 0.24521072796934865,\n\
\ \"acc_norm_stderr\": 0.015384352284543932\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.2745664739884393,\n \"acc_stderr\": 0.024027745155265023,\n\
\ \"acc_norm\": 0.2745664739884393,\n \"acc_norm_stderr\": 0.024027745155265023\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.2536312849162011,\n\
\ \"acc_stderr\": 0.014551553659369922,\n \"acc_norm\": 0.2536312849162011,\n\
\ \"acc_norm_stderr\": 0.014551553659369922\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.3104575163398693,\n \"acc_stderr\": 0.0264930332251459,\n\
\ \"acc_norm\": 0.3104575163398693,\n \"acc_norm_stderr\": 0.0264930332251459\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.29260450160771706,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.29260450160771706,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.25617283950617287,\n \"acc_stderr\": 0.0242885336377261,\n\
\ \"acc_norm\": 0.25617283950617287,\n \"acc_norm_stderr\": 0.0242885336377261\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.28368794326241137,\n \"acc_stderr\": 0.026891709428343957,\n \
\ \"acc_norm\": 0.28368794326241137,\n \"acc_norm_stderr\": 0.026891709428343957\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.2438070404172099,\n\
\ \"acc_stderr\": 0.010966507972178479,\n \"acc_norm\": 0.2438070404172099,\n\
\ \"acc_norm_stderr\": 0.010966507972178479\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.4227941176470588,\n \"acc_stderr\": 0.03000856284500347,\n\
\ \"acc_norm\": 0.4227941176470588,\n \"acc_norm_stderr\": 0.03000856284500347\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.23529411764705882,\n \"acc_stderr\": 0.01716058723504635,\n \
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.01716058723504635\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.2818181818181818,\n\
\ \"acc_stderr\": 0.043091187099464585,\n \"acc_norm\": 0.2818181818181818,\n\
\ \"acc_norm_stderr\": 0.043091187099464585\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.031362502409358936,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.031362502409358936\n \
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.3333333333333333,\n\
\ \"acc_stderr\": 0.03333333333333335,\n \"acc_norm\": 0.3333333333333333,\n\
\ \"acc_norm_stderr\": 0.03333333333333335\n },\n \"harness|hendrycksTest-us_foreign_policy|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-virology|5\": {\n \"acc\": 0.25301204819277107,\n\
\ \"acc_stderr\": 0.03384429155233136,\n \"acc_norm\": 0.25301204819277107,\n\
\ \"acc_norm_stderr\": 0.03384429155233136\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.23391812865497075,\n \"acc_stderr\": 0.03246721765117826,\n\
\ \"acc_norm\": 0.23391812865497075,\n \"acc_norm_stderr\": 0.03246721765117826\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.22766217870257038,\n\
\ \"mc1_stderr\": 0.01467925503211107,\n \"mc2\": 0.3686966632375142,\n\
\ \"mc2_stderr\": 0.014163025545486835\n }\n}\n```"
repo_url: https://huggingface.co/Devio/test-1400
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|arc:challenge|25_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hellaswag|10_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T06:25:15.872451.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-03T06:25:15.872451.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T06:25:15.872451.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-03T06:25:15.872451.parquet'
- config_name: results
data_files:
- split: 2023_09_03T06_25_15.872451
path:
- results_2023-09-03T06:25:15.872451.parquet
- split: latest
path:
- results_2023-09-03T06:25:15.872451.parquet
---
# Dataset Card for Evaluation run of Devio/test-1400
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Devio/test-1400
- **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 [Devio/test-1400](https://huggingface.co/Devio/test-1400) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_Devio__test-1400",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-03T06:25:15.872451](https://huggingface.co/datasets/open-llm-leaderboard/details_Devio__test-1400/blob/main/results_2023-09-03T06%3A25%3A15.872451.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": 0.29066385939253414,
"acc_stderr": 0.032634153881095015,
"acc_norm": 0.2942628467289629,
"acc_norm_stderr": 0.03263364427629342,
"mc1": 0.22766217870257038,
"mc1_stderr": 0.01467925503211107,
"mc2": 0.3686966632375142,
"mc2_stderr": 0.014163025545486835
},
"harness|arc:challenge|25": {
"acc": 0.35238907849829354,
"acc_stderr": 0.013960142600598685,
"acc_norm": 0.38139931740614336,
"acc_norm_stderr": 0.014194389086685263
},
"harness|hellaswag|10": {
"acc": 0.4785899223262298,
"acc_stderr": 0.004985204766555062,
"acc_norm": 0.6619199362676758,
"acc_norm_stderr": 0.004720891597174716
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.24,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.24,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.22962962962962963,
"acc_stderr": 0.036333844140734636,
"acc_norm": 0.22962962962962963,
"acc_norm_stderr": 0.036333844140734636
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3684210526315789,
"acc_stderr": 0.03925523381052932,
"acc_norm": 0.3684210526315789,
"acc_norm_stderr": 0.03925523381052932
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.21,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.21,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.3169811320754717,
"acc_stderr": 0.028637235639800935,
"acc_norm": 0.3169811320754717,
"acc_norm_stderr": 0.028637235639800935
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.24305555555555555,
"acc_stderr": 0.03586879280080343,
"acc_norm": 0.24305555555555555,
"acc_norm_stderr": 0.03586879280080343
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.3063583815028902,
"acc_stderr": 0.03514942551267439,
"acc_norm": 0.3063583815028902,
"acc_norm_stderr": 0.03514942551267439
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.3446808510638298,
"acc_stderr": 0.03106898596312215,
"acc_norm": 0.3446808510638298,
"acc_norm_stderr": 0.03106898596312215
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.23684210526315788,
"acc_stderr": 0.039994238792813344,
"acc_norm": 0.23684210526315788,
"acc_norm_stderr": 0.039994238792813344
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2620689655172414,
"acc_stderr": 0.036646663372252565,
"acc_norm": 0.2620689655172414,
"acc_norm_stderr": 0.036646663372252565
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.023266512213730564,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.023266512213730564
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.31746031746031744,
"acc_stderr": 0.04163453031302859,
"acc_norm": 0.31746031746031744,
"acc_norm_stderr": 0.04163453031302859
},
"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": 0.3225806451612903,
"acc_stderr": 0.026593084516572274,
"acc_norm": 0.3225806451612903,
"acc_norm_stderr": 0.026593084516572274
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.2857142857142857,
"acc_stderr": 0.0317852971064275,
"acc_norm": 0.2857142857142857,
"acc_norm_stderr": 0.0317852971064275
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.19,
"acc_stderr": 0.03942772444036624,
"acc_norm": 0.19,
"acc_norm_stderr": 0.03942772444036624
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.2606060606060606,
"acc_stderr": 0.034277431758165236,
"acc_norm": 0.2606060606060606,
"acc_norm_stderr": 0.034277431758165236
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.3686868686868687,
"acc_stderr": 0.034373055019806184,
"acc_norm": 0.3686868686868687,
"acc_norm_stderr": 0.034373055019806184
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.35233160621761656,
"acc_stderr": 0.03447478286414359,
"acc_norm": 0.35233160621761656,
"acc_norm_stderr": 0.03447478286414359
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.36153846153846153,
"acc_stderr": 0.02435958146539698,
"acc_norm": 0.36153846153846153,
"acc_norm_stderr": 0.02435958146539698
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.24074074074074073,
"acc_stderr": 0.026067159222275805,
"acc_norm": 0.24074074074074073,
"acc_norm_stderr": 0.026067159222275805
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.33613445378151263,
"acc_stderr": 0.03068473711513536,
"acc_norm": 0.33613445378151263,
"acc_norm_stderr": 0.03068473711513536
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.32450331125827814,
"acc_stderr": 0.03822746937658754,
"acc_norm": 0.32450331125827814,
"acc_norm_stderr": 0.03822746937658754
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.3376146788990826,
"acc_stderr": 0.0202752659866389,
"acc_norm": 0.3376146788990826,
"acc_norm_stderr": 0.0202752659866389
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4074074074074074,
"acc_stderr": 0.03350991604696043,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.03350991604696043
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.25980392156862747,
"acc_stderr": 0.03077855467869326,
"acc_norm": 0.25980392156862747,
"acc_norm_stderr": 0.03077855467869326
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.19831223628691982,
"acc_stderr": 0.025955020841621115,
"acc_norm": 0.19831223628691982,
"acc_norm_stderr": 0.025955020841621115
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.26905829596412556,
"acc_stderr": 0.029763779406874972,
"acc_norm": 0.26905829596412556,
"acc_norm_stderr": 0.029763779406874972
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.31297709923664124,
"acc_stderr": 0.04066962905677697,
"acc_norm": 0.31297709923664124,
"acc_norm_stderr": 0.04066962905677697
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.1322314049586777,
"acc_stderr": 0.030922788320445784,
"acc_norm": 0.1322314049586777,
"acc_norm_stderr": 0.030922788320445784
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.25,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.2085889570552147,
"acc_stderr": 0.03192193448934722,
"acc_norm": 0.2085889570552147,
"acc_norm_stderr": 0.03192193448934722
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.16071428571428573,
"acc_stderr": 0.03485946096475741,
"acc_norm": 0.16071428571428573,
"acc_norm_stderr": 0.03485946096475741
},
"harness|hendrycksTest-management|5": {
"acc": 0.4077669902912621,
"acc_stderr": 0.048657775704107696,
"acc_norm": 0.4077669902912621,
"acc_norm_stderr": 0.048657775704107696
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.23504273504273504,
"acc_stderr": 0.02777883590493543,
"acc_norm": 0.23504273504273504,
"acc_norm_stderr": 0.02777883590493543
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.25,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.25,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
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"acc_stderr": 0.015384352284543932,
"acc_norm": 0.24521072796934865,
"acc_norm_stderr": 0.015384352284543932
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.2745664739884393,
"acc_stderr": 0.024027745155265023,
"acc_norm": 0.2745664739884393,
"acc_norm_stderr": 0.024027745155265023
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.2536312849162011,
"acc_stderr": 0.014551553659369922,
"acc_norm": 0.2536312849162011,
"acc_norm_stderr": 0.014551553659369922
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.3104575163398693,
"acc_stderr": 0.0264930332251459,
"acc_norm": 0.3104575163398693,
"acc_norm_stderr": 0.0264930332251459
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.29260450160771706,
"acc_stderr": 0.02583989833487798,
"acc_norm": 0.29260450160771706,
"acc_norm_stderr": 0.02583989833487798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.25617283950617287,
"acc_stderr": 0.0242885336377261,
"acc_norm": 0.25617283950617287,
"acc_norm_stderr": 0.0242885336377261
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.28368794326241137,
"acc_stderr": 0.026891709428343957,
"acc_norm": 0.28368794326241137,
"acc_norm_stderr": 0.026891709428343957
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.2438070404172099,
"acc_stderr": 0.010966507972178479,
"acc_norm": 0.2438070404172099,
"acc_norm_stderr": 0.010966507972178479
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.4227941176470588,
"acc_stderr": 0.03000856284500347,
"acc_norm": 0.4227941176470588,
"acc_norm_stderr": 0.03000856284500347
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.01716058723504635,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.01716058723504635
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.2818181818181818,
"acc_stderr": 0.043091187099464585,
"acc_norm": 0.2818181818181818,
"acc_norm_stderr": 0.043091187099464585
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.4,
"acc_stderr": 0.031362502409358936,
"acc_norm": 0.4,
"acc_norm_stderr": 0.031362502409358936
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.03333333333333335,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.03333333333333335
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-virology|5": {
"acc": 0.25301204819277107,
"acc_stderr": 0.03384429155233136,
"acc_norm": 0.25301204819277107,
"acc_norm_stderr": 0.03384429155233136
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.23391812865497075,
"acc_stderr": 0.03246721765117826,
"acc_norm": 0.23391812865497075,
"acc_norm_stderr": 0.03246721765117826
},
"harness|truthfulqa:mc|0": {
"mc1": 0.22766217870257038,
"mc1_stderr": 0.01467925503211107,
"mc2": 0.3686966632375142,
"mc2_stderr": 0.014163025545486835
}
}
```
### 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] |
open-llm-leaderboard/details_RatanRohith__NeuralPizza-WestSeverus-7B-Merge-slerp | ---
pretty_name: Evaluation run of RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp](https://huggingface.co/RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp)\
\ 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_RatanRohith__NeuralPizza-WestSeverus-7B-Merge-slerp\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-25T21:04:38.751124](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-WestSeverus-7B-Merge-slerp/blob/main/results_2024-01-25T21-04-38.751124.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\": 0.6527758792986089,\n\
\ \"acc_stderr\": 0.032143078623365365,\n \"acc_norm\": 0.6525404152876083,\n\
\ \"acc_norm_stderr\": 0.032810654486367455,\n \"mc1\": 0.5458996328029376,\n\
\ \"mc1_stderr\": 0.017429593091323515,\n \"mc2\": 0.7040216304728647,\n\
\ \"mc2_stderr\": 0.014901566636067547\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6919795221843004,\n \"acc_stderr\": 0.013491429517292035,\n\
\ \"acc_norm\": 0.7141638225255973,\n \"acc_norm_stderr\": 0.013203196088537372\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7023501294562836,\n\
\ \"acc_stderr\": 0.0045629026049387395,\n \"acc_norm\": 0.8824935271858195,\n\
\ \"acc_norm_stderr\": 0.0032136470410029463\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6296296296296297,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.6296296296296297,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.62,\n\
\ \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\": 0.62,\n \
\ \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.02825420034443866,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.02825420034443866\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\"\
: 0.53,\n \"acc_stderr\": 0.050161355804659205,\n \"acc_norm\": 0.53,\n\
\ \"acc_norm_stderr\": 0.050161355804659205\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6763005780346821,\n\
\ \"acc_stderr\": 0.0356760379963917,\n \"acc_norm\": 0.6763005780346821,\n\
\ \"acc_norm_stderr\": 0.0356760379963917\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768077,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768077\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6085106382978723,\n \"acc_stderr\": 0.03190701242326812,\n\
\ \"acc_norm\": 0.6085106382978723,\n \"acc_norm_stderr\": 0.03190701242326812\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\": 0.5586206896551724,\n \"acc_stderr\": 0.04137931034482757,\n\
\ \"acc_norm\": 0.5586206896551724,\n \"acc_norm_stderr\": 0.04137931034482757\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4523809523809524,\n\
\ \"acc_stderr\": 0.044518079590553275,\n \"acc_norm\": 0.4523809523809524,\n\
\ \"acc_norm_stderr\": 0.044518079590553275\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7774193548387097,\n\
\ \"acc_stderr\": 0.02366421667164252,\n \"acc_norm\": 0.7774193548387097,\n\
\ \"acc_norm_stderr\": 0.02366421667164252\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\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.03256866661681102,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.03256866661681102\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.797979797979798,\n \"acc_stderr\": 0.028606204289229865,\n \"\
acc_norm\": 0.797979797979798,\n \"acc_norm_stderr\": 0.028606204289229865\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768763,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768763\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6692307692307692,\n \"acc_stderr\": 0.023854795680971125,\n\
\ \"acc_norm\": 0.6692307692307692,\n \"acc_norm_stderr\": 0.023854795680971125\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616255,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616255\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6932773109243697,\n \"acc_stderr\": 0.029953823891887037,\n\
\ \"acc_norm\": 0.6932773109243697,\n \"acc_norm_stderr\": 0.029953823891887037\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3708609271523179,\n \"acc_stderr\": 0.03943966699183629,\n \"\
acc_norm\": 0.3708609271523179,\n \"acc_norm_stderr\": 0.03943966699183629\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8403669724770643,\n \"acc_stderr\": 0.015703498348461783,\n \"\
acc_norm\": 0.8403669724770643,\n \"acc_norm_stderr\": 0.015703498348461783\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5046296296296297,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.5046296296296297,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8186274509803921,\n \"acc_stderr\": 0.027044621719474082,\n \"\
acc_norm\": 0.8186274509803921,\n \"acc_norm_stderr\": 0.027044621719474082\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.031024411740572213,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.031024411740572213\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752599,\n\
\ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752599\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.0401910747255735,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.0401910747255735\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\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507333,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507333\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.71,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8288633461047255,\n\
\ \"acc_stderr\": 0.013468201614066309,\n \"acc_norm\": 0.8288633461047255,\n\
\ \"acc_norm_stderr\": 0.013468201614066309\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.02378620325550829,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.02378620325550829\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4134078212290503,\n\
\ \"acc_stderr\": 0.016469814928406167,\n \"acc_norm\": 0.4134078212290503,\n\
\ \"acc_norm_stderr\": 0.016469814928406167\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7124183006535948,\n \"acc_stderr\": 0.02591780611714716,\n\
\ \"acc_norm\": 0.7124183006535948,\n \"acc_norm_stderr\": 0.02591780611714716\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7530864197530864,\n \"acc_stderr\": 0.023993501709042107,\n\
\ \"acc_norm\": 0.7530864197530864,\n \"acc_norm_stderr\": 0.023993501709042107\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46740547588005216,\n\
\ \"acc_stderr\": 0.01274307294265335,\n \"acc_norm\": 0.46740547588005216,\n\
\ \"acc_norm_stderr\": 0.01274307294265335\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6654411764705882,\n \"acc_stderr\": 0.028661996202335303,\n\
\ \"acc_norm\": 0.6654411764705882,\n \"acc_norm_stderr\": 0.028661996202335303\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6699346405228758,\n \"acc_stderr\": 0.019023726160724553,\n \
\ \"acc_norm\": 0.6699346405228758,\n \"acc_norm_stderr\": 0.019023726160724553\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\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784596,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8507462686567164,\n\
\ \"acc_stderr\": 0.02519692987482707,\n \"acc_norm\": 0.8507462686567164,\n\
\ \"acc_norm_stderr\": 0.02519692987482707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.87,\n \"acc_stderr\": 0.033799766898963086,\n \
\ \"acc_norm\": 0.87,\n \"acc_norm_stderr\": 0.033799766898963086\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8421052631578947,\n \"acc_stderr\": 0.027966785859160893,\n\
\ \"acc_norm\": 0.8421052631578947,\n \"acc_norm_stderr\": 0.027966785859160893\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5458996328029376,\n\
\ \"mc1_stderr\": 0.017429593091323515,\n \"mc2\": 0.7040216304728647,\n\
\ \"mc2_stderr\": 0.014901566636067547\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8310970797158642,\n \"acc_stderr\": 0.01052998141183891\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.690674753601213,\n \
\ \"acc_stderr\": 0.012731710925078138\n }\n}\n```"
repo_url: https://huggingface.co/RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|arc:challenge|25_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|gsm8k|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hellaswag|10_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T21-04-38.751124.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-25T21-04-38.751124.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- '**/details_harness|winogrande|5_2024-01-25T21-04-38.751124.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-25T21-04-38.751124.parquet'
- config_name: results
data_files:
- split: 2024_01_25T21_04_38.751124
path:
- results_2024-01-25T21-04-38.751124.parquet
- split: latest
path:
- results_2024-01-25T21-04-38.751124.parquet
---
# Dataset Card for Evaluation run of RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp](https://huggingface.co/RatanRohith/NeuralPizza-WestSeverus-7B-Merge-slerp) 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_RatanRohith__NeuralPizza-WestSeverus-7B-Merge-slerp",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-25T21:04:38.751124](https://huggingface.co/datasets/open-llm-leaderboard/details_RatanRohith__NeuralPizza-WestSeverus-7B-Merge-slerp/blob/main/results_2024-01-25T21-04-38.751124.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": 0.6527758792986089,
"acc_stderr": 0.032143078623365365,
"acc_norm": 0.6525404152876083,
"acc_norm_stderr": 0.032810654486367455,
"mc1": 0.5458996328029376,
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```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
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### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
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#### Who are the source data producers?
<!-- 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. -->
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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
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[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
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qgallouedec/prj_gia_dataset_metaworld_drawer_open_v2_1111 | ---
library_name: gia
tags:
- deep-reinforcement-learning
- reinforcement-learning
- gia
- multi-task
- multi-modal
- imitation-learning
- offline-reinforcement-learning
---
An imitation learning environment for the drawer-open-v2 environment, sample for the policy drawer-open-v2
This environment was created as part of the Generally Intelligent Agents project gia: https://github.com/huggingface/gia
## Load dataset
First, clone it with
```sh
git clone https://huggingface.co/datasets/qgallouedec/prj_gia_dataset_metaworld_drawer_open_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_drawer_open_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
abeer411/researchGan | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': MEN_Coats
'1': MEN_Hood
'2': MEN_Sneaker
'3': MEN_Suits
'4': MEN_Watch
'5': WOMEN_Bag
'6': WOMEN_Dress
'7': WOMEN_Hood
'8': WOMEN_Sundals
'9': WOMEN_Watch
splits:
- name: train
num_bytes: 199393244.8
num_examples: 19040
- name: test
num_bytes: 17493351.28
num_examples: 1340
download_size: 251276829
dataset_size: 216886596.08
---
# Dataset Card for "researchGan"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DLI-Lab/DONUT | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: context_id
dtype: int64
- name: candidate_id
dtype: int64
- name: context
sequence: string
- name: target
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 319463974
num_examples: 367337
download_size: 51456522
dataset_size: 319463974
---
# Dataset Card for "DONUT"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Brendan/icdst_multiwoz_turns_v24 | ---
dataset_info:
features:
- name: dialogue_id
dtype: string
- name: turn_id
dtype: int8
- name: domains
sequence: string
- name: user_utterances
sequence: string
- name: system_utterances
sequence: string
- name: slot_values
struct:
- name: hotel
struct:
- name: price range
dtype: string
- name: type
dtype: string
- name: parking
dtype: string
- name: book day
dtype: string
- name: book people
dtype: string
- name: book stay
dtype: string
- name: stars
dtype: string
- name: internet
dtype: string
- name: name
dtype: string
- name: area
dtype: string
- name: train
struct:
- name: arrive by
dtype: string
- name: departure
dtype: string
- name: day
dtype: string
- name: book people
dtype: string
- name: leave at
dtype: string
- name: destination
dtype: string
- name: attraction
struct:
- name: area
dtype: string
- name: name
dtype: string
- name: type
dtype: string
- name: restaurant
struct:
- name: price range
dtype: string
- name: area
dtype: string
- name: food
dtype: string
- name: name
dtype: string
- name: book day
dtype: string
- name: book people
dtype: string
- name: book time
dtype: string
- name: taxi
struct:
- name: leave at
dtype: string
- name: destination
dtype: string
- name: departure
dtype: string
- name: arrive by
dtype: string
- name: turn_slot_values
struct:
- name: hotel
struct:
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dtype: string
- name: type
dtype: string
- name: parking
dtype: string
- name: book day
dtype: string
- name: book people
dtype: string
- name: book stay
dtype: string
- name: stars
dtype: string
- name: internet
dtype: string
- name: name
dtype: string
- name: area
dtype: string
- name: train
struct:
- name: arrive by
dtype: string
- name: departure
dtype: string
- name: day
dtype: string
- name: book people
dtype: string
- name: leave at
dtype: string
- name: destination
dtype: string
- name: attraction
struct:
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dtype: string
- name: name
dtype: string
- name: type
dtype: string
- name: restaurant
struct:
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dtype: string
- name: area
dtype: string
- name: food
dtype: string
- name: name
dtype: string
- name: book day
dtype: string
- name: book people
dtype: string
- name: book time
dtype: string
- name: taxi
struct:
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dtype: string
- name: destination
dtype: string
- name: departure
dtype: string
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struct:
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dtype: string
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struct:
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- name: restaurant
struct:
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dtype: string
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dtype: string
- name: name
dtype: string
- name: book day
dtype: string
- name: book people
dtype: string
- name: book time
dtype: string
- name: taxi
struct:
- name: leave at
dtype: string
- name: destination
dtype: string
- name: departure
dtype: string
- name: arrive by
dtype: string
- name: system_response_acts
sequence: string
- name: system_response
dtype: string
splits:
- name: train
num_bytes: 78112115
num_examples: 54971
- name: validation
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num_examples: 7374
- name: test
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- name: valid_20p_ablation
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- name: valid_10p
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- name: valid_50p
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num_examples: 3698
- name: 1p_train_v1
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- name: 1p_train_v2
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- name: 1p_train_v3
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num_examples: 579
- name: 5p_train_v1
num_bytes: 3880667.735078496
num_examples: 2731
- name: 5p_train_v2
num_bytes: 3913350.0338360225
num_examples: 2754
- name: 5p_train_v3
num_bytes: 3806777.3204962616
num_examples: 2679
- name: 10p_train_v1
num_bytes: 7786912.921358534
num_examples: 5480
- name: 10p_train_v2
num_bytes: 7785491.951847338
num_examples: 5479
- name: 10p_train_v3
num_bytes: 7691707.964108348
num_examples: 5413
download_size: 6875945
dataset_size: 145293067.4987746
---
# Dataset Card for "icdst_multiwoz_turns_v24"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DataStudio/OCR_documents_bluir_part_03 | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 2448218573.375
num_examples: 158677
download_size: 2442829101
dataset_size: 2448218573.375
---
# Dataset Card for "OCR_document_bluir_part_03"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
KonghaYao/juejin_article_intro | ---
license: cc-by-nc-nd-4.0
---
|
arieg/cluster11_large_150 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '000615'
'1': 000897
'2': '001673'
'3': '003761'
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'95': '149778'
'96': '150265'
'97': '151404'
splits:
- name: train
num_bytes: 728489341.1
num_examples: 14700
download_size: 720192838
dataset_size: 728489341.1
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_OpenBuddy__openbuddy-llama2-13b-v11-bf16 | ---
pretty_name: Evaluation run of OpenBuddy/openbuddy-llama2-13b-v11-bf16
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [OpenBuddy/openbuddy-llama2-13b-v11-bf16](https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v11-bf16)\
\ 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_OpenBuddy__openbuddy-llama2-13b-v11-bf16\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-15T20:56:25.450892](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-llama2-13b-v11-bf16/blob/main/results_2023-10-15T20-56-25.450892.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\": 0.35371224832214765,\n\
\ \"em_stderr\": 0.004896408727607699,\n \"f1\": 0.4163443791946322,\n\
\ \"f1_stderr\": 0.004752347784514718,\n \"acc\": 0.4495593813447201,\n\
\ \"acc_stderr\": 0.011763906822420508\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.35371224832214765,\n \"em_stderr\": 0.004896408727607699,\n\
\ \"f1\": 0.4163443791946322,\n \"f1_stderr\": 0.004752347784514718\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.18877937831690675,\n \
\ \"acc_stderr\": 0.010779262837202753\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7103393843725335,\n \"acc_stderr\": 0.012748550807638263\n\
\ }\n}\n```"
repo_url: https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v11-bf16
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|arc:challenge|25_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_10_15T20_56_25.450892
path:
- '**/details_harness|drop|3_2023-10-15T20-56-25.450892.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-15T20-56-25.450892.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_15T20_56_25.450892
path:
- '**/details_harness|gsm8k|5_2023-10-15T20-56-25.450892.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-15T20-56-25.450892.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hellaswag|10_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
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- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T02:00:08.524632.parquet'
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- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
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- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T02:00:08.524632.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_24T02_00_08.524632
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T02:00:08.524632.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-24T02:00:08.524632.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_15T20_56_25.450892
path:
- '**/details_harness|winogrande|5_2023-10-15T20-56-25.450892.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-15T20-56-25.450892.parquet'
- config_name: results
data_files:
- split: 2023_10_15T20_56_25.450892
path:
- results_2023-10-15T20-56-25.450892.parquet
- split: latest
path:
- results_2023-10-15T20-56-25.450892.parquet
---
# Dataset Card for Evaluation run of OpenBuddy/openbuddy-llama2-13b-v11-bf16
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v11-bf16
- **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 [OpenBuddy/openbuddy-llama2-13b-v11-bf16](https://huggingface.co/OpenBuddy/openbuddy-llama2-13b-v11-bf16) 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_OpenBuddy__openbuddy-llama2-13b-v11-bf16",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-15T20:56:25.450892](https://huggingface.co/datasets/open-llm-leaderboard/details_OpenBuddy__openbuddy-llama2-13b-v11-bf16/blob/main/results_2023-10-15T20-56-25.450892.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": 0.35371224832214765,
"em_stderr": 0.004896408727607699,
"f1": 0.4163443791946322,
"f1_stderr": 0.004752347784514718,
"acc": 0.4495593813447201,
"acc_stderr": 0.011763906822420508
},
"harness|drop|3": {
"em": 0.35371224832214765,
"em_stderr": 0.004896408727607699,
"f1": 0.4163443791946322,
"f1_stderr": 0.004752347784514718
},
"harness|gsm8k|5": {
"acc": 0.18877937831690675,
"acc_stderr": 0.010779262837202753
},
"harness|winogrande|5": {
"acc": 0.7103393843725335,
"acc_stderr": 0.012748550807638263
}
}
```
### 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] |
liuyanchen1015/MULTI_VALUE_cola_plural_to_singular_human | ---
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: 10222
num_examples: 123
- name: test
num_bytes: 10626
num_examples: 132
- name: train
num_bytes: 61889
num_examples: 768
download_size: 43177
dataset_size: 82737
---
# Dataset Card for "MULTI_VALUE_cola_plural_to_singular_human"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
readerbench/ro-stories | ---
license: apache-2.0
language:
- ro
tags:
- dataset
- romanian
- stories
size_categories:
- 10K<n<100K
---
The corpus consists of texts written by Romanian authors between 19th century and present, representing stories, short-stories, fairy tales and sketches.
The current version contains 19 authors, 1263 full texts and 12516 paragraphs of around 200 words each, preserving paragraphs integrity.
Note: This is an extended version of ROST corpus (https://www.kaggle.com/datasets/sandamariaavram/rost-romanian-stories-and-other-texts), which only contains 400 texts and 10 authors.
## Dataset Overview
| Author | FT | PP | M(SD) FT | M(SD) Unique Words | M(SD) TTR |
|----------------------|------|------|---------------------|----------------------|----------------------|
| Alexandru Vlahuta | 96 | 647 | 1629.16 (1341.48) | 735.19 (462.04) | 0.5110 (0.0844) |
| Anton Bacalbasa | 132 | 485 | 808.17 (720.04) | 392.20 (244.57) | 0.5256 (0.0660) |
| Barbu St. Delavrancea | 47 | 747 | 4015.40 (2224.96) | 1391.72 (658.60) | 0.3730 (0.0599) |
| Costache Negruzzi | 24 | 343 | 3482.62 (2253.38) | 1236.46 (694.14) | 0.4027 (0.0883) |
| Emil Garleanu | 55 | 353 | 1533.58 (1582.43) | 609.09 (449.03) | 0.4649 (0.0767) |
| Emilia Plugaru | 41 | 382 | 2176.71 (1705.21) | 792.00 (454.83) | 0.4091 (0.0702) |
| George Toparceanu | 46 | 331 | 1689.11 (1246.86) | 711.00 (412.92) | 0.4728 (0.0815) |
| Ioan Slavici | 89 | 1716 | 4692.76 (2156.69) | 1306.64 (485.87) | 0.3043 (0.0665) |
| Ion Creanga | 45 | 424 | 2291.13 (2328.91) | 720.96 (554.58) | 0.4420 (0.1537) |
| Ion Luca Caragiale | 60 | 585 | 2444.30 (1541.96) | 895.13 (466.55) | 0.3832 (0.0485) |
| Liviu Rebreanu | 59 | 619 | 2544.49 (1770.39) | 969.80 (518.88) | 0.4165 (0.0654) |
| Mihai Eminescu | 27 | 405 | 3642.78 (2167.54) | 1284.67 (674.06) | 0.3834 (0.0767) |
| Mihai Oltean | 32 | 68 | 409.62 (394.16) | 216.28 (174.42) | 0.5938 (0.1093) |
| Mihail Sebastian | 46 | 658 | 3478.37 (1826.51) | 1234.85 (472.30) | 0.3803 (0.0532) |
| Nicolae Filimon | 35 | 375 | 2606.57 (1701.70) | 998.20 (540.52) | 0.4173 (0.0781) |
| Nicolae Iorga | 306 | 2982 | 2437.67 (2215.16) | 970.28 (741.50) | 0.4834 (0.1054) |
| Panait Istrati | 20 | 499 | 6299.85 (1202.32) | 2177.75 (369.46) | 0.3494 (0.0240) |
| Petre Ispirescu | 40 | 630 | 3768.72 (1614.16) | 1126.40 (359.51) | 0.3183 (0.0517) |
| Traian Demetrescu | 63 | 267 | 976.13 (581.40) | 472.32 (234.24) | 0.5279 (0.0845) |
| **Aggregate** | **1263** | **12516** | | | | |
openaccess-ai-collective/5e61076265acb981eb427511ec383794 | Invalid username or password. |
johannes-garstenauer/structs_token_size_4_use_pd_True_full_amt_True_unskewed_decrease_True_factor_1200 | ---
dataset_info:
features:
- name: struct
dtype: string
splits:
- name: train
num_bytes: 3913005
num_examples: 33475
download_size: 1120873
dataset_size: 3913005
---
# Dataset Card for "structs_token_size_4_use_pd_True_full_amt_True_unskewed_decrease_True_factor_1200"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
venkat-srinivasan-nexusflow/cve_train_prompt_change_only | ---
dataset_info:
features:
- name: Input
dtype: string
- name: Output
dtype: string
- name: Cot
dtype: string
splits:
- name: train
num_bytes: 396691
num_examples: 302
download_size: 119758
dataset_size: 396691
---
# Dataset Card for "cve_train_main"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_dddsaty__Merge_Sakura_Solar | ---
pretty_name: Evaluation run of dddsaty/Merge_Sakura_Solar
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [dddsaty/Merge_Sakura_Solar](https://huggingface.co/dddsaty/Merge_Sakura_Solar)\
\ 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_dddsaty__Merge_Sakura_Solar\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-09T17:07:25.449299](https://huggingface.co/datasets/open-llm-leaderboard/details_dddsaty__Merge_Sakura_Solar/blob/main/results_2024-02-09T17-07-25.449299.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\": 0.6640792443145704,\n\
\ \"acc_stderr\": 0.03166411701044172,\n \"acc_norm\": 0.6648849979380719,\n\
\ \"acc_norm_stderr\": 0.032307129084503054,\n \"mc1\": 0.5691554467564259,\n\
\ \"mc1_stderr\": 0.01733527247533237,\n \"mc2\": 0.7220886501406486,\n\
\ \"mc2_stderr\": 0.014897285217814625\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.689419795221843,\n \"acc_stderr\": 0.01352229209805306,\n\
\ \"acc_norm\": 0.7073378839590444,\n \"acc_norm_stderr\": 0.013295916103619425\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7165903206532563,\n\
\ \"acc_stderr\": 0.004497325533959638,\n \"acc_norm\": 0.8850826528579964,\n\
\ \"acc_norm_stderr\": 0.0031827038303511323\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\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\": 0.756578947368421,\n \"acc_stderr\": 0.034923496688842384,\n\
\ \"acc_norm\": 0.756578947368421,\n \"acc_norm_stderr\": 0.034923496688842384\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n\
\ \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \
\ \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6792452830188679,\n \"acc_stderr\": 0.028727502957880267,\n\
\ \"acc_norm\": 0.6792452830188679,\n \"acc_norm_stderr\": 0.028727502957880267\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\": 0.47,\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\": 0.05021167315686779,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.05021167315686779\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6820809248554913,\n\
\ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.6820809248554913,\n\
\ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.35294117647058826,\n \"acc_stderr\": 0.04755129616062946,\n\
\ \"acc_norm\": 0.35294117647058826,\n \"acc_norm_stderr\": 0.04755129616062946\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\
\ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\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\": 0.6068965517241379,\n \"acc_stderr\": 0.040703290137070705,\n\
\ \"acc_norm\": 0.6068965517241379,\n \"acc_norm_stderr\": 0.040703290137070705\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4973544973544973,\n \"acc_stderr\": 0.02575094967813039,\n \"\
acc_norm\": 0.4973544973544973,\n \"acc_norm_stderr\": 0.02575094967813039\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8096774193548387,\n\
\ \"acc_stderr\": 0.022331707611823078,\n \"acc_norm\": 0.8096774193548387,\n\
\ \"acc_norm_stderr\": 0.022331707611823078\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5123152709359606,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.5123152709359606,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\
\ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\
acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644244,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644244\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \
\ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7100840336134454,\n \"acc_stderr\": 0.029472485833136094,\n\
\ \"acc_norm\": 0.7100840336134454,\n \"acc_norm_stderr\": 0.029472485833136094\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3576158940397351,\n \"acc_stderr\": 0.03913453431177258,\n \"\
acc_norm\": 0.3576158940397351,\n \"acc_norm_stderr\": 0.03913453431177258\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8477064220183487,\n \"acc_stderr\": 0.015405084393157074,\n \"\
acc_norm\": 0.8477064220183487,\n \"acc_norm_stderr\": 0.015405084393157074\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5601851851851852,\n \"acc_stderr\": 0.0338517797604481,\n \"acc_norm\"\
: 0.5601851851851852,\n \"acc_norm_stderr\": 0.0338517797604481\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8529411764705882,\n\
\ \"acc_stderr\": 0.024857478080250454,\n \"acc_norm\": 0.8529411764705882,\n\
\ \"acc_norm_stderr\": 0.024857478080250454\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8481012658227848,\n \"acc_stderr\": 0.023363878096632446,\n\
\ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.023363878096632446\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\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\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\": 0.7361963190184049,\n \"acc_stderr\": 0.03462419931615623,\n\
\ \"acc_norm\": 0.7361963190184049,\n \"acc_norm_stderr\": 0.03462419931615623\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.45535714285714285,\n\
\ \"acc_stderr\": 0.04726835553719099,\n \"acc_norm\": 0.45535714285714285,\n\
\ \"acc_norm_stderr\": 0.04726835553719099\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8640776699029126,\n \"acc_stderr\": 0.033932957297610096,\n\
\ \"acc_norm\": 0.8640776699029126,\n \"acc_norm_stderr\": 0.033932957297610096\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8504273504273504,\n\
\ \"acc_stderr\": 0.023365051491753715,\n \"acc_norm\": 0.8504273504273504,\n\
\ \"acc_norm_stderr\": 0.023365051491753715\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8058748403575989,\n\
\ \"acc_stderr\": 0.014143970276657569,\n \"acc_norm\": 0.8058748403575989,\n\
\ \"acc_norm_stderr\": 0.014143970276657569\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7543352601156069,\n \"acc_stderr\": 0.023176298203992005,\n\
\ \"acc_norm\": 0.7543352601156069,\n \"acc_norm_stderr\": 0.023176298203992005\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4022346368715084,\n\
\ \"acc_stderr\": 0.016399716732847142,\n \"acc_norm\": 0.4022346368715084,\n\
\ \"acc_norm_stderr\": 0.016399716732847142\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7516339869281046,\n \"acc_stderr\": 0.02473998135511359,\n\
\ \"acc_norm\": 0.7516339869281046,\n \"acc_norm_stderr\": 0.02473998135511359\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7234726688102894,\n\
\ \"acc_stderr\": 0.02540383297817961,\n \"acc_norm\": 0.7234726688102894,\n\
\ \"acc_norm_stderr\": 0.02540383297817961\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7808641975308642,\n \"acc_stderr\": 0.023016705640262196,\n\
\ \"acc_norm\": 0.7808641975308642,\n \"acc_norm_stderr\": 0.023016705640262196\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4895697522816167,\n\
\ \"acc_stderr\": 0.012767457253930647,\n \"acc_norm\": 0.4895697522816167,\n\
\ \"acc_norm_stderr\": 0.012767457253930647\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7426470588235294,\n \"acc_stderr\": 0.026556519470041513,\n\
\ \"acc_norm\": 0.7426470588235294,\n \"acc_norm_stderr\": 0.026556519470041513\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.684640522875817,\n \"acc_stderr\": 0.01879808628488688,\n \
\ \"acc_norm\": 0.684640522875817,\n \"acc_norm_stderr\": 0.01879808628488688\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7346938775510204,\n \"acc_stderr\": 0.028263889943784593,\n\
\ \"acc_norm\": 0.7346938775510204,\n \"acc_norm_stderr\": 0.028263889943784593\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616913,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616913\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.91,\n \"acc_stderr\": 0.028762349126466125,\n \
\ \"acc_norm\": 0.91,\n \"acc_norm_stderr\": 0.028762349126466125\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\
\ \"acc_stderr\": 0.03836722176598052,\n \"acc_norm\": 0.5843373493975904,\n\
\ \"acc_norm_stderr\": 0.03836722176598052\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5691554467564259,\n\
\ \"mc1_stderr\": 0.01733527247533237,\n \"mc2\": 0.7220886501406486,\n\
\ \"mc2_stderr\": 0.014897285217814625\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8271507498026835,\n \"acc_stderr\": 0.010626964529971864\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6398786959818044,\n \
\ \"acc_stderr\": 0.013222559423250485\n }\n}\n```"
repo_url: https://huggingface.co/dddsaty/Merge_Sakura_Solar
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|arc:challenge|25_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|gsm8k|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hellaswag|10_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T17-07-25.449299.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
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path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_human_aging_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
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path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
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path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
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path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_machine_learning_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
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path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
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path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_nutrition_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
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path:
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- split: latest
path:
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- config_name: harness_hendrycksTest_prehistory_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
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path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
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- config_name: harness_hendrycksTest_public_relations_5
data_files:
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path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
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path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
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path:
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- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-09T17-07-25.449299.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- '**/details_harness|winogrande|5_2024-02-09T17-07-25.449299.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-09T17-07-25.449299.parquet'
- config_name: results
data_files:
- split: 2024_02_09T17_07_25.449299
path:
- results_2024-02-09T17-07-25.449299.parquet
- split: latest
path:
- results_2024-02-09T17-07-25.449299.parquet
---
# Dataset Card for Evaluation run of dddsaty/Merge_Sakura_Solar
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [dddsaty/Merge_Sakura_Solar](https://huggingface.co/dddsaty/Merge_Sakura_Solar) 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_dddsaty__Merge_Sakura_Solar",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-09T17:07:25.449299](https://huggingface.co/datasets/open-llm-leaderboard/details_dddsaty__Merge_Sakura_Solar/blob/main/results_2024-02-09T17-07-25.449299.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": 0.6640792443145704,
"acc_stderr": 0.03166411701044172,
"acc_norm": 0.6648849979380719,
"acc_norm_stderr": 0.032307129084503054,
"mc1": 0.5691554467564259,
"mc1_stderr": 0.01733527247533237,
"mc2": 0.7220886501406486,
"mc2_stderr": 0.014897285217814625
},
"harness|arc:challenge|25": {
"acc": 0.689419795221843,
"acc_stderr": 0.01352229209805306,
"acc_norm": 0.7073378839590444,
"acc_norm_stderr": 0.013295916103619425
},
"harness|hellaswag|10": {
"acc": 0.7165903206532563,
"acc_stderr": 0.004497325533959638,
"acc_norm": 0.8850826528579964,
"acc_norm_stderr": 0.0031827038303511323
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"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": 0.756578947368421,
"acc_stderr": 0.034923496688842384,
"acc_norm": 0.756578947368421,
"acc_norm_stderr": 0.034923496688842384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.73,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.73,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6792452830188679,
"acc_stderr": 0.028727502957880267,
"acc_norm": 0.6792452830188679,
"acc_norm_stderr": 0.028727502957880267
},
"harness|hendrycksTest-college_biology|5": {
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"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"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": 0.05021167315686779,
"acc_norm": 0.52,
"acc_norm_stderr": 0.05021167315686779
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6820809248554913,
"acc_stderr": 0.0355068398916558,
"acc_norm": 0.6820809248554913,
"acc_norm_stderr": 0.0355068398916558
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.35294117647058826,
"acc_stderr": 0.04755129616062946,
"acc_norm": 0.35294117647058826,
"acc_norm_stderr": 0.04755129616062946
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.625531914893617,
"acc_stderr": 0.03163910665367291,
"acc_norm": 0.625531914893617,
"acc_norm_stderr": 0.03163910665367291
},
"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": 0.6068965517241379,
"acc_stderr": 0.040703290137070705,
"acc_norm": 0.6068965517241379,
"acc_norm_stderr": 0.040703290137070705
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4973544973544973,
"acc_stderr": 0.02575094967813039,
"acc_norm": 0.4973544973544973,
"acc_norm_stderr": 0.02575094967813039
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4365079365079365,
"acc_stderr": 0.04435932892851466,
"acc_norm": 0.4365079365079365,
"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8096774193548387,
"acc_stderr": 0.022331707611823078,
"acc_norm": 0.8096774193548387,
"acc_norm_stderr": 0.022331707611823078
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5123152709359606,
"acc_stderr": 0.035169204442208966,
"acc_norm": 0.5123152709359606,
"acc_norm_stderr": 0.035169204442208966
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542128,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8121212121212121,
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"acc_norm": 0.8121212121212121,
"acc_norm_stderr": 0.03050193405942914
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8686868686868687,
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"acc_norm": 0.8686868686868687,
"acc_norm_stderr": 0.024063156416822516
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.021995311963644244,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.021995311963644244
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6641025641025641,
"acc_stderr": 0.023946724741563976,
"acc_norm": 0.6641025641025641,
"acc_norm_stderr": 0.023946724741563976
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37037037037037035,
"acc_stderr": 0.02944316932303154,
"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.02944316932303154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7100840336134454,
"acc_stderr": 0.029472485833136094,
"acc_norm": 0.7100840336134454,
"acc_norm_stderr": 0.029472485833136094
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3576158940397351,
"acc_stderr": 0.03913453431177258,
"acc_norm": 0.3576158940397351,
"acc_norm_stderr": 0.03913453431177258
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8477064220183487,
"acc_stderr": 0.015405084393157074,
"acc_norm": 0.8477064220183487,
"acc_norm_stderr": 0.015405084393157074
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5601851851851852,
"acc_stderr": 0.0338517797604481,
"acc_norm": 0.5601851851851852,
"acc_norm_stderr": 0.0338517797604481
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8529411764705882,
"acc_stderr": 0.024857478080250454,
"acc_norm": 0.8529411764705882,
"acc_norm_stderr": 0.024857478080250454
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8481012658227848,
"acc_stderr": 0.023363878096632446,
"acc_norm": 0.8481012658227848,
"acc_norm_stderr": 0.023363878096632446
},
"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": 0.7557251908396947,
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"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.037683359597287434
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228733,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228733
},
"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": 0.7361963190184049,
"acc_stderr": 0.03462419931615623,
"acc_norm": 0.7361963190184049,
"acc_norm_stderr": 0.03462419931615623
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.45535714285714285,
"acc_stderr": 0.04726835553719099,
"acc_norm": 0.45535714285714285,
"acc_norm_stderr": 0.04726835553719099
},
"harness|hendrycksTest-management|5": {
"acc": 0.8640776699029126,
"acc_stderr": 0.033932957297610096,
"acc_norm": 0.8640776699029126,
"acc_norm_stderr": 0.033932957297610096
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8504273504273504,
"acc_stderr": 0.023365051491753715,
"acc_norm": 0.8504273504273504,
"acc_norm_stderr": 0.023365051491753715
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.69,
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"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8058748403575989,
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"acc_norm": 0.8058748403575989,
"acc_norm_stderr": 0.014143970276657569
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7543352601156069,
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"acc_norm": 0.7543352601156069,
"acc_norm_stderr": 0.023176298203992005
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4022346368715084,
"acc_stderr": 0.016399716732847142,
"acc_norm": 0.4022346368715084,
"acc_norm_stderr": 0.016399716732847142
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7516339869281046,
"acc_stderr": 0.02473998135511359,
"acc_norm": 0.7516339869281046,
"acc_norm_stderr": 0.02473998135511359
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7234726688102894,
"acc_stderr": 0.02540383297817961,
"acc_norm": 0.7234726688102894,
"acc_norm_stderr": 0.02540383297817961
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7808641975308642,
"acc_stderr": 0.023016705640262196,
"acc_norm": 0.7808641975308642,
"acc_norm_stderr": 0.023016705640262196
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4895697522816167,
"acc_stderr": 0.012767457253930647,
"acc_norm": 0.4895697522816167,
"acc_norm_stderr": 0.012767457253930647
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7426470588235294,
"acc_stderr": 0.026556519470041513,
"acc_norm": 0.7426470588235294,
"acc_norm_stderr": 0.026556519470041513
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.684640522875817,
"acc_stderr": 0.01879808628488688,
"acc_norm": 0.684640522875817,
"acc_norm_stderr": 0.01879808628488688
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.04461272175910509,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7346938775510204,
"acc_stderr": 0.028263889943784593,
"acc_norm": 0.7346938775510204,
"acc_norm_stderr": 0.028263889943784593
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616913,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616913
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.91,
"acc_stderr": 0.028762349126466125,
"acc_norm": 0.91,
"acc_norm_stderr": 0.028762349126466125
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5843373493975904,
"acc_stderr": 0.03836722176598052,
"acc_norm": 0.5843373493975904,
"acc_norm_stderr": 0.03836722176598052
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03188578017686398,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03188578017686398
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5691554467564259,
"mc1_stderr": 0.01733527247533237,
"mc2": 0.7220886501406486,
"mc2_stderr": 0.014897285217814625
},
"harness|winogrande|5": {
"acc": 0.8271507498026835,
"acc_stderr": 0.010626964529971864
},
"harness|gsm8k|5": {
"acc": 0.6398786959818044,
"acc_stderr": 0.013222559423250485
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
vikcashew/S_VOICES | ---
license: apache-2.0
---
|
robertmyers/genesis | ---
license: bigscience-openrail-m
---
|
thicchips/Alcatraz | ---
license: cc-by-nc-sa-4.0
---
|
croissantllm/croissant_dataset_no_web_data | ---
task_categories:
- translation
- text-generation
- text2text-generation
- fill-mask
language:
- fr
- en
size_categories:
- 10B<n<100B
---
# CroissantLLM: A Truly Bilingual French-English Language Model
## Dataset
Ressources are currently being uploaded !
https://arxiv.org/abs/2402.00786
## Licenses
Data redistributed here is subject to the original license under which it was collected. All license information is detailed in the `Data` section of the Technical report.
## Citation
```
@misc{faysse2024croissantllm,
title={CroissantLLM: A Truly Bilingual French-English Language Model},
author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo},
year={2024},
eprint={2402.00786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
diguinho69/henryemily | ---
license: openrail
---
|
marcones/marcoselementar | ---
license: openrail
---
|
LucasThil/miniwob_plusplus_T5_labeled_1084 | ---
dataset_info:
features:
- name: episodes
dtype: string
- name: target_actions
dtype: string
- name: target_refs
dtype: int64
- name: target_text
dtype: string
splits:
- name: train
num_bytes: 167162436
num_examples: 237193
download_size: 12138892
dataset_size: 167162436
---
# Dataset Card for "miniwob_plusplus_T5_labeled_1084"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-futin__feed-top_vi-71f14a-2175469968 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/feed
eval_info:
task: text_zero_shot_classification
model: facebook/opt-125m
metrics: []
dataset_name: futin/feed
dataset_config: top_vi
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-125m
* Dataset: futin/feed
* Config: top_vi
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
CyberHarem/kishin_sagume_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of kishin_sagume/稀神サグメ/키신사구메 (Touhou)
This is the dataset of kishin_sagume/稀神サグメ/키신사구메 (Touhou), containing 500 images and their tags.
The core tags of this character are `short_hair, single_wing, wings, red_eyes, bow, feathered_wings, grey_hair, red_bow, bangs, white_hair, white_wings, breasts, hair_between_eyes, braid, french_braid`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 680.80 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kishin_sagume_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 379.14 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kishin_sagume_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1175 | 782.84 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kishin_sagume_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 593.66 MiB | [Download](https://huggingface.co/datasets/CyberHarem/kishin_sagume_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1175 | 1.07 GiB | [Download](https://huggingface.co/datasets/CyberHarem/kishin_sagume_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/kishin_sagume_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 | 6 |  |  |  |  |  | 1girl, brooch, cowboy_shot, long_sleeves, looking_at_viewer, medium_breasts, open_jacket, purple_dress, red_bowtie, solo, standing, covering_mouth, blush |
| 1 | 5 |  |  |  |  |  | 1girl, closed_mouth, long_sleeves, looking_at_viewer, open_jacket, purple_dress, simple_background, solo, white_background, brooch, red_bowtie, upper_body, white_jacket |
| 2 | 7 |  |  |  |  |  | 1girl, bowtie, long_sleeves, looking_at_viewer, purple_dress, simple_background, solo, white_background, open_jacket |
| 3 | 11 |  |  |  |  |  | 1girl, long_sleeves, looking_at_viewer, purple_dress, solo, open_jacket, red_bowtie |
| 4 | 9 |  |  |  |  |  | 1girl, full_body, long_sleeves, purple_dress, red_bowtie, solo, boots, brown_footwear, looking_at_viewer, open_jacket, simple_background, white_background, blush, closed_mouth, covering_mouth |
| 5 | 5 |  |  |  |  |  | 1boy, 1girl, blush, hetero, large_breasts, completely_nude, navel, nipples, penis, sex, solo_focus, vaginal, mosaic_censoring, pov, spread_legs, closed_mouth, cowgirl_position, cum_in_pussy, girl_on_top, holding_hands, interlocked_fingers, looking_at_viewer, open_mouth, shiny_skin, smile |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | brooch | cowboy_shot | long_sleeves | looking_at_viewer | medium_breasts | open_jacket | purple_dress | red_bowtie | solo | standing | covering_mouth | blush | closed_mouth | simple_background | white_background | upper_body | white_jacket | bowtie | full_body | boots | brown_footwear | 1boy | hetero | large_breasts | completely_nude | navel | nipples | penis | sex | solo_focus | vaginal | mosaic_censoring | pov | spread_legs | cowgirl_position | cum_in_pussy | girl_on_top | holding_hands | interlocked_fingers | open_mouth | shiny_skin | smile |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:---------|:--------------|:---------------|:--------------------|:-----------------|:--------------|:---------------|:-------------|:-------|:-----------|:-----------------|:--------|:---------------|:--------------------|:-------------------|:-------------|:---------------|:---------|:------------|:--------|:-----------------|:-------|:---------|:----------------|:------------------|:--------|:----------|:--------|:------|:-------------|:----------|:-------------------|:------|:--------------|:-------------------|:---------------|:--------------|:----------------|:----------------------|:-------------|:-------------|:--------|
| 0 | 6 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | X | | X | X | X | X | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 7 |  |  |  |  |  | X | | | X | X | | X | X | | X | | | | | X | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 11 |  |  |  |  |  | X | | | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 9 |  |  |  |  |  | X | | | X | X | | X | X | X | X | | X | X | X | X | X | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 5 | 5 |  |  |  |  |  | X | | | | X | | | | | | | | X | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
dominguesm/canarim-enem2022-tests | ---
dataset_info:
features:
- name: question
dtype: string
- name: response
dtype: string
- name: correct_alternative
dtype: string
- name: prediction
dtype: string
splits:
- name: train
num_bytes: 181150
num_examples: 84
download_size: 130828
dataset_size: 181150
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "canarim-enem2022-tests"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_T_OCR_rices_ns_500 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: prompt
sequence: string
- name: true_label
dtype: string
- name: prediction
dtype: string
splits:
- name: fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text
num_bytes: 290330
num_examples: 500
- name: fewshot_0
num_bytes: 309562
num_examples: 500
download_size: 98393
dataset_size: 599892
---
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_T_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hpprc/mmarco-ja | ---
language:
- ja
license: apache-2.0
pretty_name: MMARCO-Ja
dataset_info:
- config_name: collection
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3818456967
num_examples: 8841823
download_size: 1864051764
dataset_size: 3818456967
- config_name: dataset
features:
- name: anc
dtype: string
- name: pos_ids
sequence: int64
- name: neg_ids
sequence: int64
splits:
- name: train
num_bytes: 342315525
num_examples: 391060
download_size: 287510312
dataset_size: 342315525
configs:
- config_name: collection
data_files:
- split: train
path: collection/train-*
- config_name: dataset
data_files:
- split: train
path: dataset/train-*
---
[mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco)データセットのquery--passageのペアについて、queryをkeyとして重複を削除したデータセットです。
元データ中のエンコーディング周りのミスの修正やNFKC正規化などの前処理を行ってあります。
`dataset` subsetの`pos_ids`および`neg_ids`中のidは、`collection`subsetのインデックス番号に対応しています。
したがって、`collection[pos_id]`のようにアクセスしてもらえれば所望のデータを得ることができます。
ライセンスは元データセットに従います。 |
sparkyfina/clothing_samples | ---
dataset_info:
features:
- name: name
dtype: string
- name: description
dtype: string
- name: ad
dtype: string
splits:
- name: train
num_bytes: 7237
num_examples: 5
download_size: 15054
dataset_size: 7237
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "clothing_samples"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
galsenai/wolof_corpus | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 6976365
num_examples: 52706
download_size: 4792167
dataset_size: 6976365
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
aha-org/coco-2014-instance | ---
dataset_info:
features:
- name: image
dtype: image
- name: annotations
dtype: image
- name: objects
struct:
- name: bbox
sequence:
sequence: float32
- name: categories
sequence:
class_label:
names:
'0': person
'1': bicycle
'2': car
'3': motorcycle
'4': airplane
'5': bus
'6': train
'7': truck
'8': boat
'9': traffic light
'10': fire hydrant
'11': stop sign
'12': parking meter
'13': bench
'14': bird
'15': cat
'16': dog
'17': horse
'18': sheep
'19': cow
'20': elephant
'21': bear
'22': zebra
'23': giraffe
'24': backpack
'25': umbrella
'26': handbag
'27': tie
'28': suitcase
'29': frisbee
'30': skis
'31': snowboard
'32': sports ball
'33': kite
'34': baseball bat
'35': baseball glove
'36': skateboard
'37': surfboard
'38': tennis racket
'39': bottle
'40': wine glass
'41': cup
'42': fork
'43': knife
'44': spoon
'45': bowl
'46': banana
'47': apple
'48': sandwich
'49': orange
'50': broccoli
'51': carrot
'52': hot dog
'53': pizza
'54': donut
'55': cake
'56': chair
'57': couch
'58': potted plant
'59': bed
'60': dining table
'61': toilet
'62': tv
'63': laptop
'64': mouse
'65': remote
'66': keyboard
'67': cell phone
'68': microwave
'69': oven
'70': toaster
'71': sink
'72': refrigerator
'73': book
'74': clock
'75': vase
'76': scissors
'77': teddy bear
'78': hair drier
'79': toothbrush
- name: area
sequence: float32
- name: iscrowd
sequence: bool
- name: height
dtype: int64
- name: width
dtype: int64
- name: date_captured
dtype: string
- name: license
dtype:
class_label:
names:
'0': Attribution-NonCommercial-ShareAlike License
'1': Attribution-NonCommercial License
'2': Attribution-NonCommercial-NoDerivs License
'3': Attribution License
'4': Attribution-ShareAlike License
'5': Attribution-NoDerivs License
'6': No known
'7': United States Government Work
- name: coco_url
dtype: string
- name: flickr_url
dtype: string
splits:
- name: train
num_bytes: 13784509594.309
num_examples: 82081
- name: validation
num_bytes: 6877258108.769
num_examples: 40137
- name: test
num_bytes: 6600156203.075
num_examples: 40775
download_size: 15299492466
dataset_size: 27261923906.153
license: cc-by-4.0
task_categories:
- object-detection
tags:
- coco
size_categories:
- 100K<n<1M
---
# Dataset Card for "coco-2014-instance"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hopee4/RuivinhaV2 | ---
license: openrail
---
|
open-llm-leaderboard/details_Badgids__Gonzo-Chat-7B | ---
pretty_name: Evaluation run of Badgids/Gonzo-Chat-7B
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Badgids/Gonzo-Chat-7B](https://huggingface.co/Badgids/Gonzo-Chat-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_Badgids__Gonzo-Chat-7B\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-02T19:13:21.231650](https://huggingface.co/datasets/open-llm-leaderboard/details_Badgids__Gonzo-Chat-7B/blob/main/results_2024-03-02T19-13-21.231650.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\": 0.6375104210963503,\n\
\ \"acc_stderr\": 0.03246642741268537,\n \"acc_norm\": 0.6414249525962711,\n\
\ \"acc_norm_stderr\": 0.03311218491800464,\n \"mc1\": 0.4320685434516524,\n\
\ \"mc1_stderr\": 0.01734120239498825,\n \"mc2\": 0.6023290144167202,\n\
\ \"mc2_stderr\": 0.015381219035414503\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6237201365187713,\n \"acc_stderr\": 0.014157022555407161,\n\
\ \"acc_norm\": 0.6501706484641638,\n \"acc_norm_stderr\": 0.013936809212158292\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6709818761202948,\n\
\ \"acc_stderr\": 0.004688963175758133,\n \"acc_norm\": 0.8540131447918742,\n\
\ \"acc_norm_stderr\": 0.0035237141526513\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.049756985195624284,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.049756985195624284\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6,\n \
\ \"acc_stderr\": 0.042320736951515885,\n \"acc_norm\": 0.6,\n \
\ \"acc_norm_stderr\": 0.042320736951515885\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.7171052631578947,\n \"acc_stderr\": 0.03665349695640767,\n\
\ \"acc_norm\": 0.7171052631578947,\n \"acc_norm_stderr\": 0.03665349695640767\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.56,\n\
\ \"acc_stderr\": 0.04988876515698589,\n \"acc_norm\": 0.56,\n \
\ \"acc_norm_stderr\": 0.04988876515698589\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322663,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322663\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.51,\n \"acc_stderr\": 0.05024183937956911,\n \"acc_norm\": 0.51,\n\
\ \"acc_norm_stderr\": 0.05024183937956911\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6242774566473989,\n\
\ \"acc_stderr\": 0.036928207672648664,\n \"acc_norm\": 0.6242774566473989,\n\
\ \"acc_norm_stderr\": 0.036928207672648664\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.04512608598542127,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.04512608598542127\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5659574468085107,\n \"acc_stderr\": 0.03240038086792747,\n\
\ \"acc_norm\": 0.5659574468085107,\n \"acc_norm_stderr\": 0.03240038086792747\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.45614035087719296,\n\
\ \"acc_stderr\": 0.046854730419077895,\n \"acc_norm\": 0.45614035087719296,\n\
\ \"acc_norm_stderr\": 0.046854730419077895\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5724137931034483,\n \"acc_stderr\": 0.04122737111370333,\n\
\ \"acc_norm\": 0.5724137931034483,\n \"acc_norm_stderr\": 0.04122737111370333\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4074074074074074,\n \"acc_stderr\": 0.02530590624159063,\n \"\
acc_norm\": 0.4074074074074074,\n \"acc_norm_stderr\": 0.02530590624159063\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.38095238095238093,\n\
\ \"acc_stderr\": 0.043435254289490965,\n \"acc_norm\": 0.38095238095238093,\n\
\ \"acc_norm_stderr\": 0.043435254289490965\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001974,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001974\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7354838709677419,\n\
\ \"acc_stderr\": 0.02509189237885928,\n \"acc_norm\": 0.7354838709677419,\n\
\ \"acc_norm_stderr\": 0.02509189237885928\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5073891625615764,\n \"acc_stderr\": 0.0351760354036101,\n\
\ \"acc_norm\": 0.5073891625615764,\n \"acc_norm_stderr\": 0.0351760354036101\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\": 0.7696969696969697,\n \"acc_stderr\": 0.0328766675860349,\n\
\ \"acc_norm\": 0.7696969696969697,\n \"acc_norm_stderr\": 0.0328766675860349\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7727272727272727,\n \"acc_stderr\": 0.02985751567338642,\n \"\
acc_norm\": 0.7727272727272727,\n \"acc_norm_stderr\": 0.02985751567338642\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.02338193534812144,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.02338193534812144\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6487179487179487,\n \"acc_stderr\": 0.024203665177902803,\n\
\ \"acc_norm\": 0.6487179487179487,\n \"acc_norm_stderr\": 0.024203665177902803\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6638655462184874,\n \"acc_stderr\": 0.030684737115135367,\n\
\ \"acc_norm\": 0.6638655462184874,\n \"acc_norm_stderr\": 0.030684737115135367\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257375,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257375\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8201834862385321,\n \"acc_stderr\": 0.01646534546739152,\n \"\
acc_norm\": 0.8201834862385321,\n \"acc_norm_stderr\": 0.01646534546739152\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5138888888888888,\n \"acc_stderr\": 0.03408655867977749,\n \"\
acc_norm\": 0.5138888888888888,\n \"acc_norm_stderr\": 0.03408655867977749\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.027599174300640763,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.027599174300640763\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7637130801687764,\n \"acc_stderr\": 0.02765215314415927,\n \
\ \"acc_norm\": 0.7637130801687764,\n \"acc_norm_stderr\": 0.02765215314415927\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.031381476375754995,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.031381476375754995\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7557251908396947,\n \"acc_stderr\": 0.037683359597287434,\n\
\ \"acc_norm\": 0.7557251908396947,\n \"acc_norm_stderr\": 0.037683359597287434\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\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\": 0.5178571428571429,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.5178571428571429,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.04058042015646034,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.04058042015646034\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8717948717948718,\n\
\ \"acc_stderr\": 0.02190190511507332,\n \"acc_norm\": 0.8717948717948718,\n\
\ \"acc_norm_stderr\": 0.02190190511507332\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.013890862162876164,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.013890862162876164\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7109826589595376,\n \"acc_stderr\": 0.02440517393578323,\n\
\ \"acc_norm\": 0.7109826589595376,\n \"acc_norm_stderr\": 0.02440517393578323\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3329608938547486,\n\
\ \"acc_stderr\": 0.015761716178397566,\n \"acc_norm\": 0.3329608938547486,\n\
\ \"acc_norm_stderr\": 0.015761716178397566\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7549019607843137,\n \"acc_stderr\": 0.02463004897982477,\n\
\ \"acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.02463004897982477\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.025407197798890162,\n\
\ \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.025407197798890162\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.029790719243829727,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.029790719243829727\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.46088657105606257,\n\
\ \"acc_stderr\": 0.012731102790504519,\n \"acc_norm\": 0.46088657105606257,\n\
\ \"acc_norm_stderr\": 0.012731102790504519\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6323529411764706,\n \"acc_stderr\": 0.02928941340940319,\n\
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.02928941340940319\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6470588235294118,\n \"acc_stderr\": 0.019333142020797164,\n \
\ \"acc_norm\": 0.6470588235294118,\n \"acc_norm_stderr\": 0.019333142020797164\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\": 0.7224489795918367,\n \"acc_stderr\": 0.028666857790274648,\n\
\ \"acc_norm\": 0.7224489795918367,\n \"acc_norm_stderr\": 0.028666857790274648\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.02587064676616914,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.02587064676616914\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5301204819277109,\n\
\ \"acc_stderr\": 0.03885425420866767,\n \"acc_norm\": 0.5301204819277109,\n\
\ \"acc_norm_stderr\": 0.03885425420866767\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8245614035087719,\n \"acc_stderr\": 0.02917088550072766,\n\
\ \"acc_norm\": 0.8245614035087719,\n \"acc_norm_stderr\": 0.02917088550072766\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.4320685434516524,\n\
\ \"mc1_stderr\": 0.01734120239498825,\n \"mc2\": 0.6023290144167202,\n\
\ \"mc2_stderr\": 0.015381219035414503\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7774269928966061,\n \"acc_stderr\": 0.011690933809712662\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.4761182714177407,\n \
\ \"acc_stderr\": 0.01375676583546576\n }\n}\n```"
repo_url: https://huggingface.co/Badgids/Gonzo-Chat-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: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|arc:challenge|25_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|gsm8k|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hellaswag|10_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T19-13-21.231650.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-02T19-13-21.231650.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- '**/details_harness|winogrande|5_2024-03-02T19-13-21.231650.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-02T19-13-21.231650.parquet'
- config_name: results
data_files:
- split: 2024_03_02T19_13_21.231650
path:
- results_2024-03-02T19-13-21.231650.parquet
- split: latest
path:
- results_2024-03-02T19-13-21.231650.parquet
---
# Dataset Card for Evaluation run of Badgids/Gonzo-Chat-7B
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Badgids/Gonzo-Chat-7B](https://huggingface.co/Badgids/Gonzo-Chat-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_Badgids__Gonzo-Chat-7B",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-02T19:13:21.231650](https://huggingface.co/datasets/open-llm-leaderboard/details_Badgids__Gonzo-Chat-7B/blob/main/results_2024-03-02T19-13-21.231650.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": 0.6375104210963503,
"acc_stderr": 0.03246642741268537,
"acc_norm": 0.6414249525962711,
"acc_norm_stderr": 0.03311218491800464,
"mc1": 0.4320685434516524,
"mc1_stderr": 0.01734120239498825,
"mc2": 0.6023290144167202,
"mc2_stderr": 0.015381219035414503
},
"harness|arc:challenge|25": {
"acc": 0.6237201365187713,
"acc_stderr": 0.014157022555407161,
"acc_norm": 0.6501706484641638,
"acc_norm_stderr": 0.013936809212158292
},
"harness|hellaswag|10": {
"acc": 0.6709818761202948,
"acc_stderr": 0.004688963175758133,
"acc_norm": 0.8540131447918742,
"acc_norm_stderr": 0.0035237141526513
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6,
"acc_stderr": 0.042320736951515885,
"acc_norm": 0.6,
"acc_norm_stderr": 0.042320736951515885
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.7171052631578947,
"acc_stderr": 0.03665349695640767,
"acc_norm": 0.7171052631578947,
"acc_norm_stderr": 0.03665349695640767
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6943396226415094,
"acc_stderr": 0.028353298073322663,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322663
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956911,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956911
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6242774566473989,
"acc_stderr": 0.036928207672648664,
"acc_norm": 0.6242774566473989,
"acc_norm_stderr": 0.036928207672648664
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5659574468085107,
"acc_stderr": 0.03240038086792747,
"acc_norm": 0.5659574468085107,
"acc_norm_stderr": 0.03240038086792747
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.45614035087719296,
"acc_stderr": 0.046854730419077895,
"acc_norm": 0.45614035087719296,
"acc_norm_stderr": 0.046854730419077895
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5724137931034483,
"acc_stderr": 0.04122737111370333,
"acc_norm": 0.5724137931034483,
"acc_norm_stderr": 0.04122737111370333
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4074074074074074,
"acc_stderr": 0.02530590624159063,
"acc_norm": 0.4074074074074074,
"acc_norm_stderr": 0.02530590624159063
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.38095238095238093,
"acc_stderr": 0.043435254289490965,
"acc_norm": 0.38095238095238093,
"acc_norm_stderr": 0.043435254289490965
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.39,
"acc_stderr": 0.04902071300001974,
"acc_norm": 0.39,
"acc_norm_stderr": 0.04902071300001974
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7354838709677419,
"acc_stderr": 0.02509189237885928,
"acc_norm": 0.7354838709677419,
"acc_norm_stderr": 0.02509189237885928
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5073891625615764,
"acc_stderr": 0.0351760354036101,
"acc_norm": 0.5073891625615764,
"acc_norm_stderr": 0.0351760354036101
},
"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": 0.7696969696969697,
"acc_stderr": 0.0328766675860349,
"acc_norm": 0.7696969696969697,
"acc_norm_stderr": 0.0328766675860349
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7727272727272727,
"acc_stderr": 0.02985751567338642,
"acc_norm": 0.7727272727272727,
"acc_norm_stderr": 0.02985751567338642
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8808290155440415,
"acc_stderr": 0.02338193534812144,
"acc_norm": 0.8808290155440415,
"acc_norm_stderr": 0.02338193534812144
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6487179487179487,
"acc_stderr": 0.024203665177902803,
"acc_norm": 0.6487179487179487,
"acc_norm_stderr": 0.024203665177902803
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34444444444444444,
"acc_stderr": 0.02897264888484427,
"acc_norm": 0.34444444444444444,
"acc_norm_stderr": 0.02897264888484427
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6638655462184874,
"acc_stderr": 0.030684737115135367,
"acc_norm": 0.6638655462184874,
"acc_norm_stderr": 0.030684737115135367
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.3509933774834437,
"acc_stderr": 0.03896981964257375,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257375
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8201834862385321,
"acc_stderr": 0.01646534546739152,
"acc_norm": 0.8201834862385321,
"acc_norm_stderr": 0.01646534546739152
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5138888888888888,
"acc_stderr": 0.03408655867977749,
"acc_norm": 0.5138888888888888,
"acc_norm_stderr": 0.03408655867977749
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8088235294117647,
"acc_stderr": 0.027599174300640763,
"acc_norm": 0.8088235294117647,
"acc_norm_stderr": 0.027599174300640763
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7637130801687764,
"acc_stderr": 0.02765215314415927,
"acc_norm": 0.7637130801687764,
"acc_norm_stderr": 0.02765215314415927
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.031381476375754995,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.031381476375754995
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7557251908396947,
"acc_stderr": 0.037683359597287434,
"acc_norm": 0.7557251908396947,
"acc_norm_stderr": 0.037683359597287434
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.8055555555555556,
"acc_stderr": 0.038260763248848646,
"acc_norm": 0.8055555555555556,
"acc_norm_stderr": 0.038260763248848646
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7668711656441718,
"acc_stderr": 0.0332201579577674,
"acc_norm": 0.7668711656441718,
"acc_norm_stderr": 0.0332201579577674
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5178571428571429,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.5178571428571429,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.04058042015646034,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.04058042015646034
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8717948717948718,
"acc_stderr": 0.02190190511507332,
"acc_norm": 0.8717948717948718,
"acc_norm_stderr": 0.02190190511507332
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8148148148148148,
"acc_stderr": 0.013890862162876164,
"acc_norm": 0.8148148148148148,
"acc_norm_stderr": 0.013890862162876164
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7109826589595376,
"acc_stderr": 0.02440517393578323,
"acc_norm": 0.7109826589595376,
"acc_norm_stderr": 0.02440517393578323
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3329608938547486,
"acc_stderr": 0.015761716178397566,
"acc_norm": 0.3329608938547486,
"acc_norm_stderr": 0.015761716178397566
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.02463004897982477,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.02463004897982477
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6977491961414791,
"acc_stderr": 0.02608270069539966,
"acc_norm": 0.6977491961414791,
"acc_norm_stderr": 0.02608270069539966
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7037037037037037,
"acc_stderr": 0.025407197798890162,
"acc_norm": 0.7037037037037037,
"acc_norm_stderr": 0.025407197798890162
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.475177304964539,
"acc_stderr": 0.029790719243829727,
"acc_norm": 0.475177304964539,
"acc_norm_stderr": 0.029790719243829727
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.46088657105606257,
"acc_stderr": 0.012731102790504519,
"acc_norm": 0.46088657105606257,
"acc_norm_stderr": 0.012731102790504519
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6323529411764706,
"acc_stderr": 0.02928941340940319,
"acc_norm": 0.6323529411764706,
"acc_norm_stderr": 0.02928941340940319
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6470588235294118,
"acc_stderr": 0.019333142020797164,
"acc_norm": 0.6470588235294118,
"acc_norm_stderr": 0.019333142020797164
},
"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": 0.7224489795918367,
"acc_stderr": 0.028666857790274648,
"acc_norm": 0.7224489795918367,
"acc_norm_stderr": 0.028666857790274648
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.02587064676616914,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.02587064676616914
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5301204819277109,
"acc_stderr": 0.03885425420866767,
"acc_norm": 0.5301204819277109,
"acc_norm_stderr": 0.03885425420866767
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8245614035087719,
"acc_stderr": 0.02917088550072766,
"acc_norm": 0.8245614035087719,
"acc_norm_stderr": 0.02917088550072766
},
"harness|truthfulqa:mc|0": {
"mc1": 0.4320685434516524,
"mc1_stderr": 0.01734120239498825,
"mc2": 0.6023290144167202,
"mc2_stderr": 0.015381219035414503
},
"harness|winogrande|5": {
"acc": 0.7774269928966061,
"acc_stderr": 0.011690933809712662
},
"harness|gsm8k|5": {
"acc": 0.4761182714177407,
"acc_stderr": 0.01375676583546576
}
}
```
## 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
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## Dataset Creation
### Curation Rationale
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### Source Data
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#### Data Collection and Processing
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cis-lmu/GlotStoryBook-MT | ---
license: cc
multilinguality:
- translation
source_datasets:
- cis-lmu/GlotStoryBook
configs:
- config_name: ach
data_files:
- split: test
path: global/ach/*.csv
- config_name: ada
data_files:
- split: test
path: global/ada/*.csv
- config_name: adh
data_files:
- split: test
path: global/adh/*.csv
- config_name: adx
data_files:
- split: test
path: global/adx/*.csv
- config_name: aeb
data_files:
- split: test
path: global/aeb/*.csv
- config_name: af
data_files:
- split: test
path: global/af/*.csv
- config_name: alz
data_files:
- split: test
path: global/alz/*.csv
- config_name: am
data_files:
- split: test
path: global/am/*.csv
- config_name: anu
data_files:
- split: test
path: global/anu/*.csv
- config_name: ar
data_files:
- split: test
path: global/ar/*.csv
- config_name: ar_diacritics
data_files:
- split: test
path: global/ar_diacritics/*.csv
- config_name: as
data_files:
- split: test
path: global/as/*.csv
- config_name: bem
data_files:
- split: test
path: global/bem/*.csv
- config_name: bn
data_files:
- split: test
path: global/bn/*.csv
- config_name: bo
data_files:
- split: test
path: global/bo/*.csv
- config_name: bxk
data_files:
- split: test
path: global/bxk/*.csv
- config_name: ca
data_files:
- split: test
path: global/ca/*.csv
- config_name: cce
data_files:
- split: test
path: global/cce/*.csv
- config_name: ckb
data_files:
- split: test
path: global/ckb/*.csv
- config_name: crk
data_files:
- split: test
path: global/crk/*.csv
- config_name: csw
data_files:
- split: test
path: global/csw/*.csv
- config_name: ctu
data_files:
- split: test
path: global/ctu/*.csv
- config_name: da
data_files:
- split: test
path: global/da/*.csv
- config_name: dag
data_files:
- split: test
path: global/dag/*.csv
- config_name: de
data_files:
- split: test
path: global/de/*.csv
- config_name: dga
data_files:
- split: test
path: global/dga/*.csv
- config_name: din
data_files:
- split: test
path: global/din/*.csv
- config_name: dje
data_files:
- split: test
path: global/dje/*.csv
- config_name: ee
data_files:
- split: test
path: global/ee/*.csv
- config_name: el
data_files:
- split: test
path: global/el/*.csv
- config_name: en
data_files:
- split: test
path: global/en/*.csv
- config_name: eo
data_files:
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path: global/eo/*.csv
- config_name: es
data_files:
- split: test
path: global/es/*.csv
- config_name: fa
data_files:
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path: global/fa/*.csv
- config_name: fa_diacritics
data_files:
- split: test
path: global/fa_diacritics/*.csv
- config_name: fat
data_files:
- split: test
path: global/fat/*.csv
- config_name: ff
data_files:
- split: test
path: global/ff/*.csv
- config_name: fr
data_files:
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path: global/fr/*.csv
- config_name: gaa
data_files:
- split: test
path: global/gaa/*.csv
- config_name: gjn
data_files:
- split: test
path: global/gjn/*.csv
- config_name: gu
data_files:
- split: test
path: global/gu/*.csv
- config_name: gur
data_files:
- split: test
path: global/gur/*.csv
- config_name: guz
data_files:
- split: test
path: global/guz/*.csv
- config_name: gyn
data_files:
- split: test
path: global/gyn/*.csv
- config_name: ha
data_files:
- split: test
path: global/ha/*.csv
- config_name: hbs
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path: global/hbs/*.csv
- config_name: hch
data_files:
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path: global/hch/*.csv
- config_name: hi
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path: global/hi/*.csv
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path: global/ht/*.csv
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path: global/hu/*.csv
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path: global/hus/*.csv
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path: global/hz/*.csv
- config_name: id
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path: global/id/*.csv
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- config_name: ja
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path: global/ja/*.csv
- config_name: jam
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path: global/jam/*.csv
- config_name: kam
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path: global/kam/*.csv
- config_name: kdj
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path: global/kdj/*.csv
- config_name: keo
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path: global/keo/*.csv
- config_name: khg
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- config_name: ki
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- config_name: kj
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- config_name: kln
data_files:
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path: global/kln/*.csv
- config_name: km
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- config_name: kmr
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path: global/kmr/*.csv
- config_name: kn
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path: global/kn/*.csv
- config_name: ko
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path: global/ko/*.csv
- config_name: kok
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- config_name: koo
data_files:
- split: test
path: global/koo/*.csv
- config_name: kpz
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- split: test
path: global/kpz/*.csv
- config_name: kqn
data_files:
- split: test
path: global/kqn/*.csv
- config_name: kr
data_files:
- split: test
path: global/kr/*.csv
- config_name: kri
data_files:
- split: test
path: global/kri/*.csv
- config_name: kru
data_files:
- split: test
path: global/kru/*.csv
- config_name: ktz
data_files:
- split: test
path: global/ktz/*.csv
- config_name: kwn
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- split: test
path: global/kwn/*.csv
- config_name: la
data_files:
- split: test
path: global/la/*.csv
- config_name: laj
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- split: test
path: global/laj/*.csv
- config_name: lg
data_files:
- split: test
path: global/lg/*.csv
- config_name: lgg
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- split: test
path: global/lgg/*.csv
- config_name: lgg_official
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- split: test
path: global/lgg_official/*.csv
- config_name: lko
data_files:
- split: test
path: global/lko/*.csv
- config_name: ln
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path: global/ln/*.csv
- config_name: loz
data_files:
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path: global/loz/*.csv
- config_name: loz_na
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- split: test
path: global/loz_na/*.csv
- config_name: loz_zm
data_files:
- split: test
path: global/loz_zm/*.csv
- config_name: lsm
data_files:
- split: test
path: global/lsm/*.csv
- config_name: lt
data_files:
- split: test
path: global/lt/*.csv
- config_name: luc
data_files:
- split: test
path: global/luc/*.csv
- config_name: lue
data_files:
- split: test
path: global/lue/*.csv
- config_name: lun
data_files:
- split: test
path: global/lun/*.csv
- config_name: luo
data_files:
- split: test
path: global/luo/*.csv
- config_name: lwg
data_files:
- split: test
path: global/lwg/*.csv
- config_name: mas
data_files:
- split: test
path: global/mas/*.csv
- config_name: mat
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- split: test
path: global/mat/*.csv
- config_name: maz
data_files:
- split: test
path: global/maz/*.csv
- config_name: mer
data_files:
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path: global/mer/*.csv
- config_name: mfe
data_files:
- split: test
path: global/mfe/*.csv
- config_name: mg
data_files:
- split: test
path: global/mg/*.csv
- config_name: mhi
data_files:
- split: test
path: global/mhi/*.csv
- config_name: mhw
data_files:
- split: test
path: global/mhw/*.csv
- config_name: miu
data_files:
- split: test
path: global/miu/*.csv
- config_name: ml
data_files:
- split: test
path: global/ml/*.csv
- config_name: mmc
data_files:
- split: test
path: global/mmc/*.csv
- config_name: mnw
data_files:
- split: test
path: global/mnw/*.csv
- config_name: mqu
data_files:
- split: test
path: global/mqu/*.csv
- config_name: mr
data_files:
- split: test
path: global/mr/*.csv
- config_name: ms
data_files:
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path: global/ms/*.csv
- config_name: my
data_files:
- split: test
path: global/my/*.csv
- config_name: myx
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- split: test
path: global/myx/*.csv
- config_name: naq
data_files:
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path: global/naq/*.csv
- config_name: nb
data_files:
- split: test
path: global/nb/*.csv
- config_name: nch
data_files:
- split: test
path: global/nch/*.csv
- config_name: ne
data_files:
- split: test
path: global/ne/*.csv
- config_name: ng
data_files:
- split: test
path: global/ng/*.csv
- config_name: nhe
data_files:
- split: test
path: global/nhe/*.csv
- config_name: nhw
data_files:
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path: global/nhw/*.csv
- config_name: nl
data_files:
- split: test
path: global/nl/*.csv
- config_name: nle
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path: global/nle/*.csv
- config_name: nn
data_files:
- split: test
path: global/nn/*.csv
- config_name: 'no'
data_files:
- split: test
path: global/no/*.csv
- config_name: no_ipa
data_files:
- split: test
path: global/no_ipa/*.csv
- config_name: nr
data_files:
- split: test
path: global/nr/*.csv
- config_name: nso
data_files:
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path: global/nso/*.csv
- config_name: nuj
data_files:
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path: global/nuj/*.csv
- config_name: ny
data_files:
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path: global/ny/*.csv
- config_name: nyn
data_files:
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path: global/nyn/*.csv
- config_name: nyu
data_files:
- split: test
path: global/nyu/*.csv
- config_name: nzi
data_files:
- split: test
path: global/nzi/*.csv
- config_name: ocu
data_files:
- split: test
path: global/ocu/*.csv
- config_name: old
data_files:
- split: test
path: global/old/*.csv
- config_name: om
data_files:
- split: test
path: global/om/*.csv
- config_name: or
data_files:
- split: test
path: global/or/*.csv
- config_name: pa
data_files:
- split: test
path: global/pa/*.csv
- config_name: pa_shahmukhi
data_files:
- split: test
path: global/pa_shahmukhi/*.csv
- config_name: pcm
data_files:
- split: test
path: global/pcm/*.csv
- config_name: pl
data_files:
- split: test
path: global/pl/*.csv
- config_name: pmq
data_files:
- split: test
path: global/pmq/*.csv
- config_name: prs
data_files:
- split: test
path: global/prs/*.csv
- config_name: prs_diacritics
data_files:
- split: test
path: global/prs_diacritics/*.csv
- config_name: ps
data_files:
- split: test
path: global/ps/*.csv
- config_name: pt
data_files:
- split: test
path: global/pt/*.csv
- config_name: rki
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- split: test
path: global/rki/*.csv
- config_name: ro
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path: global/ro/*.csv
- config_name: ru
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- split: test
path: global/ru/*.csv
- config_name: rw
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path: global/rw/*.csv
- config_name: sa
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path: global/sa/*.csv
- config_name: saq
data_files:
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path: global/saq/*.csv
- config_name: sck
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path: global/sck/*.csv
- config_name: se
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path: global/se/*.csv
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path: global/sg/*.csv
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path: global/so/*.csv
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path: global/sq/*.csv
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path: global/sr/*.csv
- config_name: ss
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path: global/ss/*.csv
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path: global/st/*.csv
- config_name: sv
data_files:
- split: test
path: global/sv/*.csv
- config_name: sw
data_files:
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path: global/sw/*.csv
- config_name: ta
data_files:
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path: global/ta/*.csv
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path: global/te/*.csv
- config_name: teo
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path: global/teo/*.csv
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path: global/tet/*.csv
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path: global/ti/*.csv
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path: global/tl/*.csv
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path: global/toh/*.csv
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path: global/toi/*.csv
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path: global/tr/*.csv
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path: global/ts/*.csv
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path: global/tsc/*.csv
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path: global/ttj/*.csv
- config_name: tum
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path: global/tum/*.csv
- config_name: tuv
data_files:
- split: test
path: global/tuv/*.csv
- config_name: tw_akua
data_files:
- split: test
path: global/tw_akua/*.csv
- config_name: tw_asan
data_files:
- split: test
path: global/tw_asan/*.csv
- config_name: uk
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path: global/uk/*.csv
- config_name: ur
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path: global/ur/*.csv
- config_name: ve
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path: global/ve/*.csv
- config_name: vi
data_files:
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path: global/vi/*.csv
- config_name: xh
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path: global/xh/*.csv
- config_name: xog
data_files:
- split: test
path: global/xog/*.csv
- config_name: xsm
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- split: test
path: global/xsm/*.csv
- config_name: yo
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path: global/yo/*.csv
- config_name: yua
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- split: test
path: global/yua/*.csv
- config_name: yue
data_files:
- split: test
path: global/yue/*.csv
- config_name: zh
data_files:
- split: test
path: global/zh/*.csv
- config_name: zh_pinyin
data_files:
- split: test
path: global/zh_pinyin/*.csv
- config_name: zne
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path: global/zne/*.csv
- config_name: zu
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pretty_name: GlotStoryBook-MT
task_categories:
- translation
- text-generation
- text2text-generation
---
## Dataset Description
Machine Translation (MT) version of Story Books for 180 ISO-639-3 codes (190 variety of languages).
Original dataset: [cis-lmu/GlotStoryBook](https://huggingface.co/datasets/cis-lmu/GlotStoryBook).
This dataset consisted of 4 publishers:
1. asp: [African Storybook](https://africanstorybook.org)
2. pb: [Pratham Books](https://prathambooks.org/)
3. lcb: [Little Cree Books](http://littlecreebooks.com/)
4. lida: [LIDA Stories](https://lidastories.net/)
- **GitHub Repository:** [github](https://github.com/cisnlp/GlotStoryBook)
- **Paper:** [paper](https://arxiv.org/abs/2310.16248)
- **Point of Contact:** amir@cis.lmu.de
## Usage (HF Loader)
```python
from datasets import load_dataset
dataset = load_dataset('cis-lmu/GlotStoryBook-MT', 'en')
print(dataset['test'][0]) # First row data for en versus other languages
```
## Download
If you are not a fan of the HF dataloader, download it directly:
First, check out the directory of language of your interest (for example, 'en'):
https://huggingface.co/datasets/cis-lmu/GlotStoryBook-MT/tree/main/global/en
Then, download the pair of your interest (en-fa here):
```python
! wget https://huggingface.co/datasets/cis-lmu/GlotStoryBook-MT/blob/main/global/en/en-fa.csv
```
You can also clone the whole directory:
```python
! git clone https://huggingface.co/datasets/cis-lmu/GlotStoryBook-MT
```
## Format
Each sentence is included in a list because for some texts in the source and target languages, two versions of translations exist. However, these lists are converted to strings in this dataset.
You can bring them back to be lists again.
For example:
```python
from datasets import load_dataset
from ast import literal_eval
data_en = load_dataset("cis-lmu/GlotStoryBook-MT", 'en')
# convert the datasets object to pandas (optional)
df_en = data_en['test'].to_pandas()
# you can also use eval function for each entry.
df_en['source_sentences'] = df_en['source_sentences'].apply(literal_eval)
df_en['target_sentences'] = df_en['target_sentences'].apply(literal_eval)
df_en['source_files'] = df_en['source_files'].apply(literal_eval)
df_en['target_files'] = df_en['target_files'].apply(literal_eval)
df_en.head()
```
## License and Copyright
We do not own any of the text from which these data has been extracted.
All the files are collected from the repository located at https://github.com/global-asp/.
The source repository for each text and file is stored in the original dataset: [cis-lmu/GlotStoryBook](https://huggingface.co/datasets/cis-lmu/GlotStoryBook).
Each file in the dataset is associated with one license from the CC family.
The licenses include 'CC BY', 'CC BY-NC', 'CC BY-NC-SA', 'CC-BY', 'CC-BY-NC', and 'Public Domain'.
We also license the code, actual packaging and the metadata of these data under the cc0-1.0.
## Citation
If you use any part of this code and data in your research, please cite it (along with https://github.com/global-asp/) using the following BibTeX entry.
This work is part of the [GlotLID](https://github.com/cisnlp/GlotLID) project.
```
@inproceedings{
kargaran2023glotlid,
title={{GlotLID}: Language Identification for Low-Resource Languages},
author={Kargaran, Amir Hossein and Imani, Ayyoob and Yvon, Fran{\c{c}}ois and Sch{\"u}tze, Hinrich},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=dl4e3EBz5j}
}
``` |
Shekswess/gemma_medquad_instruct_dataset | ---
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- question-answering
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 48114257
num_examples: 16359
download_size: 17948500
dataset_size: 48114257
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- medical
---
Dataset made for instruction supervised finetuning of Gemma LLMs based on the Medquad dataset:
- Medquad dataset (https://www.kaggle.com/datasets/jpmiller/layoutlm)
## Medquad
MedQuAD is a comprehensive collection consisting of 47,457 medical question-answer pairs compiled from 12 authoritative sources within the National Institutes of Health (NIH), including domains like cancer.gov, niddk.nih.gov, GARD, and MedlinePlus Health Topics. These question-answer pairs span 37 distinct question types, covering a wide spectrum of medical subjects, including diseases, drugs, and medical procedures. The dataset features additional annotations provided in XML files, facilitating various Information Retrieval (IR) and Natural Language Processing (NLP) tasks. These annotations encompass crucial information such as question type, question focus, synonyms, Unique Identifier (CUI) from the Unified Medical Language System (UMLS), and Semantic Type. Moreover, the dataset includes categorization of question focuses into three main categories: Disease, Drug, or Other, with the exception of collections from MedlinePlus, which exclusively focus on diseases. |
DTU54DL/libri_augmented_test_set | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: text
dtype: string
- name: speaker_id
dtype: int64
- name: chapter_id
dtype: int64
- name: id
dtype: string
splits:
- name: test
num_bytes: 623397698.5
num_examples: 2620
download_size: 610524259
dataset_size: 623397698.5
---
# Dataset Card for "libri_augmented_test_set"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
michaelthwan/wiki_qa_bart_1000row | ---
license: mit
---
|
autoevaluate/autoeval-staging-eval-project-ml6team__cnn_dailymail_nl-bfaf23ee-12505670 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- ml6team/cnn_dailymail_nl
eval_info:
task: summarization
model: yhavinga/long-t5-tglobal-small-dutch-cnn
metrics: []
dataset_name: ml6team/cnn_dailymail_nl
dataset_config: default
dataset_split: test
col_mapping:
text: article
target: highlights
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: yhavinga/long-t5-tglobal-small-dutch-cnn
* Dataset: ml6team/cnn_dailymail_nl
* 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 [@yhavinga](https://huggingface.co/yhavinga) for evaluating this model. |
gamy0315/mixatis_clean | ---
dataset_info:
features:
- name: token
sequence: string
- name: tag
sequence: string
- name: intent
sequence: string
splits:
- name: train
num_bytes: 6266669
num_examples: 13162
- name: validation
num_bytes: 334004
num_examples: 759
- name: test
num_bytes: 341726
num_examples: 828
download_size: 701391
dataset_size: 6942399
---
# Dataset Card for "mixatis_clean"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
izardy/malaysia-elelong | ---
dataset_name: elelong
description: Data source from https://kehakiman.gov.my/
language:
- en
- ms
tags:
- malaysia
- law
- judgement
---
#### This data repo consist only 1 data file
|No| Filename | File Description |
|--|----------|------------------|
|1 | train.csv | Processed data from the scraped pdf |
#### Links
- https://github.com/mesolitica/malaysian-dataset/tree/master/crawl/kehakiman.gov.my/elelong
|
autoevaluate/autoeval-eval-futin__guess-en-6f8c6a-2012266596 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- futin/guess
eval_info:
task: text_zero_shot_classification
model: facebook/opt-13b
metrics: []
dataset_name: futin/guess
dataset_config: en
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: facebook/opt-13b
* Dataset: futin/guess
* Config: en
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@futin](https://huggingface.co/futin) for evaluating this model. |
andersonbcdefg/synthetic_nli_v3 | ---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
splits:
- name: train
num_bytes: 91718717.0
num_examples: 245000
download_size: 56870372
dataset_size: 91718717.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Fantazy/CHINESE-GIRL-V1.0 | ---
license: openrail
---
|
myradeng/diffusion_db_dedup_from50k_train_v2 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: seed
dtype: uint32
- name: step
dtype: uint16
- name: cfg
dtype: float32
- name: sampler
dtype: string
- name: width
dtype: uint16
- name: height
dtype: uint16
- name: user_name
dtype: string
- name: timestamp
dtype: timestamp[ns, tz=UTC]
- name: image_nsfw
dtype: float32
- name: prompt_nsfw
dtype: float32
- name: __index_level_0__
dtype: int64
- name: image_path
dtype: string
- name: image
dtype: image
splits:
- name: train
num_bytes: 20635424524.879997
num_examples: 34716
download_size: 21150988303
dataset_size: 20635424524.879997
---
# Dataset Card for "diffusion_db_dedup_from50k_train_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
xcelr8/test | ---
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 822310
num_examples: 542
download_size: 289324
dataset_size: 822310
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
YoonSeul/legal-GPT-BARD-val_v3 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1359457
num_examples: 652
download_size: 689201
dataset_size: 1359457
---
# Dataset Card for "legal-GPT-BARD-val_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
hanmaegeo/glue_text_to_text | ---
dataset_info:
features:
- name: input
dtype: string
- name: target
dtype: string
splits:
- name: validation
num_bytes: 12895402
num_examples: 69711
- name: test
num_bytes: 68584768
num_examples: 425205
download_size: 42875561
dataset_size: 81480170
---
# Dataset Card for "glue_text_to_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mc_taco | ---
annotations_creators:
- crowdsourced
- machine-generated
language_creators:
- crowdsourced
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
paperswithcode_id: mc-taco
pretty_name: MC-TACO
dataset_info:
features:
- name: sentence
dtype: string
- name: question
dtype: string
- name: answer
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: category
dtype:
class_label:
names:
'0': Event Duration
'1': Event Ordering
'2': Frequency
'3': Typical Time
'4': Stationarity
config_name: plain_text
splits:
- name: test
num_bytes: 1785553
num_examples: 9442
- name: validation
num_bytes: 713023
num_examples: 3783
download_size: 2385137
dataset_size: 2498576
---
# Dataset Card for MC-TACO
## 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:** [MC-TACO](https://cogcomp.seas.upenn.edu/page/resource_view/125)
- **Repository:** [Github repository](https://github.com/CogComp/MCTACO)
- **Paper:** ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://arxiv.org/abs/1909.03065)
- **Leaderboard:** [AI2 Leaderboard](https://leaderboard.allenai.org/mctaco)
### Dataset Summary
MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible.
### Supported Tasks and Leaderboards
The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no").
Performance is measured using two metrics:
- Exact Match -- the average number of questions for which all the candidate answers are predicted correctly.
- F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example looks like this:
```
{
"sentence": "However, more recently, it has been suggested that it may date from earlier than Abdalonymus' death.",
"question": "How often did Abdalonymus die?",
"answer": "every two years",
"label": "no",
"category": "Frequency",
}
```
### Data Fields
All fields are strings:
- `sentence`: a sentence (or context) on which the question is based
- `question`: a question querying some temporal commonsense knowledge
- `answer`: a potential answer to the question (all lowercased)
- `label`: whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise
- `category`: the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time")
### Data Splits
The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers.
From the original repository:
*Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.*
## Dataset Creation
### Curation Rationale
MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems.
### Source Data
From the original paper:
*The context sentences are randomly selected from [MultiRC](https://www.aclweb.org/anthology/N18-1023/) (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).*
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
From the original paper:
*To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.*
#### Annotation process
The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the [paper](https://arxiv.org/abs/1909.03065): question generation, question verification, candidate answer expansion and answer labeling.
#### Who are the annotators?
Paid crowdsourcers.
### 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
Unknwon
### Citation Information
```
@inproceedings{ZKNR19,
author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
booktitle = {EMNLP},
year = {2019},
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
LLMs/Alpaca-ShareGPT | ---
license: apache-2.0
---
|
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