datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
CyberHarem/ryu_lion_isitwrongtotrytopickupgirlsinadungeon | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of ryu_lion (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka)
This is the dataset of ryu_lion (Dungeon ni Deai wo Motomeru no wa Machigatteiru no Darou ka), containing 174 images and their tags.
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)).
|
argilla/banking_sentiment_zs_gpt3 | ---
dataset_info:
features:
- name: text
dtype: 'null'
- name: inputs
struct:
- name: explanation
dtype: string
- name: text
dtype: string
- name: prediction
list:
- name: label
dtype: string
- name: score
dtype: float64
- name: prediction_agent
dtype: string
- name: annotation
dtype: string
- name: annotation_agent
dtype: string
- name: multi_label
dtype: bool
- name: explanation
dtype: 'null'
- name: id
dtype: string
- name: metadata
dtype: 'null'
- name: status
dtype: string
- name: event_timestamp
dtype: 'null'
- name: metrics
struct:
- name: text_length
dtype: int64
splits:
- name: train
num_bytes: 11113
num_examples: 36
download_size: 10768
dataset_size: 11113
---
# Dataset Card for "banking_sentiment_zs_gpt3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
eagle0504/youthless-homeless-shelter-web-scrape-dataset-large | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 130725
num_examples: 100
- name: test
num_bytes: 37300
num_examples: 31
download_size: 70201
dataset_size: 168025
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-html-4000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 641964
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gennroses/mcbob | ---
license: openrail
---
|
BNNT/PatentMatch | ---
license: apache-2.0
---
|
MarkGG/Romance-cleaned-3 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 3369959.5092553934
num_examples: 6466
- name: validation
num_bytes: 374729.4907446068
num_examples: 719
download_size: 2300275
dataset_size: 3744689.0
---
# Dataset Card for "Romance-cleaned-3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
terrakom/test_embbedings | ---
license: mit
---
|
AhmadMustafa/Urdu-Instruct-News-Article-Generation | ---
language:
- ur
size_categories:
- 100K<n<1M
task_categories:
- text-generation
- text2text-generation
pretty_name: Instruct News Article Generation Urdu
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: template_id
dtype: int64
- name: template_lang
sequence: string
splits:
- name: train
num_bytes: 257489376
num_examples: 100674
- name: test
num_bytes: 28303926
num_examples: 11187
download_size: 121675545
dataset_size: 285793302
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Dataset Card for "Urdu-Instruct-News-Article-Generation"
This Dataset is converted from the [original dataset](https://data.mendeley.com/datasets/834vsxnb99/3) by Khalid Hussain, Nimra Mughal, Irfan Ali, Saif Hassan, Sher Muhammad Daudpota.
## Task:
Generate the News Article from the given headline.
## Split Size:
- train: 100674
- test: 11187
## Prompt Template (In Urdu):
Random.choice b.w these 2. The First template is **template_id** 1 and the second template is **template_id** 2 in the dataset.
```
[ "اس دی گی ایک خبر سے متعلق ایک مضمون لکھیں۔
خبر: {}",
"یہ خبر جو {} سے تعلق رکھتی ہے، اس پر ایک مضمون لکھیں۔
خبر: {}"
]
```
<b>Translation</b>:
```
1. Write an article from the given news.
news: {}
2. Given the news belonging to category {}, write an article on it.
news: {}
```
## Completion Template (In Urdu)
```
جی ضرور، یہ رہا آپ کی خبر سے متعلق ایک مضمون:
{}
```
<b>Translation</b>:
```
Sure, here is the article related to the given news
{}
``` |
Carlosgg14/HISSOKA | ---
license: openrail
---
|
autoevaluate/autoeval-staging-eval-samsum-samsum-e82d51-15636155 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- samsum
eval_info:
task: summarization
model: SamuelAllen123/t5-efficient-large-nl36_fine_tuned_for_sum
metrics: ['mse', 'mae']
dataset_name: samsum
dataset_config: samsum
dataset_split: validation
col_mapping:
text: dialogue
target: summary
---
# 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: SamuelAllen123/t5-efficient-large-nl36_fine_tuned_for_sum
* Dataset: samsum
* Config: samsum
* Split: validation
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@samuelallen123](https://huggingface.co/samuelallen123) for evaluating this model. |
arjunvb/drivetrack | ---
license: mit
---
|
daju666/butterfly_flower | ---
license: artistic-2.0
---
|
jahb57/gpt2_embeddings_BATCH_10 | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence: float32
splits:
- name: train
num_bytes: 18646055124
num_examples: 100000
download_size: 18694684530
dataset_size: 18646055124
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CATIE-AQ/wikiner_fr_prompt_ner | ---
language:
- fr
license:
- unknown
size_categories:
- 1M<n<10M
task_categories:
- token-classification
tags:
- ner
- DFP
- french prompts
annotations_creators:
- found
language_creators:
- found
multilinguality:
- monolingual
source_datasets:
- wikiner
---
# wikiner_fr_prompt_ner
## Summary
**wikiner_fr_prompt_ner** is a subset of the [**Dataset of French Prompts (DFP)**](https://huggingface.co/datasets/CATIE-AQ/DFP).
It contains **2,534,322** rows that can be used for a name entity recognition task.
The original data (without prompts) comes from the dataset [wikiner](https://huggingface.co/datasets/Jean-Baptiste/wikiner_fr) by Nothman et al. where only the French part has been kept.
A list of prompts (see below) was then applied in order to build the input and target columns and thus obtain the same format as the [xP3](https://huggingface.co/datasets/bigscience/xP3) dataset by Muennighoff et al.
## Prompts used
### List
21 prompts were created for this dataset. The logic applied consists in proposing prompts in the indicative tense, in the form of tutoiement and in the form of vouvoiement.
```
'Extraire les entités nommées du texte suivant : '+text,
'Extrais les entitées nommées du texte suivant : '+text,
'Extrayez les entitées nommées du texte suivant : '+text,
'Isoler les entitées nommées du texte suivant : '+text,
'Isole les entitées nommées du texte suivant : '+text,
'Isolez les entitées nommées du texte suivant : '+text,
'Dégager des entitées nommées dans le texte : '+text,
'Dégage des entitées nommées dans le texte : '+text,
'Dégagez des entitées nommées dans le texte : '+text,
'Générer des entitées nommées issues du texte suivant : '+text,
'Génère des entitées nommées issues du texte suivant : '+text,
'Générez des entitées nommées issues du texte suivant : '+text,
'Trouver les entitées nommées du texte : '+text,
'Trouve les entitées nommées du texte : '+text,
'Trouvez les entitées nommées du texte : '+text,
'Repérer les entitées nommées présentes dans le texte suivant : '+text,
'Repère les entitées nommées présentes dans le texte suivant : '+text,
'Repérez les entitées nommées présentes dans le texte suivant : '+text,
'Indiquer les entitées nommées du texte :'+text,
'Indique les entitées nommées du texte : '+text,
'Indiquez les entitées nommées du texte : '+text
```
### Features used in the prompts
In the prompt list above, `text` and `targets` have been constructed from:
```
wikiner_fr = load_dataset('Jean-Baptiste/wikiner_fr')
wikiner_fr['train']['tokens'] = list(map(lambda i: ' '.join(wikiner_fr['train']['tokens'][i]), range(len(wikiner_fr['train']['tokens']))))
wikiner_fr['train']['ner_tags'] = list(map(lambda x: x.replace("[","").replace("]","").replace('0','O').replace('1','LOC').replace('2','PER').replace('3','O').replace('4','ORG'), map(str, wikiner_fr['train']['ner_tags'])))
```
# Splits
- `train` with 2,534,322 samples
- no `valid` split
- no `test` split
# How to use?
```
from datasets import load_dataset
dataset = load_dataset("CATIE-AQ/wikiner_fr_prompt_ner")
```
# Citation
## Original data
> @article{NOTHMAN2013151,
title = {Learning multilingual named entity recognition from Wikipedia},
journal = {Artificial Intelligence},
volume = {194},
pages = {151-175},
year = {2013},
note = {Artificial Intelligence, Wikipedia and Semi-Structured Resources},
issn = {0004-3702},
doi = {https://doi.org/10.1016/j.artint.2012.03.006},
url = {https://www.sciencedirect.com/science/article/pii/S0004370212000276},
author = {Joel Nothman and Nicky Ringland and Will Radford and Tara Murphy and James R. Curran},
}
## This Dataset
> @misc {centre_aquitain_des_technologies_de_l'information_et_electroniques_2023,
author = { {Centre Aquitain des Technologies de l'Information et Electroniques} },
title = { DFP (Revision 1d24c09) },
year = 2023,
url = { https://huggingface.co/datasets/CATIE-AQ/DFP },
doi = { 10.57967/hf/1200 },
publisher = { Hugging Face }
}
## License
Unknow |
BangumiBase/jigokushoujoyoinotogi | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Jigoku Shoujo - Yoi No Togi
This is the image base of bangumi Jigoku Shoujo - Yoi no Togi, we detected 53 characters, 2595 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 232 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 25 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
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| noise | 59 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
open-llm-leaderboard/details_jilp00__SOLAR-10.7B-tutored | ---
pretty_name: Evaluation run of jilp00/SOLAR-10.7B-tutored
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [jilp00/SOLAR-10.7B-tutored](https://huggingface.co/jilp00/SOLAR-10.7B-tutored)\
\ 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_jilp00__SOLAR-10.7B-tutored\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-05T00:34:47.267405](https://huggingface.co/datasets/open-llm-leaderboard/details_jilp00__SOLAR-10.7B-tutored/blob/main/results_2024-01-05T00-34-47.267405.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\": 0.6412260054447222,\n\
\ \"acc_stderr\": 0.03159304918019915,\n \"acc_norm\": 0.6533509528093954,\n\
\ \"acc_norm_stderr\": 0.03245293404969856,\n \"mc1\": 0.3561811505507956,\n\
\ \"mc1_stderr\": 0.016763790728446335,\n \"mc2\": 0.5512836940802899,\n\
\ \"mc2_stderr\": 0.01538829164182792\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5853242320819113,\n \"acc_stderr\": 0.014397070564409174,\n\
\ \"acc_norm\": 0.6228668941979523,\n \"acc_norm_stderr\": 0.014163366896192593\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6251742680740888,\n\
\ \"acc_stderr\": 0.004830885704380079,\n \"acc_norm\": 0.8224457279426409,\n\
\ \"acc_norm_stderr\": 0.00381356105715034\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.562962962962963,\n\
\ \"acc_stderr\": 0.04284958639753401,\n \"acc_norm\": 0.562962962962963,\n\
\ \"acc_norm_stderr\": 0.04284958639753401\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\": 0.7,\n\
\ \"acc_stderr\": 0.046056618647183814,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.046056618647183814\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6981132075471698,\n \"acc_stderr\": 0.028254200344438665,\n\
\ \"acc_norm\": 0.6981132075471698,\n \"acc_norm_stderr\": 0.028254200344438665\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7361111111111112,\n\
\ \"acc_stderr\": 0.03685651095897532,\n \"acc_norm\": 0.7361111111111112,\n\
\ \"acc_norm_stderr\": 0.03685651095897532\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\"\
: 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.0479372485441102,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.0479372485441102\n },\n\
\ \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4019607843137255,\n \"acc_stderr\": 0.04878608714466996,\n\
\ \"acc_norm\": 0.4019607843137255,\n \"acc_norm_stderr\": 0.04878608714466996\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\": 0.5787234042553191,\n \"acc_stderr\": 0.03227834510146268,\n\
\ \"acc_norm\": 0.5787234042553191,\n \"acc_norm_stderr\": 0.03227834510146268\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.39473684210526316,\n\
\ \"acc_stderr\": 0.045981880578165414,\n \"acc_norm\": 0.39473684210526316,\n\
\ \"acc_norm_stderr\": 0.045981880578165414\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5241379310344828,\n \"acc_stderr\": 0.0416180850350153,\n\
\ \"acc_norm\": 0.5241379310344828,\n \"acc_norm_stderr\": 0.0416180850350153\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4894179894179894,\n \"acc_stderr\": 0.025745542276045478,\n \"\
acc_norm\": 0.4894179894179894,\n \"acc_norm_stderr\": 0.025745542276045478\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4126984126984127,\n\
\ \"acc_stderr\": 0.04403438954768176,\n \"acc_norm\": 0.4126984126984127,\n\
\ \"acc_norm_stderr\": 0.04403438954768176\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\"\
: 0.7870967741935484,\n \"acc_stderr\": 0.023287665127268542,\n \"\
acc_norm\": 0.7870967741935484,\n \"acc_norm_stderr\": 0.023287665127268542\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.045126085985421296,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.045126085985421296\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\
acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6871794871794872,\n \"acc_stderr\": 0.023507579020645344,\n\
\ \"acc_norm\": 0.6871794871794872,\n \"acc_norm_stderr\": 0.023507579020645344\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34074074074074073,\n \"acc_stderr\": 0.028897748741131143,\n \
\ \"acc_norm\": 0.34074074074074073,\n \"acc_norm_stderr\": 0.028897748741131143\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\"\
: 0.3509933774834437,\n \"acc_stderr\": 0.03896981964257374,\n \"\
acc_norm\": 0.3509933774834437,\n \"acc_norm_stderr\": 0.03896981964257374\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\"\
: 0.5787037037037037,\n \"acc_stderr\": 0.03367462138896078,\n \"\
acc_norm\": 0.5787037037037037,\n \"acc_norm_stderr\": 0.03367462138896078\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8382352941176471,\n \"acc_stderr\": 0.025845017986926917,\n \"\
acc_norm\": 0.8382352941176471,\n \"acc_norm_stderr\": 0.025845017986926917\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8523206751054853,\n \"acc_stderr\": 0.0230943295825957,\n \
\ \"acc_norm\": 0.8523206751054853,\n \"acc_norm_stderr\": 0.0230943295825957\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.726457399103139,\n\
\ \"acc_stderr\": 0.029918586707798827,\n \"acc_norm\": 0.726457399103139,\n\
\ \"acc_norm_stderr\": 0.029918586707798827\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082396,\n\
\ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082396\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8181818181818182,\n \"acc_stderr\": 0.035208939510976534,\n \"\
acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.035208939510976534\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7962962962962963,\n\
\ \"acc_stderr\": 0.03893542518824847,\n \"acc_norm\": 0.7962962962962963,\n\
\ \"acc_norm_stderr\": 0.03893542518824847\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.033519538795212696,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.033519538795212696\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\
\ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\
\ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8058252427184466,\n \"acc_stderr\": 0.03916667762822584,\n\
\ \"acc_norm\": 0.8058252427184466,\n \"acc_norm_stderr\": 0.03916667762822584\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8675213675213675,\n\
\ \"acc_stderr\": 0.022209309073165616,\n \"acc_norm\": 0.8675213675213675,\n\
\ \"acc_norm_stderr\": 0.022209309073165616\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\": 0.8084291187739464,\n\
\ \"acc_stderr\": 0.014072859310451949,\n \"acc_norm\": 0.8084291187739464,\n\
\ \"acc_norm_stderr\": 0.014072859310451949\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6994219653179191,\n \"acc_stderr\": 0.024685316867257796,\n\
\ \"acc_norm\": 0.6994219653179191,\n \"acc_norm_stderr\": 0.024685316867257796\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4335195530726257,\n\
\ \"acc_stderr\": 0.01657402721951763,\n \"acc_norm\": 0.4335195530726257,\n\
\ \"acc_norm_stderr\": 0.01657402721951763\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7483660130718954,\n \"acc_stderr\": 0.0248480182638752,\n\
\ \"acc_norm\": 0.7483660130718954,\n \"acc_norm_stderr\": 0.0248480182638752\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7138263665594855,\n\
\ \"acc_stderr\": 0.025670259242188933,\n \"acc_norm\": 0.7138263665594855,\n\
\ \"acc_norm_stderr\": 0.025670259242188933\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7407407407407407,\n \"acc_stderr\": 0.024383665531035454,\n\
\ \"acc_norm\": 0.7407407407407407,\n \"acc_norm_stderr\": 0.024383665531035454\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.44680851063829785,\n \"acc_stderr\": 0.029658235097666907,\n \
\ \"acc_norm\": 0.44680851063829785,\n \"acc_norm_stderr\": 0.029658235097666907\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4941329856584094,\n\
\ \"acc_stderr\": 0.012769356925216526,\n \"acc_norm\": 0.4941329856584094,\n\
\ \"acc_norm_stderr\": 0.012769356925216526\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7573529411764706,\n \"acc_stderr\": 0.02604066247420126,\n\
\ \"acc_norm\": 0.7573529411764706,\n \"acc_norm_stderr\": 0.02604066247420126\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.01887568293806945,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.01887568293806945\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.7,\n\
\ \"acc_stderr\": 0.04389311454644287,\n \"acc_norm\": 0.7,\n \
\ \"acc_norm_stderr\": 0.04389311454644287\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.763265306122449,\n \"acc_stderr\": 0.027212835884073146,\n\
\ \"acc_norm\": 0.763265306122449,\n \"acc_norm_stderr\": 0.027212835884073146\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.026508590656233268,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.026508590656233268\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.89,\n \"acc_stderr\": 0.03144660377352202,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352202\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5662650602409639,\n\
\ \"acc_stderr\": 0.03858158940685515,\n \"acc_norm\": 0.5662650602409639,\n\
\ \"acc_norm_stderr\": 0.03858158940685515\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.030611116557432528,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.030611116557432528\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3561811505507956,\n\
\ \"mc1_stderr\": 0.016763790728446335,\n \"mc2\": 0.5512836940802899,\n\
\ \"mc2_stderr\": 0.01538829164182792\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8018942383583267,\n \"acc_stderr\": 0.011201862744487052\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\"\
: 0.0\n }\n}\n```"
repo_url: https://huggingface.co/jilp00/SOLAR-10.7B-tutored
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_05T00_34_47.267405
path:
- '**/details_harness|arc:challenge|25_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|gsm8k|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hellaswag|10_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T00-34-47.267405.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-05T00-34-47.267405.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- '**/details_harness|winogrande|5_2024-01-05T00-34-47.267405.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-05T00-34-47.267405.parquet'
- config_name: results
data_files:
- split: 2024_01_05T00_34_47.267405
path:
- results_2024-01-05T00-34-47.267405.parquet
- split: latest
path:
- results_2024-01-05T00-34-47.267405.parquet
---
# Dataset Card for Evaluation run of jilp00/SOLAR-10.7B-tutored
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [jilp00/SOLAR-10.7B-tutored](https://huggingface.co/jilp00/SOLAR-10.7B-tutored) 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_jilp00__SOLAR-10.7B-tutored",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-05T00:34:47.267405](https://huggingface.co/datasets/open-llm-leaderboard/details_jilp00__SOLAR-10.7B-tutored/blob/main/results_2024-01-05T00-34-47.267405.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": 0.6412260054447222,
"acc_stderr": 0.03159304918019915,
"acc_norm": 0.6533509528093954,
"acc_norm_stderr": 0.03245293404969856,
"mc1": 0.3561811505507956,
"mc1_stderr": 0.016763790728446335,
"mc2": 0.5512836940802899,
"mc2_stderr": 0.01538829164182792
},
"harness|arc:challenge|25": {
"acc": 0.5853242320819113,
"acc_stderr": 0.014397070564409174,
"acc_norm": 0.6228668941979523,
"acc_norm_stderr": 0.014163366896192593
},
"harness|hellaswag|10": {
"acc": 0.6251742680740888,
"acc_stderr": 0.004830885704380079,
"acc_norm": 0.8224457279426409,
"acc_norm_stderr": 0.00381356105715034
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.562962962962963,
"acc_stderr": 0.04284958639753401,
"acc_norm": 0.562962962962963,
"acc_norm_stderr": 0.04284958639753401
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.7,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.7,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6981132075471698,
"acc_stderr": 0.028254200344438665,
"acc_norm": 0.6981132075471698,
"acc_norm_stderr": 0.028254200344438665
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7361111111111112,
"acc_stderr": 0.03685651095897532,
"acc_norm": 0.7361111111111112,
"acc_norm_stderr": 0.03685651095897532
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.52,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.35,
"acc_stderr": 0.0479372485441102,
"acc_norm": 0.35,
"acc_norm_stderr": 0.0479372485441102
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6705202312138728,
"acc_stderr": 0.03583901754736412,
"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4019607843137255,
"acc_stderr": 0.04878608714466996,
"acc_norm": 0.4019607843137255,
"acc_norm_stderr": 0.04878608714466996
},
"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": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.39473684210526316,
"acc_stderr": 0.045981880578165414,
"acc_norm": 0.39473684210526316,
"acc_norm_stderr": 0.045981880578165414
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5241379310344828,
"acc_stderr": 0.0416180850350153,
"acc_norm": 0.5241379310344828,
"acc_norm_stderr": 0.0416180850350153
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4894179894179894,
"acc_stderr": 0.025745542276045478,
"acc_norm": 0.4894179894179894,
"acc_norm_stderr": 0.025745542276045478
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.4126984126984127,
"acc_stderr": 0.04403438954768176,
"acc_norm": 0.4126984126984127,
"acc_norm_stderr": 0.04403438954768176
},
"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": 0.7870967741935484,
"acc_stderr": 0.023287665127268542,
"acc_norm": 0.7870967741935484,
"acc_norm_stderr": 0.023287665127268542
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4729064039408867,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.4729064039408867,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421296,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421296
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.031922715695483,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.031922715695483
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8636363636363636,
"acc_stderr": 0.024450155973189835,
"acc_norm": 0.8636363636363636,
"acc_norm_stderr": 0.024450155973189835
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328972,
"acc_norm": 0.9067357512953368,
"acc_norm_stderr": 0.02098685459328972
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6871794871794872,
"acc_stderr": 0.023507579020645344,
"acc_norm": 0.6871794871794872,
"acc_norm_stderr": 0.023507579020645344
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.34074074074074073,
"acc_stderr": 0.028897748741131143,
"acc_norm": 0.34074074074074073,
"acc_norm_stderr": 0.028897748741131143
},
"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": 0.3509933774834437,
"acc_stderr": 0.03896981964257374,
"acc_norm": 0.3509933774834437,
"acc_norm_stderr": 0.03896981964257374
},
"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": 0.5787037037037037,
"acc_stderr": 0.03367462138896078,
"acc_norm": 0.5787037037037037,
"acc_norm_stderr": 0.03367462138896078
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8382352941176471,
"acc_stderr": 0.025845017986926917,
"acc_norm": 0.8382352941176471,
"acc_norm_stderr": 0.025845017986926917
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8523206751054853,
"acc_stderr": 0.0230943295825957,
"acc_norm": 0.8523206751054853,
"acc_norm_stderr": 0.0230943295825957
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.726457399103139,
"acc_stderr": 0.029918586707798827,
"acc_norm": 0.726457399103139,
"acc_norm_stderr": 0.029918586707798827
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.732824427480916,
"acc_stderr": 0.03880848301082396,
"acc_norm": 0.732824427480916,
"acc_norm_stderr": 0.03880848301082396
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8181818181818182,
"acc_stderr": 0.035208939510976534,
"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.035208939510976534
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7962962962962963,
"acc_stderr": 0.03893542518824847,
"acc_norm": 0.7962962962962963,
"acc_norm_stderr": 0.03893542518824847
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7607361963190185,
"acc_stderr": 0.033519538795212696,
"acc_norm": 0.7607361963190185,
"acc_norm_stderr": 0.033519538795212696
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.39285714285714285,
"acc_stderr": 0.04635550135609976,
"acc_norm": 0.39285714285714285,
"acc_norm_stderr": 0.04635550135609976
},
"harness|hendrycksTest-management|5": {
"acc": 0.8058252427184466,
"acc_stderr": 0.03916667762822584,
"acc_norm": 0.8058252427184466,
"acc_norm_stderr": 0.03916667762822584
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8675213675213675,
"acc_stderr": 0.022209309073165616,
"acc_norm": 0.8675213675213675,
"acc_norm_stderr": 0.022209309073165616
},
"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": 0.8084291187739464,
"acc_stderr": 0.014072859310451949,
"acc_norm": 0.8084291187739464,
"acc_norm_stderr": 0.014072859310451949
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6994219653179191,
"acc_stderr": 0.024685316867257796,
"acc_norm": 0.6994219653179191,
"acc_norm_stderr": 0.024685316867257796
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4335195530726257,
"acc_stderr": 0.01657402721951763,
"acc_norm": 0.4335195530726257,
"acc_norm_stderr": 0.01657402721951763
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7483660130718954,
"acc_stderr": 0.0248480182638752,
"acc_norm": 0.7483660130718954,
"acc_norm_stderr": 0.0248480182638752
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7138263665594855,
"acc_stderr": 0.025670259242188933,
"acc_norm": 0.7138263665594855,
"acc_norm_stderr": 0.025670259242188933
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7407407407407407,
"acc_stderr": 0.024383665531035454,
"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.024383665531035454
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.44680851063829785,
"acc_stderr": 0.029658235097666907,
"acc_norm": 0.44680851063829785,
"acc_norm_stderr": 0.029658235097666907
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4941329856584094,
"acc_stderr": 0.012769356925216526,
"acc_norm": 0.4941329856584094,
"acc_norm_stderr": 0.012769356925216526
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7573529411764706,
"acc_stderr": 0.02604066247420126,
"acc_norm": 0.7573529411764706,
"acc_norm_stderr": 0.02604066247420126
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.01887568293806945,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.01887568293806945
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.7,
"acc_stderr": 0.04389311454644287,
"acc_norm": 0.7,
"acc_norm_stderr": 0.04389311454644287
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.763265306122449,
"acc_stderr": 0.027212835884073146,
"acc_norm": 0.763265306122449,
"acc_norm_stderr": 0.027212835884073146
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
"acc_stderr": 0.026508590656233268,
"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.026508590656233268
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.89,
"acc_stderr": 0.03144660377352202,
"acc_norm": 0.89,
"acc_norm_stderr": 0.03144660377352202
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5662650602409639,
"acc_stderr": 0.03858158940685515,
"acc_norm": 0.5662650602409639,
"acc_norm_stderr": 0.03858158940685515
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.030611116557432528,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.030611116557432528
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3561811505507956,
"mc1_stderr": 0.016763790728446335,
"mc2": 0.5512836940802899,
"mc2_stderr": 0.01538829164182792
},
"harness|winogrande|5": {
"acc": 0.8018942383583267,
"acc_stderr": 0.011201862744487052
},
"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. -->
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## 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. -->
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## 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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### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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#### Who are the annotators?
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#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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## Bias, Risks, and Limitations
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### Recommendations
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## 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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qgallouedec/prj_gia_dataset_metaworld_drawer_close_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-close-v2 environment, sample for the policy drawer-close-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_close_v2_1111
```
Then, load it with
```python
import numpy as np
dataset = np.load("prj_gia_dataset_metaworld_drawer_close_v2_1111/dataset.npy", allow_pickle=True).item()
print(dataset.keys()) # dict_keys(['observations', 'actions', 'dones', 'rewards'])
```
|
bilgedogan/streaming_options_xtts_v2 | ---
license: apache-2.0
dataset_info:
- config_name: all_techniques
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 1784124.0
num_examples: 4
download_size: 806586
dataset_size: 1784124.0
- config_name: punctuation_streaming
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 321808.0
num_examples: 7
download_size: 171688
dataset_size: 321808.0
- config_name: sentence-by-sentence_streaming
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 278364.0
num_examples: 5
download_size: 150822
dataset_size: 278364.0
- config_name: without_streaming
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 237556.0
num_examples: 1
download_size: 139908
dataset_size: 237556.0
- config_name: word-by-word_streaming
features:
- name: audio
dtype: audio
- name: id
dtype: string
- name: text
dtype: string
- name: time
dtype: float64
splits:
- name: train
num_bytes: 975311.0
num_examples: 29
download_size: 364403
dataset_size: 975311.0
configs:
- config_name: all_techniques
data_files:
- split: train
path: all_techniques/train-*
- config_name: punctuation_streaming
data_files:
- split: train
path: punctuation_streaming/train-*
- config_name: sentence-by-sentence_streaming
data_files:
- split: train
path: sentence-by-sentence_streaming/train-*
- config_name: without_streaming
data_files:
- split: train
path: without_streaming/train-*
- config_name: word-by-word_streaming
data_files:
- split: train
path: word-by-word_streaming/train-*
---
|
trrt8/binding_sites | ---
license: mit
---
|
oliverbob/biblejson | ---
license: apache-2.0
---
|
nanalada/discord-groupchat | ---
task_categories:
- conversational
language:
- en
pretty_name: Discord Groupchat Dataset
---
A dataset consisting of around 80k+ messsages between me and my friends in a discord group chat, will be uploaded soon after scrubbing |
liuyanchen1015/MULTI_VALUE_rte_completive_finish | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 272090
num_examples: 623
- name: train
num_bytes: 235385
num_examples: 497
download_size: 329762
dataset_size: 507475
---
# Dataset Card for "MULTI_VALUE_rte_completive_finish"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
showchen/chenglingsu | ---
license: apache-2.0
---
|
open-llm-leaderboard/details_fierysurf__Ambari-7B-Instruct-v0.1-sharded | ---
pretty_name: Evaluation run of fierysurf/Ambari-7B-Instruct-v0.1-sharded
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fierysurf/Ambari-7B-Instruct-v0.1-sharded](https://huggingface.co/fierysurf/Ambari-7B-Instruct-v0.1-sharded)\
\ 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_fierysurf__Ambari-7B-Instruct-v0.1-sharded\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-18T14:37:24.844010](https://huggingface.co/datasets/open-llm-leaderboard/details_fierysurf__Ambari-7B-Instruct-v0.1-sharded/blob/main/results_2024-01-18T14-37-24.844010.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\": 0.3838329966166915,\n\
\ \"acc_stderr\": 0.03388122362619041,\n \"acc_norm\": 0.388551088671872,\n\
\ \"acc_norm_stderr\": 0.034737127539897106,\n \"mc1\": 0.2607099143206854,\n\
\ \"mc1_stderr\": 0.015368841620766372,\n \"mc2\": 0.4039249872200656,\n\
\ \"mc2_stderr\": 0.014252904541596906\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.46331058020477817,\n \"acc_stderr\": 0.01457200052775699,\n\
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.014611390804670088\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5575582553276239,\n\
\ \"acc_stderr\": 0.004956609327218403,\n \"acc_norm\": 0.7458673571001793,\n\
\ \"acc_norm_stderr\": 0.004344827546976545\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.37037037037037035,\n\
\ \"acc_stderr\": 0.041716541613545426,\n \"acc_norm\": 0.37037037037037035,\n\
\ \"acc_norm_stderr\": 0.041716541613545426\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.3092105263157895,\n \"acc_stderr\": 0.037610708698674805,\n\
\ \"acc_norm\": 0.3092105263157895,\n \"acc_norm_stderr\": 0.037610708698674805\n\
\ },\n \"harness|hendrycksTest-business_ethics|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-clinical_knowledge|5\"\
: {\n \"acc\": 0.4188679245283019,\n \"acc_stderr\": 0.030365050829115205,\n\
\ \"acc_norm\": 0.4188679245283019,\n \"acc_norm_stderr\": 0.030365050829115205\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.3888888888888889,\n\
\ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.3888888888888889,\n\
\ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.29,\n\
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.3179190751445087,\n\
\ \"acc_stderr\": 0.0355068398916558,\n \"acc_norm\": 0.3179190751445087,\n\
\ \"acc_norm_stderr\": 0.0355068398916558\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.23529411764705882,\n \"acc_stderr\": 0.04220773659171452,\n\
\ \"acc_norm\": 0.23529411764705882,\n \"acc_norm_stderr\": 0.04220773659171452\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.36,\n \"acc_stderr\": 0.04824181513244218,\n \"acc_norm\": 0.36,\n\
\ \"acc_norm_stderr\": 0.04824181513244218\n },\n \"harness|hendrycksTest-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\": 0.24561403508771928,\n\
\ \"acc_stderr\": 0.04049339297748142,\n \"acc_norm\": 0.24561403508771928,\n\
\ \"acc_norm_stderr\": 0.04049339297748142\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.27586206896551724,\n \"acc_stderr\": 0.037245636197746325,\n\
\ \"acc_norm\": 0.27586206896551724,\n \"acc_norm_stderr\": 0.037245636197746325\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.2857142857142857,\n \"acc_stderr\": 0.02326651221373057,\n \"\
acc_norm\": 0.2857142857142857,\n \"acc_norm_stderr\": 0.02326651221373057\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.23809523809523808,\n\
\ \"acc_stderr\": 0.038095238095238106,\n \"acc_norm\": 0.23809523809523808,\n\
\ \"acc_norm_stderr\": 0.038095238095238106\n },\n \"harness|hendrycksTest-global_facts|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-high_school_biology|5\": {\n \"acc\"\
: 0.3870967741935484,\n \"acc_stderr\": 0.02770935967503249,\n \"\
acc_norm\": 0.3870967741935484,\n \"acc_norm_stderr\": 0.02770935967503249\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.2660098522167488,\n \"acc_stderr\": 0.03108982600293752,\n \"\
acc_norm\": 0.2660098522167488,\n \"acc_norm_stderr\": 0.03108982600293752\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \"acc_norm\"\
: 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.503030303030303,\n \"acc_stderr\": 0.03904272341431856,\n\
\ \"acc_norm\": 0.503030303030303,\n \"acc_norm_stderr\": 0.03904272341431856\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.51010101010101,\n \"acc_stderr\": 0.035616254886737454,\n \"acc_norm\"\
: 0.51010101010101,\n \"acc_norm_stderr\": 0.035616254886737454\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.5233160621761658,\n \"acc_stderr\": 0.036045136724422014,\n\
\ \"acc_norm\": 0.5233160621761658,\n \"acc_norm_stderr\": 0.036045136724422014\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.37435897435897436,\n \"acc_stderr\": 0.024537591572830513,\n\
\ \"acc_norm\": 0.37435897435897436,\n \"acc_norm_stderr\": 0.024537591572830513\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26666666666666666,\n \"acc_stderr\": 0.026962424325073828,\n \
\ \"acc_norm\": 0.26666666666666666,\n \"acc_norm_stderr\": 0.026962424325073828\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.3865546218487395,\n \"acc_stderr\": 0.031631458075523804,\n\
\ \"acc_norm\": 0.3865546218487395,\n \"acc_norm_stderr\": 0.031631458075523804\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.24503311258278146,\n \"acc_stderr\": 0.03511807571804724,\n \"\
acc_norm\": 0.24503311258278146,\n \"acc_norm_stderr\": 0.03511807571804724\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.47889908256880737,\n \"acc_stderr\": 0.021418224754264643,\n \"\
acc_norm\": 0.47889908256880737,\n \"acc_norm_stderr\": 0.021418224754264643\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.25925925925925924,\n \"acc_stderr\": 0.029886910547626964,\n \"\
acc_norm\": 0.25925925925925924,\n \"acc_norm_stderr\": 0.029886910547626964\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.4362745098039216,\n \"acc_stderr\": 0.03480693138457039,\n \"\
acc_norm\": 0.4362745098039216,\n \"acc_norm_stderr\": 0.03480693138457039\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.45569620253164556,\n \"acc_stderr\": 0.03241920684693333,\n \
\ \"acc_norm\": 0.45569620253164556,\n \"acc_norm_stderr\": 0.03241920684693333\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.5022421524663677,\n\
\ \"acc_stderr\": 0.033557465352232634,\n \"acc_norm\": 0.5022421524663677,\n\
\ \"acc_norm_stderr\": 0.033557465352232634\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.3969465648854962,\n \"acc_stderr\": 0.04291135671009225,\n\
\ \"acc_norm\": 0.3969465648854962,\n \"acc_norm_stderr\": 0.04291135671009225\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.5537190082644629,\n \"acc_stderr\": 0.04537935177947879,\n \"\
acc_norm\": 0.5537190082644629,\n \"acc_norm_stderr\": 0.04537935177947879\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.49074074074074076,\n\
\ \"acc_stderr\": 0.04832853553437055,\n \"acc_norm\": 0.49074074074074076,\n\
\ \"acc_norm_stderr\": 0.04832853553437055\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3803680981595092,\n \"acc_stderr\": 0.03814269893261836,\n\
\ \"acc_norm\": 0.3803680981595092,\n \"acc_norm_stderr\": 0.03814269893261836\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.39285714285714285,\n\
\ \"acc_stderr\": 0.04635550135609976,\n \"acc_norm\": 0.39285714285714285,\n\
\ \"acc_norm_stderr\": 0.04635550135609976\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.3786407766990291,\n \"acc_stderr\": 0.04802694698258975,\n\
\ \"acc_norm\": 0.3786407766990291,\n \"acc_norm_stderr\": 0.04802694698258975\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.5555555555555556,\n\
\ \"acc_stderr\": 0.03255326307272487,\n \"acc_norm\": 0.5555555555555556,\n\
\ \"acc_norm_stderr\": 0.03255326307272487\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.5057471264367817,\n\
\ \"acc_stderr\": 0.01787878232612923,\n \"acc_norm\": 0.5057471264367817,\n\
\ \"acc_norm_stderr\": 0.01787878232612923\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.3988439306358382,\n \"acc_stderr\": 0.026362437574546545,\n\
\ \"acc_norm\": 0.3988439306358382,\n \"acc_norm_stderr\": 0.026362437574546545\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24022346368715083,\n\
\ \"acc_stderr\": 0.014288343803925293,\n \"acc_norm\": 0.24022346368715083,\n\
\ \"acc_norm_stderr\": 0.014288343803925293\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.369281045751634,\n \"acc_stderr\": 0.027634176689602656,\n\
\ \"acc_norm\": 0.369281045751634,\n \"acc_norm_stderr\": 0.027634176689602656\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.44694533762057875,\n\
\ \"acc_stderr\": 0.028237769422085335,\n \"acc_norm\": 0.44694533762057875,\n\
\ \"acc_norm_stderr\": 0.028237769422085335\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.38271604938271603,\n \"acc_stderr\": 0.027044538138402616,\n\
\ \"acc_norm\": 0.38271604938271603,\n \"acc_norm_stderr\": 0.027044538138402616\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.3120567375886525,\n \"acc_stderr\": 0.027640120545169927,\n \
\ \"acc_norm\": 0.3120567375886525,\n \"acc_norm_stderr\": 0.027640120545169927\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.3057366362451108,\n\
\ \"acc_stderr\": 0.011766973847072915,\n \"acc_norm\": 0.3057366362451108,\n\
\ \"acc_norm_stderr\": 0.011766973847072915\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.3382352941176471,\n \"acc_stderr\": 0.028739328513983576,\n\
\ \"acc_norm\": 0.3382352941176471,\n \"acc_norm_stderr\": 0.028739328513983576\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.380718954248366,\n \"acc_stderr\": 0.019643801557924803,\n \
\ \"acc_norm\": 0.380718954248366,\n \"acc_norm_stderr\": 0.019643801557924803\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5272727272727272,\n\
\ \"acc_stderr\": 0.04782001791380061,\n \"acc_norm\": 0.5272727272727272,\n\
\ \"acc_norm_stderr\": 0.04782001791380061\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.30612244897959184,\n \"acc_stderr\": 0.02950489645459596,\n\
\ \"acc_norm\": 0.30612244897959184,\n \"acc_norm_stderr\": 0.02950489645459596\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.46766169154228854,\n\
\ \"acc_stderr\": 0.035281314729336065,\n \"acc_norm\": 0.46766169154228854,\n\
\ \"acc_norm_stderr\": 0.035281314729336065\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.54,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.54,\n \"acc_norm_stderr\": 0.05009082659620333\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.37349397590361444,\n\
\ \"acc_stderr\": 0.037658451171688624,\n \"acc_norm\": 0.37349397590361444,\n\
\ \"acc_norm_stderr\": 0.037658451171688624\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.5497076023391813,\n \"acc_stderr\": 0.038158273659132366,\n\
\ \"acc_norm\": 0.5497076023391813,\n \"acc_norm_stderr\": 0.038158273659132366\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2607099143206854,\n\
\ \"mc1_stderr\": 0.015368841620766372,\n \"mc2\": 0.4039249872200656,\n\
\ \"mc2_stderr\": 0.014252904541596906\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6953433307024467,\n \"acc_stderr\": 0.012935646499325312\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.018953752843062926,\n \
\ \"acc_stderr\": 0.0037560783410314712\n }\n}\n```"
repo_url: https://huggingface.co/fierysurf/Ambari-7B-Instruct-v0.1-sharded
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_18T14_37_24.844010
path:
- '**/details_harness|arc:challenge|25_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|gsm8k|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hellaswag|10_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T14-37-24.844010.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-18T14-37-24.844010.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- '**/details_harness|winogrande|5_2024-01-18T14-37-24.844010.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-18T14-37-24.844010.parquet'
- config_name: results
data_files:
- split: 2024_01_18T14_37_24.844010
path:
- results_2024-01-18T14-37-24.844010.parquet
- split: latest
path:
- results_2024-01-18T14-37-24.844010.parquet
---
# Dataset Card for Evaluation run of fierysurf/Ambari-7B-Instruct-v0.1-sharded
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [fierysurf/Ambari-7B-Instruct-v0.1-sharded](https://huggingface.co/fierysurf/Ambari-7B-Instruct-v0.1-sharded) 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_fierysurf__Ambari-7B-Instruct-v0.1-sharded",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-18T14:37:24.844010](https://huggingface.co/datasets/open-llm-leaderboard/details_fierysurf__Ambari-7B-Instruct-v0.1-sharded/blob/main/results_2024-01-18T14-37-24.844010.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": 0.3838329966166915,
"acc_stderr": 0.03388122362619041,
"acc_norm": 0.388551088671872,
"acc_norm_stderr": 0.034737127539897106,
"mc1": 0.2607099143206854,
"mc1_stderr": 0.015368841620766372,
"mc2": 0.4039249872200656,
"mc2_stderr": 0.014252904541596906
},
"harness|arc:challenge|25": {
"acc": 0.46331058020477817,
"acc_stderr": 0.01457200052775699,
"acc_norm": 0.5,
"acc_norm_stderr": 0.014611390804670088
},
"harness|hellaswag|10": {
"acc": 0.5575582553276239,
"acc_stderr": 0.004956609327218403,
"acc_norm": 0.7458673571001793,
"acc_norm_stderr": 0.004344827546976545
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.37037037037037035,
"acc_stderr": 0.041716541613545426,
"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.041716541613545426
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.3092105263157895,
"acc_stderr": 0.037610708698674805,
"acc_norm": 0.3092105263157895,
"acc_norm_stderr": 0.037610708698674805
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.43,
"acc_stderr": 0.049756985195624284,
"acc_norm": 0.43,
"acc_norm_stderr": 0.049756985195624284
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.4188679245283019,
"acc_stderr": 0.030365050829115205,
"acc_norm": 0.4188679245283019,
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"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-machine_learning|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-medical_genetics|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-nutrition|5": {
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"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-professional_accounting|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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"harness|hendrycksTest-professional_psychology|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"acc_norm": 0.5497076023391813,
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"harness|truthfulqa:mc|0": {
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"mc2": 0.4039249872200656,
"mc2_stderr": 0.014252904541596906
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"harness|winogrande|5": {
"acc": 0.6953433307024467,
"acc_stderr": 0.012935646499325312
},
"harness|gsm8k|5": {
"acc": 0.018953752843062926,
"acc_stderr": 0.0037560783410314712
}
}
```
## 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]
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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 Authors [optional]
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## Dataset Card Contact
[More Information Needed] |
carlosejimenez/seq2seq-squad-tokenized | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 89432112
num_examples: 87599
- name: validation
num_bytes: 10940273
num_examples: 10570
download_size: 32194916
dataset_size: 100372385
---
# Dataset Card for "seq2seq-squad-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SUSTech/sci-llm-raw | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
splits:
- name: train
num_bytes: 16076587
num_examples: 35895
- name: test
num_bytes: 182405
num_examples: 200
download_size: 9778395
dataset_size: 16258992
---
# Dataset Card for "sci-llm-raw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_eren23__ogno-monarch-jaskier-merge-7b-v2 | ---
pretty_name: Evaluation run of eren23/ogno-monarch-jaskier-merge-7b-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [eren23/ogno-monarch-jaskier-merge-7b-v2](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_eren23__ogno-monarch-jaskier-merge-7b-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-20T20:08:58.214242](https://huggingface.co/datasets/open-llm-leaderboard/details_eren23__ogno-monarch-jaskier-merge-7b-v2/blob/main/results_2024-02-20T20-08-58.214242.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\": 0.6537177122752788,\n\
\ \"acc_stderr\": 0.03199260522508157,\n \"acc_norm\": 0.6531606900461773,\n\
\ \"acc_norm_stderr\": 0.032659989869059436,\n \"mc1\": 0.6266829865361077,\n\
\ \"mc1_stderr\": 0.016932370557570638,\n \"mc2\": 0.7775830648728518,\n\
\ \"mc2_stderr\": 0.013710816401625935\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7090443686006825,\n \"acc_stderr\": 0.01327307786590759,\n\
\ \"acc_norm\": 0.7286689419795221,\n \"acc_norm_stderr\": 0.012993807727545797\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7154949213304123,\n\
\ \"acc_stderr\": 0.004502563079349392,\n \"acc_norm\": 0.8914558852818164,\n\
\ \"acc_norm_stderr\": 0.0031043064349724637\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6518518518518519,\n\
\ \"acc_stderr\": 0.041153246103369526,\n \"acc_norm\": 0.6518518518518519,\n\
\ \"acc_norm_stderr\": 0.041153246103369526\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6973684210526315,\n \"acc_stderr\": 0.03738520676119669,\n\
\ \"acc_norm\": 0.6973684210526315,\n \"acc_norm_stderr\": 0.03738520676119669\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.63,\n\
\ \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n \
\ \"acc_norm_stderr\": 0.048523658709391\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\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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-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\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.03656343653353158,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.03656343653353158\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107224,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107224\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\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\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\": 0.5655172413793104,\n \"acc_stderr\": 0.04130740879555498,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.04130740879555498\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.41534391534391535,\n \"acc_stderr\": 0.025379524910778394,\n \"\
acc_norm\": 0.41534391534391535,\n \"acc_norm_stderr\": 0.025379524910778394\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\": 0.34,\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\": 0.7838709677419354,\n\
\ \"acc_stderr\": 0.02341529343356853,\n \"acc_norm\": 0.7838709677419354,\n\
\ \"acc_norm_stderr\": 0.02341529343356853\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5320197044334976,\n \"acc_stderr\": 0.035107665979592154,\n\
\ \"acc_norm\": 0.5320197044334976,\n \"acc_norm_stderr\": 0.035107665979592154\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.71,\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\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483016,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483016\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8080808080808081,\n \"acc_stderr\": 0.028057791672989017,\n \"\
acc_norm\": 0.8080808080808081,\n \"acc_norm_stderr\": 0.028057791672989017\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8963730569948186,\n \"acc_stderr\": 0.021995311963644237,\n\
\ \"acc_norm\": 0.8963730569948186,\n \"acc_norm_stderr\": 0.021995311963644237\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6615384615384615,\n \"acc_stderr\": 0.023991500500313036,\n\
\ \"acc_norm\": 0.6615384615384615,\n \"acc_norm_stderr\": 0.023991500500313036\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.32592592592592595,\n \"acc_stderr\": 0.02857834836547308,\n \
\ \"acc_norm\": 0.32592592592592595,\n \"acc_norm_stderr\": 0.02857834836547308\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6764705882352942,\n \"acc_stderr\": 0.03038835355188679,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.03038835355188679\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4105960264900662,\n \"acc_stderr\": 0.04016689594849927,\n \"\
acc_norm\": 0.4105960264900662,\n \"acc_norm_stderr\": 0.04016689594849927\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8385321100917431,\n \"acc_stderr\": 0.01577623925616323,\n \"\
acc_norm\": 0.8385321100917431,\n \"acc_norm_stderr\": 0.01577623925616323\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5092592592592593,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.5092592592592593,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8431372549019608,\n \"acc_stderr\": 0.025524722324553353,\n \"\
acc_norm\": 0.8431372549019608,\n \"acc_norm_stderr\": 0.025524722324553353\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7974683544303798,\n \"acc_stderr\": 0.026160568246601446,\n \
\ \"acc_norm\": 0.7974683544303798,\n \"acc_norm_stderr\": 0.026160568246601446\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6905829596412556,\n\
\ \"acc_stderr\": 0.03102441174057221,\n \"acc_norm\": 0.6905829596412556,\n\
\ \"acc_norm_stderr\": 0.03102441174057221\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.8091603053435115,\n \"acc_stderr\": 0.03446513350752598,\n\
\ \"acc_norm\": 0.8091603053435115,\n \"acc_norm_stderr\": 0.03446513350752598\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7603305785123967,\n \"acc_stderr\": 0.03896878985070416,\n \"\
acc_norm\": 0.7603305785123967,\n \"acc_norm_stderr\": 0.03896878985070416\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\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04697113923010212,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04697113923010212\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8974358974358975,\n\
\ \"acc_stderr\": 0.01987565502786744,\n \"acc_norm\": 0.8974358974358975,\n\
\ \"acc_norm_stderr\": 0.01987565502786744\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\": 0.8237547892720306,\n\
\ \"acc_stderr\": 0.013625556907993455,\n \"acc_norm\": 0.8237547892720306,\n\
\ \"acc_norm_stderr\": 0.013625556907993455\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7225433526011561,\n \"acc_stderr\": 0.024105712607754307,\n\
\ \"acc_norm\": 0.7225433526011561,\n \"acc_norm_stderr\": 0.024105712607754307\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.45139664804469276,\n\
\ \"acc_stderr\": 0.016643307372315872,\n \"acc_norm\": 0.45139664804469276,\n\
\ \"acc_norm_stderr\": 0.016643307372315872\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7189542483660131,\n \"acc_stderr\": 0.025738854797818737,\n\
\ \"acc_norm\": 0.7189542483660131,\n \"acc_norm_stderr\": 0.025738854797818737\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.707395498392283,\n\
\ \"acc_stderr\": 0.02583989833487798,\n \"acc_norm\": 0.707395498392283,\n\
\ \"acc_norm_stderr\": 0.02583989833487798\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7222222222222222,\n \"acc_stderr\": 0.02492200116888633,\n\
\ \"acc_norm\": 0.7222222222222222,\n \"acc_norm_stderr\": 0.02492200116888633\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5106382978723404,\n \"acc_stderr\": 0.02982074719142244,\n \
\ \"acc_norm\": 0.5106382978723404,\n \"acc_norm_stderr\": 0.02982074719142244\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.47392438070404175,\n\
\ \"acc_stderr\": 0.01275285834653313,\n \"acc_norm\": 0.47392438070404175,\n\
\ \"acc_norm_stderr\": 0.01275285834653313\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6764705882352942,\n \"acc_stderr\": 0.018926082916083383,\n \
\ \"acc_norm\": 0.6764705882352942,\n \"acc_norm_stderr\": 0.018926082916083383\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.044612721759105085,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.044612721759105085\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.746938775510204,\n \"acc_stderr\": 0.02783302387139967,\n\
\ \"acc_norm\": 0.746938775510204,\n \"acc_norm_stderr\": 0.02783302387139967\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8407960199004975,\n\
\ \"acc_stderr\": 0.025870646766169146,\n \"acc_norm\": 0.8407960199004975,\n\
\ \"acc_norm_stderr\": 0.025870646766169146\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5542168674698795,\n\
\ \"acc_stderr\": 0.03869543323472101,\n \"acc_norm\": 0.5542168674698795,\n\
\ \"acc_norm_stderr\": 0.03869543323472101\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.847953216374269,\n \"acc_stderr\": 0.027539122889061452,\n\
\ \"acc_norm\": 0.847953216374269,\n \"acc_norm_stderr\": 0.027539122889061452\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6266829865361077,\n\
\ \"mc1_stderr\": 0.016932370557570638,\n \"mc2\": 0.7775830648728518,\n\
\ \"mc2_stderr\": 0.013710816401625935\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8429360694554064,\n \"acc_stderr\": 0.010226303949598484\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6929492039423806,\n \
\ \"acc_stderr\": 0.012705685723131707\n }\n}\n```"
repo_url: https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|arc:challenge|25_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|gsm8k|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hellaswag|10_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-20T20-08-58.214242.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-20T20-08-58.214242.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- '**/details_harness|winogrande|5_2024-02-20T20-08-58.214242.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-20T20-08-58.214242.parquet'
- config_name: results
data_files:
- split: 2024_02_20T20_08_58.214242
path:
- results_2024-02-20T20-08-58.214242.parquet
- split: latest
path:
- results_2024-02-20T20-08-58.214242.parquet
---
# Dataset Card for Evaluation run of eren23/ogno-monarch-jaskier-merge-7b-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [eren23/ogno-monarch-jaskier-merge-7b-v2](https://huggingface.co/eren23/ogno-monarch-jaskier-merge-7b-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_eren23__ogno-monarch-jaskier-merge-7b-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-20T20:08:58.214242](https://huggingface.co/datasets/open-llm-leaderboard/details_eren23__ogno-monarch-jaskier-merge-7b-v2/blob/main/results_2024-02-20T20-08-58.214242.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": 0.6537177122752788,
"acc_stderr": 0.03199260522508157,
"acc_norm": 0.6531606900461773,
"acc_norm_stderr": 0.032659989869059436,
"mc1": 0.6266829865361077,
"mc1_stderr": 0.016932370557570638,
"mc2": 0.7775830648728518,
"mc2_stderr": 0.013710816401625935
},
"harness|arc:challenge|25": {
"acc": 0.7090443686006825,
"acc_stderr": 0.01327307786590759,
"acc_norm": 0.7286689419795221,
"acc_norm_stderr": 0.012993807727545797
},
"harness|hellaswag|10": {
"acc": 0.7154949213304123,
"acc_stderr": 0.004502563079349392,
"acc_norm": 0.8914558852818164,
"acc_norm_stderr": 0.0031043064349724637
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6518518518518519,
"acc_stderr": 0.041153246103369526,
"acc_norm": 0.6518518518518519,
"acc_norm_stderr": 0.041153246103369526
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6973684210526315,
"acc_stderr": 0.03738520676119669,
"acc_norm": 0.6973684210526315,
"acc_norm_stderr": 0.03738520676119669
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"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": 0.7708333333333334,
"acc_stderr": 0.03514697467862388,
"acc_norm": 0.7708333333333334,
"acc_norm_stderr": 0.03514697467862388
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.56,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.56,
"acc_norm_stderr": 0.04988876515698589
},
"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": 0.6416184971098265,
"acc_stderr": 0.03656343653353158,
"acc_norm": 0.6416184971098265,
"acc_norm_stderr": 0.03656343653353158
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
"acc_stderr": 0.04835503696107224,
"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107224
},
"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": 0.5404255319148936,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.5404255319148936,
"acc_norm_stderr": 0.03257901482099835
},
"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": 0.5655172413793104,
"acc_stderr": 0.04130740879555498,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.04130740879555498
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.41534391534391535,
"acc_stderr": 0.025379524910778394,
"acc_norm": 0.41534391534391535,
"acc_norm_stderr": 0.025379524910778394
},
"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": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7838709677419354,
"acc_stderr": 0.02341529343356853,
"acc_norm": 0.7838709677419354,
"acc_norm_stderr": 0.02341529343356853
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5320197044334976,
"acc_stderr": 0.035107665979592154,
"acc_norm": 0.5320197044334976,
"acc_norm_stderr": 0.035107665979592154
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.031922715695483016,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.031922715695483016
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8080808080808081,
"acc_stderr": 0.028057791672989017,
"acc_norm": 0.8080808080808081,
"acc_norm_stderr": 0.028057791672989017
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8963730569948186,
"acc_stderr": 0.021995311963644237,
"acc_norm": 0.8963730569948186,
"acc_norm_stderr": 0.021995311963644237
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6615384615384615,
"acc_stderr": 0.023991500500313036,
"acc_norm": 0.6615384615384615,
"acc_norm_stderr": 0.023991500500313036
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.32592592592592595,
"acc_stderr": 0.02857834836547308,
"acc_norm": 0.32592592592592595,
"acc_norm_stderr": 0.02857834836547308
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.03038835355188679,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.03038835355188679
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4105960264900662,
"acc_stderr": 0.04016689594849927,
"acc_norm": 0.4105960264900662,
"acc_norm_stderr": 0.04016689594849927
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8385321100917431,
"acc_stderr": 0.01577623925616323,
"acc_norm": 0.8385321100917431,
"acc_norm_stderr": 0.01577623925616323
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5092592592592593,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.5092592592592593,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.025524722324553353,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.025524722324553353
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7974683544303798,
"acc_stderr": 0.026160568246601446,
"acc_norm": 0.7974683544303798,
"acc_norm_stderr": 0.026160568246601446
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6905829596412556,
"acc_stderr": 0.03102441174057221,
"acc_norm": 0.6905829596412556,
"acc_norm_stderr": 0.03102441174057221
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.8091603053435115,
"acc_stderr": 0.03446513350752598,
"acc_norm": 0.8091603053435115,
"acc_norm_stderr": 0.03446513350752598
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7603305785123967,
"acc_stderr": 0.03896878985070416,
"acc_norm": 0.7603305785123967,
"acc_norm_stderr": 0.03896878985070416
},
"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": 0.7423312883435583,
"acc_stderr": 0.03436150827846917,
"acc_norm": 0.7423312883435583,
"acc_norm_stderr": 0.03436150827846917
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04697113923010212,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04697113923010212
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8974358974358975,
"acc_stderr": 0.01987565502786744,
"acc_norm": 0.8974358974358975,
"acc_norm_stderr": 0.01987565502786744
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8237547892720306,
"acc_stderr": 0.013625556907993455,
"acc_norm": 0.8237547892720306,
"acc_norm_stderr": 0.013625556907993455
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7225433526011561,
"acc_stderr": 0.024105712607754307,
"acc_norm": 0.7225433526011561,
"acc_norm_stderr": 0.024105712607754307
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.45139664804469276,
"acc_stderr": 0.016643307372315872,
"acc_norm": 0.45139664804469276,
"acc_norm_stderr": 0.016643307372315872
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.7189542483660131,
"acc_stderr": 0.025738854797818737,
"acc_norm": 0.7189542483660131,
"acc_norm_stderr": 0.025738854797818737
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.707395498392283,
"acc_stderr": 0.02583989833487798,
"acc_norm": 0.707395498392283,
"acc_norm_stderr": 0.02583989833487798
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7222222222222222,
"acc_stderr": 0.02492200116888633,
"acc_norm": 0.7222222222222222,
"acc_norm_stderr": 0.02492200116888633
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5106382978723404,
"acc_stderr": 0.02982074719142244,
"acc_norm": 0.5106382978723404,
"acc_norm_stderr": 0.02982074719142244
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.47392438070404175,
"acc_stderr": 0.01275285834653313,
"acc_norm": 0.47392438070404175,
"acc_norm_stderr": 0.01275285834653313
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.6838235294117647,
"acc_stderr": 0.028245687391462923,
"acc_norm": 0.6838235294117647,
"acc_norm_stderr": 0.028245687391462923
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6764705882352942,
"acc_stderr": 0.018926082916083383,
"acc_norm": 0.6764705882352942,
"acc_norm_stderr": 0.018926082916083383
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
"acc_stderr": 0.044612721759105085,
"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.044612721759105085
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.746938775510204,
"acc_stderr": 0.02783302387139967,
"acc_norm": 0.746938775510204,
"acc_norm_stderr": 0.02783302387139967
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8407960199004975,
"acc_stderr": 0.025870646766169146,
"acc_norm": 0.8407960199004975,
"acc_norm_stderr": 0.025870646766169146
},
"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": 0.5542168674698795,
"acc_stderr": 0.03869543323472101,
"acc_norm": 0.5542168674698795,
"acc_norm_stderr": 0.03869543323472101
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.847953216374269,
"acc_stderr": 0.027539122889061452,
"acc_norm": 0.847953216374269,
"acc_norm_stderr": 0.027539122889061452
},
"harness|truthfulqa:mc|0": {
"mc1": 0.6266829865361077,
"mc1_stderr": 0.016932370557570638,
"mc2": 0.7775830648728518,
"mc2_stderr": 0.013710816401625935
},
"harness|winogrande|5": {
"acc": 0.8429360694554064,
"acc_stderr": 0.010226303949598484
},
"harness|gsm8k|5": {
"acc": 0.6929492039423806,
"acc_stderr": 0.012705685723131707
}
}
```
## 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]
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## 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. -->
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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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DynamicSuperbPrivate/PronounciationEvaluationAccuracy_Speechocean762 | ---
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: instruction
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 314107519.0
num_examples: 2000
- name: validation
num_bytes: 65550012.0
num_examples: 500
download_size: 315823950
dataset_size: 379657531.0
---
# Dataset Card for "PronounciationEvaluationAccuracy_Speechocean762"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Falah/stories_1_prompts | ---
dataset_info:
features:
- name: prompts
dtype: string
splits:
- name: train
num_bytes: 3381
num_examples: 11
download_size: 5022
dataset_size: 3381
---
# Dataset Card for "stories_1_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_Hertz__Mistral-Hermes-2x7b | ---
pretty_name: Evaluation run of Hertz/Mistral-Hermes-2x7b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Hertz/Mistral-Hermes-2x7b](https://huggingface.co/Hertz/Mistral-Hermes-2x7b)\
\ 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_Hertz__Mistral-Hermes-2x7b\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T15:41:30.902351](https://huggingface.co/datasets/open-llm-leaderboard/details_Hertz__Mistral-Hermes-2x7b/blob/main/results_2024-03-21T15-41-30.902351.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\": 0.6391125056656544,\n\
\ \"acc_stderr\": 0.03226178583018925,\n \"acc_norm\": 0.6410480239937978,\n\
\ \"acc_norm_stderr\": 0.032908743908124205,\n \"mc1\": 0.3427172582619339,\n\
\ \"mc1_stderr\": 0.016614949385347036,\n \"mc2\": 0.5120222208831835,\n\
\ \"mc2_stderr\": 0.015130731234851647\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6220136518771331,\n \"acc_stderr\": 0.0141696645203031,\n\
\ \"acc_norm\": 0.6518771331058021,\n \"acc_norm_stderr\": 0.013921008595179349\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6605257916749652,\n\
\ \"acc_stderr\": 0.004725630911520328,\n \"acc_norm\": 0.8527185819557856,\n\
\ \"acc_norm_stderr\": 0.0035366196730199965\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.042039210401562783,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.042039210401562783\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6513157894736842,\n \"acc_stderr\": 0.03878139888797611,\n\
\ \"acc_norm\": 0.6513157894736842,\n \"acc_norm_stderr\": 0.03878139888797611\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.720754716981132,\n \"acc_stderr\": 0.027611163402399715,\n\
\ \"acc_norm\": 0.720754716981132,\n \"acc_norm_stderr\": 0.027611163402399715\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\": 0.45,\n \"acc_stderr\": 0.05,\n \"acc_norm\"\
: 0.45,\n \"acc_norm_stderr\": 0.05\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.050251890762960605\n \
\ },\n \"harness|hendrycksTest-college_mathematics|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_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\": 0.39215686274509803,\n\
\ \"acc_stderr\": 0.04858083574266345,\n \"acc_norm\": 0.39215686274509803,\n\
\ \"acc_norm_stderr\": 0.04858083574266345\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\": 0.5787234042553191,\n\
\ \"acc_stderr\": 0.03227834510146268,\n \"acc_norm\": 0.5787234042553191,\n\
\ \"acc_norm_stderr\": 0.03227834510146268\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.47368421052631576,\n \"acc_stderr\": 0.046970851366478626,\n\
\ \"acc_norm\": 0.47368421052631576,\n \"acc_norm_stderr\": 0.046970851366478626\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.5103448275862069,\n \"acc_stderr\": 0.04165774775728763,\n \"\
acc_norm\": 0.5103448275862069,\n \"acc_norm_stderr\": 0.04165774775728763\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4126984126984127,\n \"acc_stderr\": 0.025355741263055266,\n \"\
acc_norm\": 0.4126984126984127,\n \"acc_norm_stderr\": 0.025355741263055266\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4603174603174603,\n\
\ \"acc_stderr\": 0.04458029125470973,\n \"acc_norm\": 0.4603174603174603,\n\
\ \"acc_norm_stderr\": 0.04458029125470973\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939099,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939099\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7677419354838709,\n\
\ \"acc_stderr\": 0.024022256130308235,\n \"acc_norm\": 0.7677419354838709,\n\
\ \"acc_norm_stderr\": 0.024022256130308235\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4876847290640394,\n \"acc_stderr\": 0.035169204442208966,\n\
\ \"acc_norm\": 0.4876847290640394,\n \"acc_norm_stderr\": 0.035169204442208966\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252607,\n \"acc_norm\"\
: 0.67,\n \"acc_norm_stderr\": 0.04725815626252607\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7757575757575758,\n \"acc_stderr\": 0.032568666616811015,\n\
\ \"acc_norm\": 0.7757575757575758,\n \"acc_norm_stderr\": 0.032568666616811015\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7676767676767676,\n \"acc_stderr\": 0.030088629490217487,\n \"\
acc_norm\": 0.7676767676767676,\n \"acc_norm_stderr\": 0.030088629490217487\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8652849740932642,\n \"acc_stderr\": 0.024639789097709443,\n\
\ \"acc_norm\": 0.8652849740932642,\n \"acc_norm_stderr\": 0.024639789097709443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.676923076923077,\n \"acc_stderr\": 0.02371088850197057,\n \
\ \"acc_norm\": 0.676923076923077,\n \"acc_norm_stderr\": 0.02371088850197057\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34814814814814815,\n \"acc_stderr\": 0.029045600290616258,\n \
\ \"acc_norm\": 0.34814814814814815,\n \"acc_norm_stderr\": 0.029045600290616258\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.680672268907563,\n \"acc_stderr\": 0.030283995525884396,\n \
\ \"acc_norm\": 0.680672268907563,\n \"acc_norm_stderr\": 0.030283995525884396\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8256880733944955,\n \"acc_stderr\": 0.01626567563201034,\n \"\
acc_norm\": 0.8256880733944955,\n \"acc_norm_stderr\": 0.01626567563201034\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5416666666666666,\n \"acc_stderr\": 0.03398110890294636,\n \"\
acc_norm\": 0.5416666666666666,\n \"acc_norm_stderr\": 0.03398110890294636\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8088235294117647,\n \"acc_stderr\": 0.02759917430064076,\n \"\
acc_norm\": 0.8088235294117647,\n \"acc_norm_stderr\": 0.02759917430064076\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\": 0.6860986547085202,\n\
\ \"acc_stderr\": 0.031146796482972465,\n \"acc_norm\": 0.6860986547085202,\n\
\ \"acc_norm_stderr\": 0.031146796482972465\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7709923664122137,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.7709923664122137,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.03695980128098824,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.03695980128098824\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094633\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.032910995786157686,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.032910995786157686\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7961165048543689,\n \"acc_stderr\": 0.03989139859531771,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.03989139859531771\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8888888888888888,\n\
\ \"acc_stderr\": 0.020588491316092375,\n \"acc_norm\": 0.8888888888888888,\n\
\ \"acc_norm_stderr\": 0.020588491316092375\n },\n \"harness|hendrycksTest-medical_genetics|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-miscellaneous|5\": {\n \"acc\": 0.8148148148148148,\n\
\ \"acc_stderr\": 0.013890862162876166,\n \"acc_norm\": 0.8148148148148148,\n\
\ \"acc_norm_stderr\": 0.013890862162876166\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7427745664739884,\n \"acc_stderr\": 0.023532925431044287,\n\
\ \"acc_norm\": 0.7427745664739884,\n \"acc_norm_stderr\": 0.023532925431044287\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.29608938547486036,\n\
\ \"acc_stderr\": 0.015268677317602281,\n \"acc_norm\": 0.29608938547486036,\n\
\ \"acc_norm_stderr\": 0.015268677317602281\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.7647058823529411,\n \"acc_stderr\": 0.0242886194660461,\n\
\ \"acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.0242886194660461\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\
\ \"acc_stderr\": 0.02531176597542612,\n \"acc_norm\": 0.7266881028938906,\n\
\ \"acc_norm_stderr\": 0.02531176597542612\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7283950617283951,\n \"acc_stderr\": 0.02474862449053737,\n\
\ \"acc_norm\": 0.7283950617283951,\n \"acc_norm_stderr\": 0.02474862449053737\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4716312056737589,\n \"acc_stderr\": 0.029779450957303055,\n \
\ \"acc_norm\": 0.4716312056737589,\n \"acc_norm_stderr\": 0.029779450957303055\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4621903520208605,\n\
\ \"acc_stderr\": 0.01273367188034251,\n \"acc_norm\": 0.4621903520208605,\n\
\ \"acc_norm_stderr\": 0.01273367188034251\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6838235294117647,\n \"acc_stderr\": 0.028245687391462923,\n\
\ \"acc_norm\": 0.6838235294117647,\n \"acc_norm_stderr\": 0.028245687391462923\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6650326797385621,\n \"acc_stderr\": 0.019094228167000318,\n \
\ \"acc_norm\": 0.6650326797385621,\n \"acc_norm_stderr\": 0.019094228167000318\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6636363636363637,\n\
\ \"acc_stderr\": 0.04525393596302506,\n \"acc_norm\": 0.6636363636363637,\n\
\ \"acc_norm_stderr\": 0.04525393596302506\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7510204081632653,\n \"acc_stderr\": 0.027682979522960234,\n\
\ \"acc_norm\": 0.7510204081632653,\n \"acc_norm_stderr\": 0.027682979522960234\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8208955223880597,\n\
\ \"acc_stderr\": 0.027113286753111837,\n \"acc_norm\": 0.8208955223880597,\n\
\ \"acc_norm_stderr\": 0.027113286753111837\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\": 0.5120481927710844,\n\
\ \"acc_stderr\": 0.03891364495835817,\n \"acc_norm\": 0.5120481927710844,\n\
\ \"acc_norm_stderr\": 0.03891364495835817\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8304093567251462,\n \"acc_stderr\": 0.02878210810540171,\n\
\ \"acc_norm\": 0.8304093567251462,\n \"acc_norm_stderr\": 0.02878210810540171\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3427172582619339,\n\
\ \"mc1_stderr\": 0.016614949385347036,\n \"mc2\": 0.5120222208831835,\n\
\ \"mc2_stderr\": 0.015130731234851647\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7932123125493291,\n \"acc_stderr\": 0.011382566829235797\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.5928733889310084,\n \
\ \"acc_stderr\": 0.013532811069356533\n }\n}\n```"
repo_url: https://huggingface.co/Hertz/Mistral-Hermes-2x7b
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_21T15_41_30.902351
path:
- '**/details_harness|arc:challenge|25_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|gsm8k|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hellaswag|10_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-41-30.902351.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T15-41-30.902351.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- '**/details_harness|winogrande|5_2024-03-21T15-41-30.902351.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T15-41-30.902351.parquet'
- config_name: results
data_files:
- split: 2024_03_21T15_41_30.902351
path:
- results_2024-03-21T15-41-30.902351.parquet
- split: latest
path:
- results_2024-03-21T15-41-30.902351.parquet
---
# Dataset Card for Evaluation run of Hertz/Mistral-Hermes-2x7b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [Hertz/Mistral-Hermes-2x7b](https://huggingface.co/Hertz/Mistral-Hermes-2x7b) 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_Hertz__Mistral-Hermes-2x7b",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T15:41:30.902351](https://huggingface.co/datasets/open-llm-leaderboard/details_Hertz__Mistral-Hermes-2x7b/blob/main/results_2024-03-21T15-41-30.902351.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": 0.6391125056656544,
"acc_stderr": 0.03226178583018925,
"acc_norm": 0.6410480239937978,
"acc_norm_stderr": 0.032908743908124205,
"mc1": 0.3427172582619339,
"mc1_stderr": 0.016614949385347036,
"mc2": 0.5120222208831835,
"mc2_stderr": 0.015130731234851647
},
"harness|arc:challenge|25": {
"acc": 0.6220136518771331,
"acc_stderr": 0.0141696645203031,
"acc_norm": 0.6518771331058021,
"acc_norm_stderr": 0.013921008595179349
},
"harness|hellaswag|10": {
"acc": 0.6605257916749652,
"acc_stderr": 0.004725630911520328,
"acc_norm": 0.8527185819557856,
"acc_norm_stderr": 0.0035366196730199965
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.3,
"acc_stderr": 0.046056618647183814,
"acc_norm": 0.3,
"acc_norm_stderr": 0.046056618647183814
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.042039210401562783,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.042039210401562783
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6513157894736842,
"acc_stderr": 0.03878139888797611,
"acc_norm": 0.6513157894736842,
"acc_norm_stderr": 0.03878139888797611
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.720754716981132,
"acc_stderr": 0.027611163402399715,
"acc_norm": 0.720754716981132,
"acc_norm_stderr": 0.027611163402399715
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7638888888888888,
"acc_stderr": 0.03551446610810826,
"acc_norm": 0.7638888888888888,
"acc_norm_stderr": 0.03551446610810826
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.45,
"acc_stderr": 0.05,
"acc_norm": 0.45,
"acc_norm_stderr": 0.05
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.5,
"acc_stderr": 0.050251890762960605,
"acc_norm": 0.5,
"acc_norm_stderr": 0.050251890762960605
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6763005780346821,
"acc_stderr": 0.0356760379963917,
"acc_norm": 0.6763005780346821,
"acc_norm_stderr": 0.0356760379963917
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.39215686274509803,
"acc_stderr": 0.04858083574266345,
"acc_norm": 0.39215686274509803,
"acc_norm_stderr": 0.04858083574266345
},
"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": 0.5787234042553191,
"acc_stderr": 0.03227834510146268,
"acc_norm": 0.5787234042553191,
"acc_norm_stderr": 0.03227834510146268
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.47368421052631576,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.47368421052631576,
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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]
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## 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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#### 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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kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-latex-35000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
- name: train
num_bytes: 13336000
num_examples: 1000
download_size: 1009703
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
ibranze/araproje_hellaswag_tr_conf_bestscore | ---
dataset_info:
features:
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_b
dtype: string
- name: ctx
dtype: string
- name: endings
sequence: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
splits:
- name: validation
num_bytes: 162703.0
num_examples: 250
download_size: 87097
dataset_size: 162703.0
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "araproje_hellaswag_tr_conf_bestscore"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HydraLM/hermes_sbert_full_cleaned | ---
dataset_info:
features:
- name: conversations
list:
- name: input
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
- name: conversation_id
dtype: int64
- name: dataset_id
dtype: string
- name: cluster_text
dtype: string
- name: embedding
sequence: float64
- name: unique_id
dtype: string
splits:
- name: train
num_bytes: 5925812379.477194
num_examples: 599929
download_size: 4015733332
dataset_size: 5925812379.477194
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "hermes_sbert_full_cleaned"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
YogeshPatil2902/test_stable_diffusion | ---
license: apache-2.0
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 14640853.0
num_examples: 99
download_size: 14627409
dataset_size: 14640853.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
KyS/OCR-Saliere | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: Saliere.pdf
num_bytes: 124157300.0
num_examples: 5240
download_size: 124089276
dataset_size: 124157300.0
configs:
- config_name: default
data_files:
- split: Saliere.pdf
path: data/Saliere.pdf-*
---
|
Multimodal-Fatima/CIFAR10_train_embeddings | ---
dataset_info:
features:
- name: image
dtype: image
- name: id
dtype: int64
- name: vision_embeddings
sequence: float32
splits:
- name: openai_clip_vit_large_patch14
num_bytes: 267448310.0
num_examples: 50000
download_size: 298997114
dataset_size: 267448310.0
---
# Dataset Card for "CIFAR10_train_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/sakata_nemuno_touhou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of sakata_nemuno/坂田ネムノ (Touhou)
This is the dataset of sakata_nemuno/坂田ネムノ (Touhou), containing 257 images and their tags.
The core tags of this character are `long_hair, red_eyes, grey_hair, breasts, wavy_hair, very_long_hair, large_breasts, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 257 | 275.41 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakata_nemuno_touhou/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 257 | 173.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakata_nemuno_touhou/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 557 | 342.25 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakata_nemuno_touhou/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 257 | 251.98 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakata_nemuno_touhou/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 557 | 457.81 MiB | [Download](https://huggingface.co/datasets/CyberHarem/sakata_nemuno_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/sakata_nemuno_touhou',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 16 |  |  |  |  |  | 1girl, bare_shoulders, detached_sleeves, looking_at_viewer, multicolored_dress, nata_(tool), single_strap, solo, holding_weapon, orange_dress, yellow_dress, collarbone, simple_background, barefoot, closed_mouth, full_body, white_background, blue_sleeves, cleaver, medium_breasts, smile, standing |
| 1 | 5 |  |  |  |  |  | 1girl, barefoot, detached_sleeves, full_body, holding, looking_at_viewer, multicolored_dress, nata_(tool), single_strap, solo, bare_shoulders, open_mouth, weapon, blue_sleeves, smile, standing, yellow_dress |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | detached_sleeves | looking_at_viewer | multicolored_dress | nata_(tool) | single_strap | solo | holding_weapon | orange_dress | yellow_dress | collarbone | simple_background | barefoot | closed_mouth | full_body | white_background | blue_sleeves | cleaver | medium_breasts | smile | standing | holding | open_mouth | weapon |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:-------------------|:--------------------|:---------------------|:--------------|:---------------|:-------|:-----------------|:---------------|:---------------|:-------------|:--------------------|:-----------|:---------------|:------------|:-------------------|:---------------|:----------|:-----------------|:--------|:-----------|:----------|:-------------|:---------|
| 0 | 16 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | |
| 1 | 5 |  |  |  |  |  | X | X | X | X | X | X | X | X | | | X | | | X | | X | | X | | | X | X | X | X | X |
|
sander-wood/irishman | ---
license: mit
task_categories:
- text-generation
pretty_name: IrishMAN
size_categories:
- 100K<n<1M
tags:
- music
---
If you prefer MIDI or MusicXML, download [IrishMAN-MIDI](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-midi.zip) or [IrishMAN-XML](https://huggingface.co/datasets/sander-wood/irishman/resolve/main/irishman-xml.zip). For better use of structural info in control codes, consider ABC notation.
## Dataset Summary
The **Irish Massive ABC Notation (IrishMAN)** dataset includes 216,284 Irish tunes in ABC notation, divided into 99\% (214,122 tunes) for training and 1\% (2,162 tunes) for validation. These tunes were collected from thesession.org and abcnotation.com, both renowned for sharing traditional music. To ensure uniformity in formatting, all tunes were converted to XML and then back to ABC using [scripts](https://wim.vree.org/svgParse/), and fields containing natural language (e.g., titles and lyrics) were removed.
Each tune is automatically annotated with control codes derived from ABC symbols, as described in the below section. These control codes offer insights into the musical forms and structures of each composition.
In the IrishMAN dataset, a [music21](https://web.mit.edu/music21/doc/index.html#)-filtered [subset](https://huggingface.co/datasets/sander-wood/irishman/raw/main/leadsheet_ids.json) includes 34,211 lead sheets, each human-annotated with chord symbols. It is from this very subset that [TunesFormer](https://huggingface.co/sander-wood/tunesformer) developed its capacity to generate melodies with harmonies.
A noteworthy aspect is the copyright status. All tunes in the dataset are in the public domain, ensuring ethical and legal usage for research and creative projects.
## ABC Notation
ABC notation is an ASCII-based plain text musical notation system that is commonly used for transcribing traditional music and sharing sheet music online. It provides a simple and concise way to represent musical elements such as notes, rhythms, chords, and more.
For those looking to interact with ABC notation in various ways, there are several tools available:
1. **[Online ABC Player](https://abc.rectanglered.com/):** This web-based tool allows you to input ABC notation and hear the corresponding audio playback. By pasting your ABC code into the player, you can instantly listen to the tune as it would sound when played.
2. **[ABC Sheet Music Editor - EasyABC](https://easyabc.sourceforge.net/):** EasyABC is a user-friendly software application designed for creating, editing, and formatting ABC notation. Its graphical interface enables you to input your ABC code, preview the sheet music, and make adjustments as necessary.
## Control Codes
Inspired by [CTRL](https://huggingface.co/ctrl), we incorporate control codes into TunesFormer to represent musical forms. These codes, positioned ahead of the ABC notation, enable users to specify the structures of the generated tunes. The following control codes are introduced:
- **S:number of sections**: determines the number of sections in the entire melody. It counts on several symbols that can be used to represent section boundaries: `[|`, `||`, `|]`, `|:`, `::`, and `:|`. In our dataset, the range is 1 to 8 (e.g., `S:1` for a single-section melody, and `S:8` for a melody with eight sections).
- **B:number of bars**: specifies the desired number of bars within a section. It counts on the bar symbol `|`. In our dataset, the range is 1 to 32 (e.g., `B:1` for a one-bar section, and `B:32` for a section with 32 bars).
- **E:edit distance similarity**: controls the similarity level between the current section $c$ and a previous section $p$ in the melody. It is based on the Levenshtein distance $lev(c,p)$ , quantifying the difference between sections for creating variations or contrasts. Mathematically, it can be expressed as:
```
eds(c,p) = 1 - lev(c,p) / max(|c|,|p|)
```
where $|c|$ and $|p|$ are the string lengths of the two sections. It is discretized into 11 levels, ranging from no match at all to an exact match (e.g., `E:0` for no similarity, and `E:10` for an exact match).
## Copyright Disclaimer
This dataset is for research use only and not for commercial purposes. We believe all data in this dataset is in the public domain. If you own the copyright to any musical composition in the IrishMAN dataset and have concerns, please contact us at shangda@mail.ccom.edu.cn. We will address your concerns and take appropriate action if needed.
## Special Thanks
We would like to extend a special thanks to thesession.org and abcnotation.com for their contributions to the development and promotion of ABC notation, as well as their significant impact on the field of music information retrieval. Their platforms have become invaluable resources for the traditional and folk music community. We also wish to express our gratitude to Willem (Wim) for providing the essential conversion tools that enabled the transformation of the tunes into a uniform format. Together, these collaborations have made it possible for researchers like us to create and study extensive datasets like IrishMAN.
### BibTeX entry and citation info
```bibtex
@inproceedings{DBLP:conf/hcmir/WuLY023,
author = {Shangda Wu and
Xiaobing Li and
Feng Yu and
Maosong Sun},
editor = {Lorenzo Porcaro and
Roser Batlle{-}Roca and
Emilia G{\'{o}}mez},
title = {TunesFormer: Forming Irish Tunes with Control Codes by Bar Patching},
booktitle = {Proceedings of the 2nd Workshop on Human-Centric Music Information
Retrieval 2023 co-located with the 24th International Society for
Music Information Retrieval Conference {(ISMIR} 2023), Milan, Italy,
November 10, 2023},
series = {{CEUR} Workshop Proceedings},
volume = {3528},
publisher = {CEUR-WS.org},
year = {2023},
url = {https://ceur-ws.org/Vol-3528/paper1.pdf},
timestamp = {Tue, 19 Dec 2023 17:15:12 +0100},
biburl = {https://dblp.org/rec/conf/hcmir/WuLY023.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
xlangai/windows_x86 | ---
license: apache-2.0
---
|
manishiitg/data-check-v2 | ---
dataset_info:
features:
- name: system
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
- name: lang
dtype: string
- name: processed
dtype: bool
splits:
- name: train
num_bytes: 343571684
num_examples: 115889
download_size: 150655177
dataset_size: 343571684
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
enakilci/sv_corpora_parliament_processed | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 292351437
num_examples: 1892723
download_size: 0
dataset_size: 292351437
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sv_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
autoevaluate/autoeval-eval-bazzhangz__sumdataset-bazzhangz__sumdataset-18687b-2355774138 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- bazzhangz/sumdataset
eval_info:
task: summarization
model: knkarthick/MEETING_SUMMARY
metrics: []
dataset_name: bazzhangz/sumdataset
dataset_config: bazzhangz--sumdataset
dataset_split: train
col_mapping:
text: dialogue
target: summary
---
# 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: knkarthick/MEETING_SUMMARY
* Dataset: bazzhangz/sumdataset
* Config: bazzhangz--sumdataset
* Split: train
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@bazzhangz](https://huggingface.co/bazzhangz) for evaluating this model. |
Angry-Wizard/rpg_grid_maps | ---
license: mit
---
|
aneeshas/imsdb-action-movie-scripts | ---
dataset_info:
features:
- name: Action
dtype: string
splits:
- name: train
num_bytes: 36309492
num_examples: 150
download_size: 15207860
dataset_size: 36309492
---
# Dataset Card for "imsdb-action-movie-scripts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/46a294c5 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 184
num_examples: 10
download_size: 1340
dataset_size: 184
---
# Dataset Card for "46a294c5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/wiki_find_passage_train400_eval40_rare | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 627940
num_examples: 840
- name: validation
num_bytes: 33454
num_examples: 40
download_size: 243645
dataset_size: 661394
---
# Dataset Card for "wiki_find_passage_train400_eval40_rare"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
fanfare71/testset01 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 5132
num_examples: 30
download_size: 4057
dataset_size: 5132
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
JosephLee/science_textbook_elementary_kor_seed | ---
task_categories:
- question-answering
language:
- ko
pretty_name: test dataset
--- |
jjz5463/topics_common_crawl_2.0 | ---
size_categories:
- n<1K
dataset_info:
features:
- name: Common crawl text
dtype: string
- name: Topics
dtype: string
splits:
- name: train
num_bytes: 307604
num_examples: 800
download_size: 181738
dataset_size: 307604
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
library_name: datadreamer
tags:
- datadreamer
- datadreamer-0.25.0
- synthetic
- gpt-4
---
# Dataset Card
[Add more information here](https://huggingface.co/datasets/templates/dataset-card-example)
---
This dataset was produced with [DataDreamer 🤖💤](https://datadreamer.dev). The synthetic dataset card can be found [here](datadreamer.json). |
theGhoul21/t-pas-train-light-3 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 30045476
num_examples: 90132
download_size: 14780349
dataset_size: 30045476
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
distilled-from-one-sec-cv12/chunk_62 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1139755616
num_examples: 222088
download_size: 1155406188
dataset_size: 1139755616
---
# Dataset Card for "chunk_62"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
azz1990/jsonL | ---
license: apache-2.0
---
|
hermanshid/doctor-id-qa | ---
license: apache-2.0
task_categories:
- text2text-generation
- question-answering
language:
- id
tags:
- doctor
- qa
pretty_name: Indonesian Health Question Answer
size_categories:
- 1K<n<10K
--- |
Yehor/ukrainian-tts-tetiana | ---
language:
- uk
license: apache-2.0
task_categories:
- text-to-speech
---
# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named TETIANA
Join Ukrainian community - https://t.me/speech_synthesis_uk
More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/tetiana
# Voice TETIANA (female)
License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE)
## Features
- Quality: high
- Duration: 8h
- Audio formats: OPUS/WAV
- Text format: JSONL (a `metadata.jsonl` file)
- Frequency: 16000/22050/48000 Hz
## Original version
### In the `OPUS` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_ogg.zip
## Trimmed version (removed silence)
Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence
### In the `WAV` format
- 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_48khz.zip
- 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_22khz.zip
- 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_16khz.zip |
result-kand2-sdxl-wuerst-karlo/e03089c4 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 186
num_examples: 10
download_size: 1356
dataset_size: 186
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e03089c4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
papluca/language-identification | ---
annotations_creators: []
language_creators: []
language:
- ar
- bg
- de
- el
- en
- es
- fr
- hi
- it
- ja
- nl
- pl
- pt
- ru
- sw
- th
- tr
- ur
- vi
- zh
license: []
multilinguality:
- multilingual
pretty_name: Language Identification dataset
size_categories:
- unknown
source_datasets:
- extended|amazon_reviews_multi
- extended|xnli
- extended|stsb_multi_mt
task_categories:
- text-classification
task_ids:
- multi-class-classification
---
# Dataset Card for Language Identification dataset
## 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:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Language Identification dataset is a collection of 90k samples consisting of text passages and corresponding language label.
This dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
### Supported Tasks and Leaderboards
The dataset can be used to train a model for language identification, which is a **multi-class text classification** task.
The model [papluca/xlm-roberta-base-language-detection](https://huggingface.co/papluca/xlm-roberta-base-language-detection), which is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base), was trained on this dataset and currently achieves 99.6% accuracy on the test set.
### Languages
The Language Identification dataset contains text in 20 languages, which are:
`arabic (ar), bulgarian (bg), german (de), modern greek (el), english (en), spanish (es), french (fr), hindi (hi), italian (it), japanese (ja), dutch (nl), polish (pl), portuguese (pt), russian (ru), swahili (sw), thai (th), turkish (tr), urdu (ur), vietnamese (vi), and chinese (zh)`
## Dataset Structure
### Data Instances
For each instance, there is a string for the text and a string for the label (the language tag). Here is an example:
`{'labels': 'fr', 'text': 'Conforme à la description, produit pratique.'}`
### Data Fields
- **labels:** a string indicating the language label.
- **text:** a string consisting of one or more sentences in one of the 20 languages listed above.
### Data Splits
The Language Identification dataset has 3 splits: *train*, *valid*, and *test*.
The train set contains 70k samples, while the validation and test sets 10k each.
All splits are perfectly balanced: the train set contains 3500 samples per language, while the validation and test sets 500.
## Dataset Creation
### Curation Rationale
This dataset was built during *The Hugging Face Course Community Event*, which took place in November 2021, with the goal of collecting a dataset with enough samples for each language to train a robust language detection model.
### Source Data
The Language Identification dataset was created by collecting data from 3 sources: [Multilingual Amazon Reviews Corpus](https://huggingface.co/datasets/amazon_reviews_multi), [XNLI](https://huggingface.co/datasets/xnli), and [STSb Multi MT](https://huggingface.co/datasets/stsb_multi_mt).
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or the crowdworkers.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating (balanced) multi-class text classification models.
### Discussion of Biases
The possible biases correspond to those of the 3 datasets on which this dataset is based.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@LucaPapariello](https://github.com/LucaPapariello) for adding this dataset.
|
kpriyanshu256/MultiTabQA-multitable_pretraining-Salesforce-codet5-base_train-markdown-37000 | ---
dataset_info:
features:
- name: input_ids
sequence:
sequence: int32
- name: attention_mask
sequence:
sequence: int8
- name: labels
sequence:
sequence: int64
splits:
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num_bytes: 13336000
num_examples: 1000
download_size: 1049196
dataset_size: 13336000
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
owanr/o1o2o3_xl_r2_coedit_iter_with_human_pref_practice | ---
dataset_info:
features:
- name: src
dtype: string
- name: tgt
dtype: string
splits:
- name: train
num_bytes: 70690230
num_examples: 244786
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num_examples: 10642
- name: test
num_bytes: 2920962
num_examples: 10667
download_size: 31828020
dataset_size: 76531420
---
# Dataset Card for "o1o2o3_xl_r2_coedit_iter_with_human_pref_practice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HEN10/kbis_qa_train03_ph02 | ---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: uint8
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
- name: bbox
sequence:
sequence: int64
- name: start_positions
dtype: int64
- name: end_positions
dtype: int64
splits:
- name: train
num_bytes: 61168800
num_examples: 350
download_size: 3132705
dataset_size: 61168800
---
# Dataset Card for "kbis_qa_train03_ph02"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
skrishna/SeqSense_mcq_32 | ---
dataset_info:
features:
- name: input
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 59623
num_examples: 300
download_size: 23173
dataset_size: 59623
---
# Dataset Card for "SeqSense_mcq_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
smhdigital/smh_dataset_230604 | ---
license: other
---
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xl_mode_C_T_A_OCR_rices_ns_1000 | ---
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: 1150725
num_examples: 1000
download_size: 200208
dataset_size: 1150725
---
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xl_mode_C_T_A_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
huggingartists/muse | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/muse"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.266599 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/26f575585ec649d88d09a1e402bb936b.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/muse">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Muse</div>
<a href="https://genius.com/artists/muse">
<div style="text-align: center; font-size: 14px;">@muse</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/muse).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/muse")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|333| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/muse")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
Weni/LLM_Base_2.0.0_DPO | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: question
dtype: string
- name: chosen_response
dtype: string
- name: rejected_response
dtype: string
- name: contexto
dtype: string
- name: correct_ans
dtype: int64
splits:
- name: train
num_bytes: 22516794
num_examples: 11250
download_size: 10116843
dataset_size: 22516794
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
FreedomIntelligence/XMedbench | ---
license: apache-2.0
configs:
- config_name: test
data_files:
- split: english
path: test/en.json
- split: chinese
path: test/zh.json
- split: french
path: test/fr.json
- split: spanish
path: test/es.json
- split: arabic
path: test/ar.json
- split: Hindi
path: test/hi.json
---
# Multilingual Medicine: Model, Dataset, Benchmark, Code
Covering English, Chinese, French, Hindi, Spanish, Hindi, Arabic So far
<p align="center">
👨🏻💻<a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Github</a> •📃 <a href="https://arxiv.org/abs/2403.03640" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a>
<br> <a href="./README_zh.md"> 中文 </a> | <a href="./README.md"> English
</p>
## 🌈 Update
* **[2024.03.07]** [Paper](https://arxiv.org/abs/2403.03640) released.
* **[2024.02.12]** <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus" target="_blank">ApolloCorpus</a> and <a href="https://huggingface.co/datasets/FreedomIntelligence/XMedbench" target="_blank">XMedBench</a> is published！🎉
* **[2024.01.23]** Apollo repo is published！🎉
## Results
## Usage
- [Zip File](https://huggingface.co/datasets/FreedomIntelligence/XMedbench/blob/main/XMedbench.zip)
- [Data category](https://huggingface.co/datasets/FreedomIntelligence/XMedbench/tree/main/test)
## Data:
- EN:
- [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
- [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
- [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
- [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- ZH:
- [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
- [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
- Randomly sample 2,000 multiple-choice questions with single answer.
- [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
- Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
- [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
- Randomly sample 2,000 multiple-choice questions
- ES: [Head_qa](https://huggingface.co/datasets/head_qa)
- FR: [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
- HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
- AR: [MMLU_Ara](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
- Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
## Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{wang2024apollo,
title={Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People},
author={Xidong Wang and Nuo Chen and Junyin Chen and Yan Hu and Yidong Wang and Xiangbo Wu and Anningzhe Gao and Xiang Wan and Haizhou Li and Benyou Wang},
year={2024},
eprint={2403.03640},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
humarin/chatgpt-paraphrases | ---
license: openrail
task_categories:
- text2text-generation
language:
- en
size_categories:
- 100K<n<1M
---
This is a dataset of paraphrases created by ChatGPT.
Model based on this dataset is avaible: [model](https://huggingface.co/humarin/chatgpt_paraphraser_on_T5_base)
## We used this prompt to generate paraphrases
Generate 5 similar paraphrases for this question, show it like a numbered list without commentaries: *{text}*
This dataset is based on the [Quora paraphrase question](https://www.kaggle.com/competitions/quora-question-pairs), texts from the [SQUAD 2.0](https://huggingface.co/datasets/squad_v2) and the [CNN news dataset](https://huggingface.co/datasets/cnn_dailymail).
We generated 5 paraphrases for each sample, totally this dataset has about 420k data rows. You can make 30 rows from a row from each sample. In this way you can make 12.6 millions train pairs (420k rows with 5 paraphrases -> 6x5x420000 = 12.6 millions of bidirected or 6x5x420000/2 = 6.3 millions of unique pairs).
## We used
- 247138 questions from the Quora dataset
- 91983 texts from the Squad 2.0 dataset
- 80076 texts from the CNN news dataset
## Structure of the dataset
- text column - an original sentence or question from the datasets
- paraphrases - a list of 5 paraphrases
- category - question / sentence
- source - quora / squad_2 / cnn_news
## Legal disclaimer
Data is based on OpenAI’s gpt-3.5-turbo, whose [terms of use](https://openai.com/policies/terms-of-use) prohibit developing models that compete with OpenAI. So if you use this dataset to train a model, don't compete with OpenAI.
### BibTeX entry and citation info
```bibtex
@inproceedings{chatgpt_paraphrases_dataset,
author={Vladimir Vorobev, Maxim Kuznetsov},
title={ChatGPT paraphrases dataset},
year={2023}
}
``` |
tinyBenchmarks/tinyMMLU | ---
dataset_info:
config_name: all
features:
- name: question
dtype: string
- name: subject
dtype: string
- name: choices
sequence: string
- name: answer
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
- name: input_formatted
dtype: string
splits:
- name: test
num_bytes: 337628
num_examples: 100
- name: validation
num_bytes: 5425300
num_examples: 1531
- name: dev
num_bytes: 858526
num_examples: 285
download_size: 1661908
dataset_size: 6621454
configs:
- config_name: all
data_files:
- split: test
path: all/test-*
- split: validation
path: all/validation-*
- split: dev
path: all/dev-*
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
multilinguality:
- monolingual
source_datasets:
- cais/mmlu
task_categories:
- question-answering
task_ids:
- multiple-choice-qa
pretty_name: tinyMMLU
language_bcp47:
- en-US
---
# tinyMMLU
Welcome to tinyMMLU! This dataset serves as a concise version of the [MMLU](https://huggingface.co/datasets/cais/mmlu) dataset, offering a subset of 100 data points selected from the original compilation.
tinyMMLU is designed to enable users to efficiently estimate the performance of a large language model (LLM) with reduced dataset size, saving computational resources
while maintaining the essence of the MMLU evaluation.
## Features
- **Compact Dataset:** With only 100 data points, tinyMMLU provides a swift and efficient way to evaluate your LLM's performance against a benchmark set, maintaining the essence of the original MMLU dataset.
- **Compatibility:** tinyMMLU is compatible with evaluation using the [lm evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness/), but can also be integrated into your custom pipeline. See below for more details.
## Model Evaluation
Users looking to evaluate a new model with tinyMMLU can use the [lm evaluation harness (v0.4.1 or later)](https://github.com/EleutherAI/lm-evaluation-harness/).
To do so, download this [task config folder](https://drive.google.com/uc?export=download&id=1IMeCFfcWyYVEzJ2hoMZn0cPftWcxYd82), and add the uncompressed folder
to your version of the evaluation harness at `lm-evaluation-harness/lm_eval/tasks/`. Afterwards, run your evaluation harness as usual with `--tasks=tinyMMLU` and `--log_samples` but without the `--num_fewshot` argument:
```shell
lm_eval --model hf --model_args pretrained="<your-model>" --tasks=tinyMMLU --batch_size=1 --output_path=<output_path> --log_samples
```
Alternatively, tinyMMLU can be integrated into any other pipeline by downloading the data via
```python
from datasets import load_dataset
tiny_data = load_dataset('tinyBenchmarks/tinyMMLU', 'all')['test']
```
Now, `tiny_data` contains the 100 subsampled data points with the same features as the original dataset, as well as an additional field containing the preformatted data points.
The preformatted data points follow the formatting used in the [open llm leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) including the respective in-context examples.
When using the lm evaluation harness, you can then estimate your LLM's performance using the following code. First, ensure you have the tinyBenchmarks package installed:
```shell
pip install git+https://github.com/felipemaiapolo/tinyBenchmarks
```
Then, use the code snippet below for the evaluation:
```python
import numpy as np
import tinyBenchmarks as tb
### Score vector
y = # your original score vector
### Parameters
benchmark = 'mmlu'
### Evaluation
tb.evaluate(y, benchmark)
```
This process will help you estimate the performance of your LLM against the tinyMMLU dataset, providing a streamlined approach to benchmarking.
Please be aware that evaluating on multiple GPUs can change the order of outputs in the lm evaluation harness.
Ordering your score vector following the original order in tinyMMLU will be necessary to use the tinyBenchmarks library.
For more detailed instructions on evaluating new models and computing scores, please refer to the comprehensive guides available at [lm evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness/) and [tinyBenchmarks GitHub](https://github.com/felipemaiapolo/tinyBenchmarks).
Happy benchmarking!
## More tinyBenchmarks
**Open LLM leaderboard**:
[tinyArc-Challenge](https://huggingface.co/datasets/tinyBenchmarks/tinyAI2_arc),
[tinyWinogrande](https://huggingface.co/datasets/tinyBenchmarks/tinyWinogrande),
[tinyHellaswag](https://huggingface.co/datasets/tinyBenchmarks/tinyHellaswag),
[tinyTruthfulQA](https://huggingface.co/datasets/tinyBenchmarks/tinyTruthfulQA),
[tinyGSM8k](https://huggingface.co/datasets/tinyBenchmarks/tinyGSM8k)
**AlpacaEval**:
[tinyAlpacaEval](https://huggingface.co/datasets/tinyBenchmarks/tinyAlpacaEval)
**HELM-lite**:
_work-in-progress_
## Citation
@article{polo2024tinybenchmarks,
title={tinyBenchmarks: evaluating LLMs with fewer examples},
author={Felipe Maia Polo and Lucas Weber and Leshem Choshen and Yuekai Sun and Gongjun Xu and Mikhail Yurochkin},
year={2024},
eprint={2402.14992},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{hendryckstest2021,
title={Measuring Massive Multitask Language Understanding},
author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
journal={Proceedings of the International Conference on Learning Representations (ICLR)},
year={2021}
} |
wengflow/robust-e-nerf | ---
pretty_name: Robust e-NeRF
paperswithcode_id: robust-e-nerf-synthetic-event-dataset
license: cc-by-4.0
viewer: false
size_categories:
- n<1K
---
# Robust *e*-NeRF Synthetic Event Dataset
<table style="display: block">
<tr>
<td><a href="https://wengflow.github.io/robust-e-nerf"><img src="https://img.shields.io/badge/Project_Page-black?style=for-the-badge" alt="Project Page"></a></td>
<td><a href="https://arxiv.org/abs/2309.08596"><img src="https://img.shields.io/badge/arXiv-black?style=for-the-badge" alt="arXiv"></a></td>
<td><a href="https://github.com/wengflow/robust-e-nerf"><img src="https://img.shields.io/badge/Code-black?style=for-the-badge" alt="Code"></a></td>
<td><a href="https://github.com/wengflow/rpg_esim"><img src="https://img.shields.io/badge/Simulator-black?style=for-the-badge" alt="Simulator"></a></td>
</tr>
</table>
<p align="center">
<img src="assets/dataset_easy.gif" alt="Easy" width=60%/>
<img src="assets/dataset_medium.gif" alt="Medium" width=60%/>
<img src="assets/dataset_hard.gif" alt="Hard" width=60%/>
</p>
This repository contains the synthetic event dataset used in [**Robust *e*-NeRF**](https://wengflow.github.io/robust-e-nerf) to study the collective effect of camera speed profile, contrast threshold variation and refractory period on the quality of NeRF reconstruction from a moving event camera. The dataset is simulated using an [improved version of ESIM](https://github.com/wengflow/rpg_esim) with three different camera configurations of increasing difficulty levels (*i.e.* *easy*, *medium* and *hard*) on seven Realistic Synthetic 360 scenes (adopted in the synthetic experiments of NeRF), resulting in a total of 21 sequence recordings. Please refer to the [Robust *e*-NeRF paper](https://arxiv.org/abs/2309.08596) for more details.
This synthetic event dataset allows for a retrospective comparison between event-based and image-based NeRF reconstruction methods, as the event sequences were simulated under highly similar conditions as the NeRF synthetic dataset. In particular, we adopt the same camera intrinsics and camera distance to the object at the origin. Furthermore, the event camera travels in a hemi-/spherical spiral motion about the object, thereby having a similar camera pose distribution for training. Apart from that, we also use the same test camera poses/views. Nonetheless, this new synthetic event dataset is not only specific to NeRF reconstruction, but also suitable for novel view synthesis, 3D reconstruction, localization and SLAM in general.
If you use this synthetic event dataset for your work, please cite:
```bibtex
@inproceedings{low2023_robust-e-nerf,
title = {Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion},
author = {Low, Weng Fei and Lee, Gim Hee},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2023}
}
```
## Dataset Structure and Contents
This synthetic event dataset is organized first by scene, then by level of difficulty. Each sequence recording is given in the form of a [ROS bag](http://wiki.ros.org/rosbag) named `esim.bag`, with the following data streams:
| ROS Topic | Data | Publishing Rate (Hz) |
| :--- | :--- | :--- |
| `/cam0/events` | Events | - |
| `/cam0/pose` | Camera Pose | 1000 |
| `/imu` | IMU measurements with simulated noise | 1000 |
| `/cam0/image_raw` | RGB image | 250 |
| `/cam0/depthmap` | Depth map | 10 |
| `/cam0/optic_flow` | Optical flow map | 10 |
| `/cam0/camera_info` | Camera intrinsic and lens distortion parameters | 10
It is obtained by running the improved ESIM with the associated `esim.conf` configuration file, which references camera intrinsics configuration files `pinhole_mono_nodistort_f={1111, 1250}.yaml` and camera trajectory CSV files `{hemisphere, sphere}_spiral-rev=4[...].csv`.
The validation and test views of each scene are given in the `views/` folder, which is structured according to the NeRF synthetic dataset (except for the depth and normal maps). These views are rendered from the scene Blend-files, given in the `scenes/` folder. Specifically, we create a [Conda](https://docs.conda.io/en/latest/) environment with [Blender as a Python module](https://docs.blender.org/api/current/info_advanced_blender_as_bpy.html) installed, according to [these instructions](https://github.com/wengflow/rpg_esim#blender), to run the `bpy_render_views.py` Python script for rendering the evaluation views.
## Setup
1. Install [Git LFS](https://git-lfs.com/) according to the [official instructions](https://github.com/git-lfs/git-lfs?utm_source=gitlfs_site&utm_medium=installation_link&utm_campaign=gitlfs#installing).
2. Setup Git LFS for your user account with:
```bash
git lfs install
```
3. Clone this dataset repository into the desired destination directory with:
```bash
git lfs clone https://huggingface.co/datasets/wengflow/robust-e-nerf
```
4. To minimize disk usage, remove the `.git/` folder. However, this would complicate the pulling of changes in this upstream dataset repository.
|
rvs007/lele_rvs | ---
license: other
---
|
Dauren-Nur/kaz_rus_parallel_corpora_KAZNU | ---
dataset_info:
features:
- name: kaz
dtype: string
- name: rus
dtype: string
splits:
- name: train
num_bytes: 36422766.148103595
num_examples: 69162
- name: test
num_bytes: 9105954.851896405
num_examples: 17291
download_size: 23404279
dataset_size: 45528721
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- translation
language:
- kk
- ru
pretty_name: Parallel corpora for Kazakh and Russian language
size_categories:
- 10K<n<100K
--- |
open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4 | ---
pretty_name: Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4)\
\ 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_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-09-28T15:50:00.560199](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4/blob/main/results_2023-09-28T15-50-00.560199.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\": 0.018246644295302015,\n\
\ \"em_stderr\": 0.0013706682452812888,\n \"f1\": 0.0714765100671141,\n\
\ \"f1_stderr\": 0.0018411955158404013,\n \"acc\": 0.32543219642729987,\n\
\ \"acc_stderr\": 0.007862138879264232\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.018246644295302015,\n \"em_stderr\": 0.0013706682452812888,\n\
\ \"f1\": 0.0714765100671141,\n \"f1_stderr\": 0.0018411955158404013\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.006823351023502654,\n \
\ \"acc_stderr\": 0.0022675371022545044\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6440410418310971,\n \"acc_stderr\": 0.013456740656273959\n\
\ }\n}\n```"
repo_url: https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_09_28T15_50_00.560199
path:
- '**/details_harness|drop|3_2023-09-28T15-50-00.560199.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-09-28T15-50-00.560199.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_09_28T15_50_00.560199
path:
- '**/details_harness|gsm8k|5_2023-09-28T15-50-00.560199.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-09-28T15-50-00.560199.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:47:41.742069.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-07-19T14:47:41.742069.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_09_28T15_50_00.560199
path:
- '**/details_harness|winogrande|5_2023-09-28T15-50-00.560199.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-09-28T15-50-00.560199.parquet'
- config_name: results
data_files:
- split: 2023_07_19T14_47_41.742069
path:
- results_2023-07-19T14:47:41.742069.parquet
- split: 2023_09_28T15_50_00.560199
path:
- results_2023-09-28T15-50-00.560199.parquet
- split: latest
path:
- results_2023-09-28T15-50-00.560199.parquet
---
# Dataset Card for Evaluation run of Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4
- **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 [Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4](https://huggingface.co/Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4) 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_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-28T15:50:00.560199](https://huggingface.co/datasets/open-llm-leaderboard/details_Fredithefish__RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4/blob/main/results_2023-09-28T15-50-00.560199.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": 0.018246644295302015,
"em_stderr": 0.0013706682452812888,
"f1": 0.0714765100671141,
"f1_stderr": 0.0018411955158404013,
"acc": 0.32543219642729987,
"acc_stderr": 0.007862138879264232
},
"harness|drop|3": {
"em": 0.018246644295302015,
"em_stderr": 0.0013706682452812888,
"f1": 0.0714765100671141,
"f1_stderr": 0.0018411955158404013
},
"harness|gsm8k|5": {
"acc": 0.006823351023502654,
"acc_stderr": 0.0022675371022545044
},
"harness|winogrande|5": {
"acc": 0.6440410418310971,
"acc_stderr": 0.013456740656273959
}
}
```
### 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] |
Circularmachines/batch_indexing_machine_230529_014 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 160648209.0
num_examples: 720
download_size: 160659400
dataset_size: 160648209.0
---
# Dataset Card for "batch_indexing_machine_230529_014"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/satake_minako_theidolmstermillionlive | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of satake_minako/佐竹美奈子 (THE iDOLM@STER: Million Live!)
This is the dataset of satake_minako/佐竹美奈子 (THE iDOLM@STER: Million Live!), containing 472 images and their tags.
The core tags of this character are `brown_hair, blue_eyes, ponytail, breasts, long_hair, bangs, bow, hair_bow, medium_breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 472 | 463.02 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 472 | 315.01 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1121 | 653.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 472 | 427.07 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1121 | 840.28 MiB | [Download](https://huggingface.co/datasets/CyberHarem/satake_minako_theidolmstermillionlive/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/satake_minako_theidolmstermillionlive',
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 | 8 |  |  |  |  |  | 1girl, :d, looking_at_viewer, open_mouth, solo, striped, character_name, hair_ribbon, shorts |
| 1 | 7 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, smile, open_mouth, large_breasts, cleavage, naked_apron |
| 2 | 9 |  |  |  |  |  | 1girl, blush, looking_at_viewer, smile, solo, cleavage, tasuki, collarbone, large_breasts, onsen, yukata |
| 3 | 6 |  |  |  |  |  | 1girl, blush, navel, nipples, nude, solo, female_pubic_hair, smile, collarbone, looking_at_viewer, open_mouth |
| 4 | 21 |  |  |  |  |  | 1girl, blush, hetero, solo_focus, 1boy, nipples, open_mouth, vaginal, smile, navel, penis, sweat, female_pubic_hair, looking_at_viewer, pussy, completely_nude, large_breasts, spread_legs, girl_on_top, happy_sex, cowgirl_position, mosaic_censoring, pov |
| 5 | 7 |  |  |  |  |  | 1girl, blush, open_mouth, solo, :d, collarbone, looking_at_viewer, upper_body, bare_shoulders, red_bow, sidelocks, simple_background, striped_shirt, holding, long_sleeves, upper_teeth_only, white_shirt, ladle, off-shoulder_shirt, white_background |
| 6 | 6 |  |  |  |  |  | 1girl, blush, smile, solo, sunflower, yellow_flower, blue_sky, cloud, day, flower_field, holding_flower, looking_at_viewer, outdoors, red_bow, blurry, dress, open_mouth, shirt, upper_body |
| 7 | 13 |  |  |  |  |  | 1girl, china_dress, looking_at_viewer, smile, solo, blush, cleavage_cutout, bun_cover, hair_bun, open_mouth, wrist_cuffs, baozi, blue_dress, large_breasts, white_background |
| 8 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, sleeveless_dress, solo, white_dress, bare_shoulders, blush, earrings, white_gloves, large_breasts, open_mouth, sidelocks, smile, arms_up, closed_mouth, dated, heart, upper_body |
| 9 | 10 |  |  |  |  |  | bikini, cleavage, day, smile, 1girl, cloud, looking_at_viewer, outdoors, solo, blue_sky, beach, navel, ocean, blush, collarbone, hair_ribbon, open_mouth |
| 10 | 6 |  |  |  |  |  | 1girl, china_dress, floral_print, side_slit, blush, cowboy_shot, looking_at_viewer, open_mouth, print_dress, red_dress, smile, solo, standing, short_dress, sidelocks, sleeveless_dress, folding_fan, hair_ornament, holding_fan, jewelry, medium_hair, simple_background, white_background |
| 11 | 9 |  |  |  |  |  | 1girl, hair_ribbon, looking_at_viewer, plaid_skirt, pleated_skirt, school_uniform, solo, miniskirt, smile, white_shirt, blush, striped, long_sleeves, polka_dot, red_skirt, holding, medium_hair, open_mouth, blazer, closed_mouth, collared_shirt, dress_shirt, red_necktie, simple_background, sweater_vest, white_background, wing_collar |
| 12 | 9 |  |  |  |  |  | 1girl, cleavage, detached_collar, playboy_bunny, rabbit_ears, solo, looking_at_viewer, smile, fake_animal_ears, strapless_leotard, wrist_cuffs, black_pantyhose, open_mouth, bare_shoulders, black_bowtie, black_leotard, rabbit_tail, simple_background, standing |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | :d | looking_at_viewer | open_mouth | solo | striped | character_name | hair_ribbon | shorts | blush | smile | large_breasts | cleavage | naked_apron | tasuki | collarbone | onsen | yukata | navel | nipples | nude | female_pubic_hair | hetero | solo_focus | 1boy | vaginal | penis | sweat | pussy | completely_nude | spread_legs | girl_on_top | happy_sex | cowgirl_position | mosaic_censoring | pov | upper_body | bare_shoulders | red_bow | sidelocks | simple_background | striped_shirt | holding | long_sleeves | upper_teeth_only | white_shirt | ladle | off-shoulder_shirt | white_background | sunflower | yellow_flower | blue_sky | cloud | day | flower_field | holding_flower | outdoors | blurry | dress | shirt | china_dress | cleavage_cutout | bun_cover | hair_bun | wrist_cuffs | baozi | blue_dress | sleeveless_dress | white_dress | earrings | white_gloves | arms_up | closed_mouth | dated | heart | bikini | beach | ocean | floral_print | side_slit | cowboy_shot | print_dress | red_dress | standing | short_dress | folding_fan | hair_ornament | holding_fan | jewelry | medium_hair | plaid_skirt | pleated_skirt | school_uniform | miniskirt | polka_dot | red_skirt | blazer | collared_shirt | dress_shirt | red_necktie | sweater_vest | wing_collar | detached_collar | playboy_bunny | rabbit_ears | fake_animal_ears | strapless_leotard | black_pantyhose | black_bowtie | black_leotard | rabbit_tail |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-----|:--------------------|:-------------|:-------|:----------|:-----------------|:--------------|:---------|:--------|:--------|:----------------|:-----------|:--------------|:---------|:-------------|:--------|:---------|:--------|:----------|:-------|:--------------------|:---------|:-------------|:-------|:----------|:--------|:--------|:--------|:------------------|:--------------|:--------------|:------------|:-------------------|:-------------------|:------|:-------------|:-----------------|:----------|:------------|:--------------------|:----------------|:----------|:---------------|:-------------------|:--------------|:--------|:---------------------|:-------------------|:------------|:----------------|:-----------|:--------|:------|:---------------|:-----------------|:-----------|:---------|:--------|:--------|:--------------|:------------------|:------------|:-----------|:--------------|:--------|:-------------|:-------------------|:--------------|:-----------|:---------------|:----------|:---------------|:--------|:--------|:---------|:--------|:--------|:---------------|:------------|:--------------|:--------------|:------------|:-----------|:--------------|:--------------|:----------------|:--------------|:----------|:--------------|:--------------|:----------------|:-----------------|:------------|:------------|:------------|:---------|:-----------------|:--------------|:--------------|:---------------|:--------------|:------------------|:----------------|:--------------|:-------------------|:--------------------|:------------------|:---------------|:----------------|:--------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | | X | X | X | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 9 |  |  |  |  |  | X | | X | | X | | | | | X | X | X | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 6 |  |  |  |  |  | X | | X | X | X | | | | | X | X | | | | | X | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 21 |  |  |  |  |  | X | | X | X | | | | | | X | X | X | | | | | | | X | X | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 7 |  |  |  |  |  | X | X | X | X | X | | | | | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | | X | X | X | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 13 |  |  |  |  |  | X | | X | X | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 5 |  |  |  |  |  | X | | X | X | X | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 10 |  |  |  |  |  | X | | X | X | X | | | 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 | | | | | | | X | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 11 | 9 |  |  |  |  |  | X | | X | X | X | X | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | X | X | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | |
| 12 | 9 |  |  |  |  |  | X | | X | X | X | | | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X |
|
qazisaad/llama_2_optimized_product_titles-esci-4-7 | ---
dataset_info:
features:
- name: index
dtype: int64
- name: product_title
dtype: string
- name: average_score
dtype: float64
- name: total_score
dtype: float64
- name: text
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 28182010
num_examples: 9861
download_size: 4414697
dataset_size: 28182010
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "llama_2_optimized_product_titles-esci-4-7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NYTK/HuCoPA | ---
annotations_creators:
- found
language_creators:
- found
- expert-generated
language:
- hu
license:
- bsd-2-clause
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|other
task_categories:
- other
task_ids: []
pretty_name: HuCoPA
tags:
- commonsense-reasoning
---
# Dataset Card for HuCoPA
## 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:**
- **Repository:**
[HuCoPA dataset](https://github.com/nytud/HuCoPA)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)
### Dataset Summary
This is the dataset card for the Hungarian Choice of Plausible Alternatives Corpus (HuCoPA), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit [HuLU](hulu.nlp.nytud.hu). The corpus was created by translating and re-annotating the original English CoPA corpus (Roemmele et al., 2011).
### Supported Tasks and Leaderboards
'commonsense reasoning'
'question answering'
### Languages
The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.
## Dataset Structure
### Data Instances
For each instance, there is an id, a premise, a question ('cause' or 'effect'), two alternatives and a label (1 or 2).
An example:
```
{"idx": "1",
"question": "cause",
"label": "1",
"premise": "A testem árnyékot vetett a fűre.",
"choice1": "Felkelt a nap.",
"choice2": "A füvet lenyírták."}
```
### Data Fields
- id: unique id of the instances, an integer between 1 and 1000;
- question: "cause" or "effect". It suggests what kind of causal relation are we looking for: in the case of "cause" we search for the more plausible alternative that may be a cause of the premise. In the case of "effect" we are looking for a plausible result of the premise;
- premise: the premise, a sentence;
- choice1: the first alternative, a sentence;
- choice2: the second alternative, a sentence;
- label: the number of the more plausible alternative (1 or 2).
### Data Splits
HuCoPA has 3 splits: *train*, *validation* and *test*.
| Dataset split | Number of instances in the split |
|---------------|----------------------------------|
| train | 400 |
| validation | 100 |
| test | 500 |
The test data is distributed without the labels. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment).
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
The data is a translation of the content of the CoPA corpus. Each sentence was translated by a human translator. Each translation was manually checked and further refined by another annotator.
### Annotations
#### Annotation process
The instances initially inherited their original labels from the CoPA dataset. Each instance was annotated by a human annotator. If the original label and the human annotator's label did not match, we manually curated the instance and assigned a final label to that. This step was necessary to ensure that the causal realationship had not been changed or lost during the translation process.
#### Who are the annotators?
The translators were native Hungarian speakers with English proficiency. The annotators were university students with some linguistic background.
## Additional Information
The human performance on the test set is 96% (accuracy).
### Licensing Information
HuCoPA is released under the BSD 2-Clause License.
Copyright (c) 2010, University of Southern California
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
### Citation Information
If you use this resource or any part of its documentation, please refer to:
Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis
kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. In: Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika (eds), XVIII. Magyar Számítógépes Nyelvészeti Konferencia. JATEPress, Szeged. 431–446.
```
@inproceedings{ligetinagy2022hulu,
title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year={2022},
editors = {Berend, Gábor and Gosztolya, Gábor and Vincze, Veronika},
address = {Szeged},
publisher = {JATEPress},
pages = {431–446}
}
```
and to:
Roemmele, M., Bejan, C., and Gordon, A. (2011) Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning. AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, Stanford University, March 21-23, 2011.
```
@inproceedings{roemmele2011choice,
title={Choice of plausible alternatives: An evaluation of commonsense causal reasoning},
author={Roemmele, Melissa and Bejan, Cosmin Adrian and Gordon, Andrew S},
booktitle={2011 AAAI Spring Symposium Series},
year={2011},
url={https://people.ict.usc.edu/~gordon/publications/AAAI-SPRING11A.PDF},
}
```
### Contributions
Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset.
|
tyzhu/random_letter_find_passage_train100_eval20_num | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 19600
num_examples: 220
- name: validation
num_bytes: 2240
num_examples: 20
download_size: 0
dataset_size: 21840
---
# Dataset Card for "random_letter_find_passage_train100_eval20_num"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pnr-svc/ner-bioo | ---
dataset_info:
features:
- name: text
sequence: string
- name: tags
sequence: string
splits:
- name: train
num_bytes: 3449
num_examples: 8
download_size: 3097
dataset_size: 3449
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
gremlin97/cute-controlnet | ---
license: cc-by-4.0
---
|
EleutherAI/quirky_squaring_increment0_alice | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype: bool
splits:
- name: train
num_bytes: 6382022.0
num_examples: 92000
- name: validation
num_bytes: 277592.0
num_examples: 4000
- name: test
num_bytes: 277585.0
num_examples: 4000
download_size: 2581961
dataset_size: 6937199.0
---
# Dataset Card for "quirky_squaring_increment0_alice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
CyberHarem/ilyukhina_irina_renaiflops | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Ilyukhina Irina
This is the dataset of Ilyukhina Irina, containing 177 images and their tags.
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)).
| Name | Images | Download | Description |
|:----------------|---------:|:----------------------------------------|:-----------------------------------------------------------------------------------------|
| raw | 177 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 377 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| raw-stage3-eyes | 429 | [Download](dataset-raw-stage3-eyes.zip) | 3-stage cropped (with eye-focus) raw data with meta information. |
| 384x512 | 177 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x704 | 177 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x880 | 177 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 377 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 377 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-p512-640 | 273 | [Download](dataset-stage3-p512-640.zip) | 3-stage cropped dataset with the area not less than 512x512 pixels. |
| stage3-eyes-640 | 429 | [Download](dataset-stage3-eyes-640.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 640 pixels. |
| stage3-eyes-800 | 429 | [Download](dataset-stage3-eyes-800.zip) | 3-stage cropped (with eye-focus) dataset with the shorter side not exceeding 800 pixels. |
|
shidowake/FreedomIntelligence_alpaca-gpt4-japanese_subset_split_1 | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 4863217.322740098
num_examples: 4997
download_size: 2497466
dataset_size: 4863217.322740098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
parinzee/pingu-images-dreambooth | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 11360169.0
num_examples: 14
download_size: 11358375
dataset_size: 11360169.0
---
# Dataset Card for "pingu-images-dreambooth"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SebastianSchramm/ultrafeedback_binarized_prefs_filtered | ---
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
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: score_chosen
dtype: float64
- name: score_rejected
dtype: float64
splits:
- name: train_prefs
num_bytes: 360858348.6922183
num_examples: 56286
- name: test_prefs
num_bytes: 11663780.3125
num_examples: 1825
download_size: 180008939
dataset_size: 372522129.0047183
---
# Dataset Card for "ultrafeedback_binarized_prefs_filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
results-sd-v1-5-sd-v2-1-if-v1-0-karlo/5c7f1cb7 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 182
num_examples: 10
download_size: 1343
dataset_size: 182
---
# Dataset Card for "5c7f1cb7"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
israel/Amharic-News-Text-classification-Dataset | ---
license: cc-by-4.0
---
# An Amharic News Text classification Dataset
> In NLP, text classification is one of the primary problems we try to solve and its uses in language analyses are indisputable. The lack of labeled training data made it harder to do these tasks in low resource languages like Amharic. The task of collecting, labeling, annotating, and making valuable this kind of data will encourage junior researchers, schools, and machine learning practitioners to implement existing classification models in their language. In this short paper, we aim to introduce the Amharic text classification dataset that consists of more than 50k news articles that were categorized into 6 classes. This dataset is made available with easy baseline performances to encourage studies and better performance experiments.
```
@misc{https://doi.org/10.48550/arxiv.2103.05639,
doi = {10.48550/ARXIV.2103.05639},
url = {https://arxiv.org/abs/2103.05639},
author = {Azime, Israel Abebe and Mohammed, Nebil},
keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {An Amharic News Text classification Dataset},
publisher = {arXiv},
year = {2021},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
|
CyberHarem/jingei_kantaicollection | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of jingei (Kantai Collection)
This is the dataset of jingei (Kantai Collection), containing 484 images and their tags.
The core tags of this character are `black_hair, long_hair, red_eyes, hair_ornament, breasts, large_breasts, hairclip, low_ponytail`, 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 | 484 | 508.56 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jingei_kantaicollection/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 484 | 327.68 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jingei_kantaicollection/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1136 | 699.39 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jingei_kantaicollection/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 484 | 465.83 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jingei_kantaicollection/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1136 | 928.00 MiB | [Download](https://huggingface.co/datasets/CyberHarem/jingei_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/jingei_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 | 26 |  |  |  |  |  | 1girl, hair_flaps, blue_bikini, solo, frilled_bikini, looking_at_viewer, navel, official_alternate_costume, cowboy_shot, simple_background, blush |
| 1 | 6 |  |  |  |  |  | 1girl, cleavage, cowboy_shot, hair_flaps, looking_at_viewer, solo, side-tie_bikini_bottom, simple_background, artist_name, blue_bikini, dated, navel, white_background |
| 2 | 13 |  |  |  |  |  | 1girl, hair_flaps, solo, blue_one-piece_swimsuit, cowboy_shot, looking_at_viewer, collarbone, simple_background, white_background, competition_swimsuit, dated, smile, twitter_username, school_swimsuit |
| 3 | 12 |  |  |  |  |  | hair_flaps, 1girl, solo, striped_kimono, yukata, blue_kimono, official_alternate_costume, single_braid, hair_over_shoulder, looking_at_viewer, upper_body, dated, obi |
| 4 | 6 |  |  |  |  |  | 1girl, blue_shirt, hair_flaps, white_pants, ass, looking_at_viewer, single_braid, solo, from_behind, looking_back, official_alternate_costume, simple_background, blue_sweater, blush, cowboy_shot, long_sleeves, white_background |
| 5 | 6 |  |  |  |  |  | 1girl, blue_skirt, cleavage, hair_flaps, race_queen, solo, cowboy_shot, navel, miniskirt, artist_name, bandeau, blue_jacket, cropped_jacket, midriff, simple_background, white_background, white_thighhighs |
| 6 | 6 |  |  |  |  |  | 1girl, bag, hair_flaps, solo, black_dress, necklace, official_alternate_costume, smile, long_sleeves, looking_at_viewer, sweater, white_jacket, grey_jacket, hair_between_eyes, open_mouth |
| 7 | 21 |  |  |  |  |  | 1girl, blue_sailor_collar, blue_serafuku, blue_skirt, hair_flaps, shawl, solo, waist_apron, white_apron, white_neckerchief, pleated_skirt, bandana, headgear, looking_at_viewer, long_sleeves, open_mouth, simple_background, cowboy_shot, white_background, white_thighhighs, smile |
| 8 | 21 |  |  |  |  |  | 1girl, blue_sailor_collar, blue_serafuku, hair_flaps, shawl, solo, upper_body, white_neckerchief, bandana, looking_at_viewer, headgear, simple_background, white_background, long_sleeves, smile, open_mouth |
| 9 | 14 |  |  |  |  |  | hair_flaps, red_capelet, red_headwear, santa_hat, 1girl, solo, fur-trimmed_capelet, long_sleeves, pleated_skirt, red_skirt, smile, white_shirt, garter_straps, hair_between_eyes, white_apron, blush, fur-trimmed_headwear, santa_costume, cowboy_shot, brown_thighhighs, christmas, heart, waist_apron |
| 10 | 9 |  |  |  |  |  | 1girl, detached_collar, hair_flaps, playboy_bunny, rabbit_ears, solo, strapless_leotard, fake_animal_ears, cowboy_shot, simple_background, wrist_cuffs, looking_at_viewer, white_background, dated, rabbit_tail, black_leotard, black_pantyhose, blue_leotard, cleavage, fishnet_pantyhose, red_bowtie, single_braid |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | hair_flaps | blue_bikini | solo | frilled_bikini | looking_at_viewer | navel | official_alternate_costume | cowboy_shot | simple_background | blush | cleavage | side-tie_bikini_bottom | artist_name | dated | white_background | blue_one-piece_swimsuit | collarbone | competition_swimsuit | smile | twitter_username | school_swimsuit | striped_kimono | yukata | blue_kimono | single_braid | hair_over_shoulder | upper_body | obi | blue_shirt | white_pants | ass | from_behind | looking_back | blue_sweater | long_sleeves | blue_skirt | race_queen | miniskirt | bandeau | blue_jacket | cropped_jacket | midriff | white_thighhighs | bag | black_dress | necklace | sweater | white_jacket | grey_jacket | hair_between_eyes | open_mouth | blue_sailor_collar | blue_serafuku | shawl | waist_apron | white_apron | white_neckerchief | pleated_skirt | bandana | headgear | red_capelet | red_headwear | santa_hat | fur-trimmed_capelet | red_skirt | white_shirt | garter_straps | fur-trimmed_headwear | santa_costume | brown_thighhighs | christmas | heart | detached_collar | playboy_bunny | rabbit_ears | strapless_leotard | fake_animal_ears | wrist_cuffs | rabbit_tail | black_leotard | black_pantyhose | blue_leotard | fishnet_pantyhose | red_bowtie |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:-------------|:--------------|:-------|:-----------------|:--------------------|:--------|:-----------------------------|:--------------|:--------------------|:--------|:-----------|:-------------------------|:--------------|:--------|:-------------------|:--------------------------|:-------------|:-----------------------|:--------|:-------------------|:------------------|:-----------------|:---------|:--------------|:---------------|:---------------------|:-------------|:------|:-------------|:--------------|:------|:--------------|:---------------|:---------------|:---------------|:-------------|:-------------|:------------|:----------|:--------------|:-----------------|:----------|:-------------------|:------|:--------------|:-----------|:----------|:---------------|:--------------|:--------------------|:-------------|:---------------------|:----------------|:--------|:--------------|:--------------|:--------------------|:----------------|:----------|:-----------|:--------------|:---------------|:------------|:----------------------|:------------|:--------------|:----------------|:-----------------------|:----------------|:-------------------|:------------|:--------|:------------------|:----------------|:--------------|:--------------------|:-------------------|:--------------|:--------------|:----------------|:------------------|:---------------|:--------------------|:-------------|
| 0 | 26 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 6 |  |  |  |  |  | X | X | X | X | | X | X | | X | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 13 |  |  |  |  |  | X | X | | X | | X | | | X | X | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 12 |  |  |  |  |  | X | X | | X | | X | | X | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 6 |  |  |  |  |  | X | X | | X | | X | | X | X | X | X | | | | | X | | | | | | | | | | X | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | | X | | | X | | X | X | | X | | X | | X | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 6 |  |  |  |  |  | X | X | | X | | X | | X | | | | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 21 |  |  |  |  |  | X | X | | X | | X | | | X | X | | | | | | X | | | | X | | | | | | | | | | | | | | | | X | X | | | | | | | X | | | | | | | | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 21 |  |  |  |  |  | X | X | | X | | X | | | | X | | | | | | X | | | | X | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | X | X | X | X | | | X | | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 14 |  |  |  |  |  | X | X | | X | | | | | X | | X | | | | | | | | | X | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | X | X | | X | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 10 | 9 |  |  |  |  |  | X | X | | X | | X | | | X | X | | X | | | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
imodels/compas-recidivism | ---
annotations_creators: []
language: []
language_creators: []
license: []
multilinguality: []
pretty_name: compas-recividivsm
size_categories:
- 1K<n<10K
source_datasets: []
tags:
- interpretability
- fairness
task_categories:
- tabular-classification
task_ids: []
---
Port of the compas-recidivism dataset from propublica (github [here](https://github.com/propublica/compas-analysis)). See details there and use carefully, as there are serious known social impacts and biases present in this dataset.
Basic preprocessing done by the [imodels team](https://github.com/csinva/imodels) in [this notebook](https://github.com/csinva/imodels-data/blob/master/notebooks_fetch_data/00_get_datasets_custom.ipynb).
The target is the binary outcome `is_recid`.
### Sample usage
Load the data:
```
from datasets import load_dataset
dataset = load_dataset("imodels/compas-recidivism")
df = pd.DataFrame(dataset['train'])
X = df.drop(columns=['is_recid'])
y = df['is_recid'].values
```
Fit a model:
```
import imodels
import numpy as np
m = imodels.FIGSClassifier(max_rules=5)
m.fit(X, y)
print(m)
```
Evaluate:
```
df_test = pd.DataFrame(dataset['test'])
X_test = df.drop(columns=['is_recid'])
y_test = df['is_recid'].values
print('accuracy', np.mean(m.predict(X_test) == y_test))
``` |
ihaflix1/botine | ---
license: openrail
---
|
X-Wang/tokenized_Tatoeba-Challenge | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 2967573277
num_examples: 14128610
- name: test
num_bytes: 361399
num_examples: 2497
- name: validation
num_bytes: 418992
num_examples: 2894
download_size: 1317867764
dataset_size: 2968353668
---
# Dataset Card for "tokenized_Tatoeba-Challenge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
awacke1/SNOMED-Clinical-Terminology | ---
license: mit
---
|
DTU54DL/libri-augmented-test-prepared | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 2517004616
num_examples: 2620
download_size: 712936594
dataset_size: 2517004616
---
# Dataset Card for "libri-augmented-test-prepared"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
NajiAboo/comparison-data-falcon | ---
size_categories: 1K<n<10K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for comparison-data-falcon
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.cfg`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("NajiAboo/comparison-data-falcon")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("NajiAboo/comparison-data-falcon")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/guides/llms/conceptual_guides/data_model.html) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| instruction | User instruction | TextField | True | False |
| response-1 | Response-1 | TextField | True | False |
| response-2 | Response-2 | TextField | True | False |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| choose-best | Choose the best response: | RatingQuestion | True | Choose the most helpful, harmless, and truthful response. Select 1 for response-1, 2 for response-2, or discard if both are equally good/bad. | [1, 2] |
Finally, the **guidelines** are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": null,
"fields": {
"instruction": "What is Depreciation",
"response-1": "Depreciation is the drop in value of an asset due to wear and tear, age and obsolescence (going out of date) as recorded in an organization\u0027s financial records.",
"response-2": "What is Depreciation on a Car?\nDepreciation is the value decrease that occurs in a car over time. It is the difference between the price the car was sold for and the price of the same vehicle in a few years or more. The amount of depreciation varies based on how much the car was used and the type of driving it was done."
},
"metadata": null,
"responses": []
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"choose-best": null,
"external_id": null,
"instruction": "What is Depreciation",
"metadata": null,
"response-1": "Depreciation is the drop in value of an asset due to wear and tear, age and obsolescence (going out of date) as recorded in an organization\u0027s financial records.",
"response-2": "What is Depreciation on a Car?\nDepreciation is the value decrease that occurs in a car over time. It is the difference between the price the car was sold for and the price of the same vehicle in a few years or more. The amount of depreciation varies based on how much the car was used and the type of driving it was done."
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are suppported. These are the ones that will be used to provide responses to the questions.
* **instruction** is of type `TextField`.
* **response-1** is of type `TextField`.
* **response-2** is of type `TextField`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as rating, text, single choice, or multiple choice.
* **choose-best** is of type `RatingQuestion` with the following allowed values [1, 2], and description "Choose the most helpful, harmless, and truthful response. Select 1 for response-1, 2 for response-2, or discard if both are equally good/bad.".
Additionally, we also have one more field which is optional and is the following:
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
These guidelines are based on the paper [Training Language Models to Follow Instructions with Human Feedback](https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf)
**You are given a text-based description of a task, submitted by a user.**
This task description may be in the form of an explicit instruction (e.g. "Write a story about a wise frog."). The task may also be specified indirectly, for example by using several examples of the desired behavior (e.g. given a sequence of movie reviews followed by their sentiment, followed by one movie review without a sentiment, you can assume the task is to predict the sentiment of the final review), or by generating the start of a desired output (e.g. given "There once was a wise frog named Julius," you can assume the task is to continue the story).
**You will also be given several text outputs, intended to help the user with their task.** Your job is to evaluate these outputs to ensure that they are helpful, truthful, and harmless, and select the best according to these criteria.
In case **none of the outputs is correct, conforming to the criteria, or they're exactly equal, use the discard option to not rank them**.
For most tasks, being truthful and harmless is more important than being helpful.
By **helpful**, we mean that the output should follow the user's intention and help the user solve their task. Some examples of helpful behavior:
- Writing in clear language.
- Answering the question they meant to ask, even if they mis-asked it.
- Being sensitive to internationality (e.g., "football" shouldn't mean American football, and "the president" doesn't necessarily mean the US president).
- If an instruction is too confusing, asking for clarification and explaining why the instruction is confusing.
- Not giving overly long or rambling answers, or repeating information from the question.
- Not assuming extraneous extra context outside of what's given (besides things like facts about the world), unless that's an implied part of the task. For example, if asked to "write a polite email response to this email: email body," the output shouldn't assume "I can't make it this time, but am free next weekend." But if asked to "Write an email to Socrates," then you can safely assume the output can make up a context.
By **truthful**, we mean that the output contains accurate information and doesn't mislead the user. Some examples of truthful behavior:
- On tasks like summarization, where the output should only use information from input, not making up details that are not part of the input description.
- Not producing clearly false information about the world (e.g., making up facts or promoting conspiracies). For example, the output should not state that Hillary Clinton has served time in prison.
- Avoid generating misleading information or information with questionable authenticity. For example, if the input asks "Why did Hillary Clinton go to jail?", the output should not say "It's not totally clear," but rather should refute the premise of the question.
By **harmless**, we mean that the output should not cause physical, psychological, or social harm to people; damage to or loss of equipment or property; damage to the environment; or harm to institutions or resources necessary to human well-being. Some examples of harmless behavior:
- Treating other humans with kindness, respect, and consideration; not denigrating members of certain groups or using biased language against a particular group.
- Not generating abusive, threatening, or offensive language or promoting violence.
- Not writing sexual or violent content if it's not asked for.
- Not giving bad real-world advice or promoting illegal activity.
Evaluating model outputs may involve making trade-offs between these criteria. These trade-offs will depend on the task.
#### 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_rte_null_relcl | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 365769
num_examples: 835
- name: train
num_bytes: 319031
num_examples: 708
download_size: 450804
dataset_size: 684800
---
# Dataset Card for "MULTI_VALUE_rte_null_relcl"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
WinterSiren/medical-meadow | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 10560892
num_examples: 10178
download_size: 5508015
dataset_size: 10560892
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
s-nlp/lc_quad2 | ---
license: gpl-3.0
task_categories:
- question-answering
---
# Dataset Card for LC-QuAD 2.0 with answers |
jahb57/bert_embeddings_test | ---
dataset_info:
features:
- name: sentence
dtype: string
- name: last_hidden_state
sequence:
sequence: float64
- name: pooler_output
sequence: float64
splits:
- name: train
num_bytes: 3136056
num_examples: 10
download_size: 2718173
dataset_size: 3136056
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pocasrocas/recipe-gantt | ---
license: mit
task_categories:
- text2text-generation
language:
- en
tags:
- art
- chemistry
- food
- recipes
---
## Summary
A very small dataset of input recipes and output recipe gantt charts in TSV format where each column represents a method step and each row represents a single ingredient. Cells of the output TSV are populated with X if that ingredient is used in that step.
It was used to fine-tune [pocasrocas/recipe-gantt-v0.1](https://huggingface.co/pocasrocas/recipe-gantt-v0.1).
## Format
It follows the [alpaca](https://github.com/tatsu-lab/stanford_alpaca?tab=readme-ov-file#data-release) instruction/input/response format, shared here in .jsonl format for easy use with libraries such as [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
## Development process
1. Used the [openrecipes](https://github.com/fictivekin/openrecipes) dataset to get a few hundred recipe URLs
1. Used [recipe-scrapers](https://github.com/hhursev/recipe-scrapers) library to extract the ingredients and method steps when given a recipe URL ([code](https://github.com/jbremz/recipe-gantt/blob/1c37b115b155a128e0765040197c5783b5a91ff3/notebooks/001-get-data/02-save-recipes.ipynb)).
1. A custom GPT Assistant was written to generate the desired gantt charts as TSV files (albeit slowly and expensively) from simplified Ingredients, Method formatted recipes ([code](https://github.com/jbremz/recipe-gantt/blob/1c37b115b155a128e0765040197c5783b5a91ff3/notebooks/001-get-data/03-query-gpt4.ipynb)). A publicly accessible GPT version of the same assistant is [here](https://chat.openai.com/g/g-VG5s6fStY-recipe-gantt).
1. Did a small amount of manual tweaking of the outputs to improve data quality before I lost my mind and moved on ([code](https://github.com/jbremz/recipe-gantt/blob/1c37b115b155a128e0765040197c5783b5a91ff3/notebooks/001-get-data/04-check-results.ipynb)).
Full details of dataset creation can be found [here](https://github.com/jbremz/recipe-gantt/tree/3f153a23f5aed15236631e322064d56c737b151c/notebooks/001-get-data).
## Limitations
- **Size:** I stopped at 288 examples because I didn't want to spend any more money on OpenAI credits (~£20). Otherwise, it would be very striaghtforward to scale this dataset.
- **Errors:** being generated by GPT-4 there are errors in the outputs that I found, I only manually checked ~30 examples before deeming that the accuracy was sufficient for my needs.
- You will notice that the Instructions are all identical. I made this decision as the dataset was so small - I was keen to make it as easy as possible for the model to understand the task when finetuning. It is redundant information though and if I had scaled this dataset larger I would have removed the `input` field (as is valid with alpaca) and moved it to the `instruction` field, replacing the boilerplate prompt. |
mnhnam/portal-support-open-chat-3.5-prompt | ---
license: mit
---
|
open-llm-leaderboard/details_fblgit__UNA-POLAR-10.7B-InstructMath-v2 | ---
pretty_name: Evaluation run of fblgit/UNA-POLAR-10.7B-InstructMath-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [fblgit/UNA-POLAR-10.7B-InstructMath-v2](https://huggingface.co/fblgit/UNA-POLAR-10.7B-InstructMath-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 1 run(s). Each run can be\
\ found as a specific split in each configuration, the split being named using the\
\ timestamp of the run.The \"train\" split is always pointing to the latest results.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the aggregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_fblgit__UNA-POLAR-10.7B-InstructMath-v2\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-04T13:31:30.327640](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-POLAR-10.7B-InstructMath-v2/blob/main/results_2024-01-04T13-31-30.327640.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\": 0.6640742249502881,\n\
\ \"acc_stderr\": 0.03170094021991354,\n \"acc_norm\": 0.6648977588200776,\n\
\ \"acc_norm_stderr\": 0.03234593494804173,\n \"mc1\": 0.572827417380661,\n\
\ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.717276829204571,\n\
\ \"mc2_stderr\": 0.0151125842350684\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6825938566552902,\n \"acc_stderr\": 0.013602239088038167,\n\
\ \"acc_norm\": 0.7073378839590444,\n \"acc_norm_stderr\": 0.013295916103619429\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7093208524198367,\n\
\ \"acc_stderr\": 0.004531477407589653,\n \"acc_norm\": 0.8819956184027086,\n\
\ \"acc_norm_stderr\": 0.0032195397905004737\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.39,\n \"acc_stderr\": 0.04902071300001975,\n \
\ \"acc_norm\": 0.39,\n \"acc_norm_stderr\": 0.04902071300001975\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\": 0.7368421052631579,\n \"acc_stderr\": 0.03583496176361072,\n\
\ \"acc_norm\": 0.7368421052631579,\n \"acc_norm_stderr\": 0.03583496176361072\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.77,\n\
\ \"acc_stderr\": 0.04229525846816505,\n \"acc_norm\": 0.77,\n \
\ \"acc_norm_stderr\": 0.04229525846816505\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7708333333333334,\n\
\ \"acc_stderr\": 0.03514697467862388,\n \"acc_norm\": 0.7708333333333334,\n\
\ \"acc_norm_stderr\": 0.03514697467862388\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\"\
: 0.5,\n \"acc_stderr\": 0.050251890762960605,\n \"acc_norm\": 0.5,\n\
\ \"acc_norm_stderr\": 0.050251890762960605\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.653179190751445,\n\
\ \"acc_stderr\": 0.036291466701596636,\n \"acc_norm\": 0.653179190751445,\n\
\ \"acc_norm_stderr\": 0.036291466701596636\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4215686274509804,\n \"acc_stderr\": 0.04913595201274498,\n\
\ \"acc_norm\": 0.4215686274509804,\n \"acc_norm_stderr\": 0.04913595201274498\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.74,\n \"acc_stderr\": 0.04408440022768078,\n \"acc_norm\": 0.74,\n\
\ \"acc_norm_stderr\": 0.04408440022768078\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6170212765957447,\n \"acc_stderr\": 0.03177821250236922,\n\
\ \"acc_norm\": 0.6170212765957447,\n \"acc_norm_stderr\": 0.03177821250236922\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5087719298245614,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.5087719298245614,\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\"\
: 0.4708994708994709,\n \"acc_stderr\": 0.025707658614154964,\n \"\
acc_norm\": 0.4708994708994709,\n \"acc_norm_stderr\": 0.025707658614154964\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.04793724854411019,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.04793724854411019\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8129032258064516,\n\
\ \"acc_stderr\": 0.022185710092252252,\n \"acc_norm\": 0.8129032258064516,\n\
\ \"acc_norm_stderr\": 0.022185710092252252\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\": 0.71,\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\": 0.8121212121212121,\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\"\
: 0.8636363636363636,\n \"acc_stderr\": 0.024450155973189835,\n \"\
acc_norm\": 0.8636363636363636,\n \"acc_norm_stderr\": 0.024450155973189835\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\": 0.6564102564102564,\n \"acc_stderr\": 0.024078696580635474,\n\
\ \"acc_norm\": 0.6564102564102564,\n \"acc_norm_stderr\": 0.024078696580635474\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.3592592592592593,\n \"acc_stderr\": 0.029252905927251972,\n \
\ \"acc_norm\": 0.3592592592592593,\n \"acc_norm_stderr\": 0.029252905927251972\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7184873949579832,\n \"acc_stderr\": 0.02921354941437217,\n \
\ \"acc_norm\": 0.7184873949579832,\n \"acc_norm_stderr\": 0.02921354941437217\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.39072847682119205,\n \"acc_stderr\": 0.039837983066598075,\n \"\
acc_norm\": 0.39072847682119205,\n \"acc_norm_stderr\": 0.039837983066598075\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8495412844036697,\n \"acc_stderr\": 0.015328563932669235,\n \"\
acc_norm\": 0.8495412844036697,\n \"acc_norm_stderr\": 0.015328563932669235\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.033622774366080424,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.033622774366080424\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8529411764705882,\n \"acc_stderr\": 0.02485747808025046,\n \"\
acc_norm\": 0.8529411764705882,\n \"acc_norm_stderr\": 0.02485747808025046\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8438818565400844,\n \"acc_stderr\": 0.023627159460318667,\n \
\ \"acc_norm\": 0.8438818565400844,\n \"acc_norm_stderr\": 0.023627159460318667\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.695067264573991,\n\
\ \"acc_stderr\": 0.030898610882477515,\n \"acc_norm\": 0.695067264573991,\n\
\ \"acc_norm_stderr\": 0.030898610882477515\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7251908396946565,\n \"acc_stderr\": 0.03915345408847836,\n\
\ \"acc_norm\": 0.7251908396946565,\n \"acc_norm_stderr\": 0.03915345408847836\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\": 0.8240740740740741,\n\
\ \"acc_stderr\": 0.03680918141673881,\n \"acc_norm\": 0.8240740740740741,\n\
\ \"acc_norm_stderr\": 0.03680918141673881\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7607361963190185,\n \"acc_stderr\": 0.03351953879521269,\n\
\ \"acc_norm\": 0.7607361963190185,\n \"acc_norm_stderr\": 0.03351953879521269\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4375,\n\
\ \"acc_stderr\": 0.04708567521880525,\n \"acc_norm\": 0.4375,\n \
\ \"acc_norm_stderr\": 0.04708567521880525\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.0376017800602662,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.0376017800602662\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\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8084291187739464,\n\
\ \"acc_stderr\": 0.01407285931045195,\n \"acc_norm\": 0.8084291187739464,\n\
\ \"acc_norm_stderr\": 0.01407285931045195\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7485549132947977,\n \"acc_stderr\": 0.023357365785874037,\n\
\ \"acc_norm\": 0.7485549132947977,\n \"acc_norm_stderr\": 0.023357365785874037\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3843575418994413,\n\
\ \"acc_stderr\": 0.016269088663959402,\n \"acc_norm\": 0.3843575418994413,\n\
\ \"acc_norm_stderr\": 0.016269088663959402\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.02440439492808787,\n\
\ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.02440439492808787\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\": 0.7839506172839507,\n \"acc_stderr\": 0.022899162918445806,\n\
\ \"acc_norm\": 0.7839506172839507,\n \"acc_norm_stderr\": 0.022899162918445806\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5070921985815603,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.5070921985815603,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.485006518904824,\n\
\ \"acc_stderr\": 0.012764493202193255,\n \"acc_norm\": 0.485006518904824,\n\
\ \"acc_norm_stderr\": 0.012764493202193255\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7463235294117647,\n \"acc_stderr\": 0.026431329870789527,\n\
\ \"acc_norm\": 0.7463235294117647,\n \"acc_norm_stderr\": 0.026431329870789527\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6862745098039216,\n \"acc_stderr\": 0.01877168389352817,\n \
\ \"acc_norm\": 0.6862745098039216,\n \"acc_norm_stderr\": 0.01877168389352817\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\": 0.7551020408163265,\n \"acc_stderr\": 0.027529637440174927,\n\
\ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.027529637440174927\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\": 0.89,\n \"acc_stderr\": 0.03144660377352203,\n \
\ \"acc_norm\": 0.89,\n \"acc_norm_stderr\": 0.03144660377352203\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\
\ \"acc_stderr\": 0.038444531817709175,\n \"acc_norm\": 0.5783132530120482,\n\
\ \"acc_norm_stderr\": 0.038444531817709175\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.783625730994152,\n \"acc_stderr\": 0.03158149539338733,\n\
\ \"acc_norm\": 0.783625730994152,\n \"acc_norm_stderr\": 0.03158149539338733\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.572827417380661,\n\
\ \"mc1_stderr\": 0.017316834410963926,\n \"mc2\": 0.717276829204571,\n\
\ \"mc2_stderr\": 0.0151125842350684\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.829518547750592,\n \"acc_stderr\": 0.010569021122825897\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6474601971190296,\n \
\ \"acc_stderr\": 0.013159909755930333\n }\n}\n```"
repo_url: https://huggingface.co/fblgit/UNA-POLAR-10.7B-InstructMath-v2
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|arc:challenge|25_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|gsm8k|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hellaswag|10_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-31-30.327640.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-04T13-31-30.327640.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- '**/details_harness|winogrande|5_2024-01-04T13-31-30.327640.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-04T13-31-30.327640.parquet'
- config_name: results
data_files:
- split: 2024_01_04T13_31_30.327640
path:
- results_2024-01-04T13-31-30.327640.parquet
- split: latest
path:
- results_2024-01-04T13-31-30.327640.parquet
---
# Dataset Card for Evaluation run of fblgit/UNA-POLAR-10.7B-InstructMath-v2
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [fblgit/UNA-POLAR-10.7B-InstructMath-v2](https://huggingface.co/fblgit/UNA-POLAR-10.7B-InstructMath-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.
An additional configuration "results" store all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_fblgit__UNA-POLAR-10.7B-InstructMath-v2",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-04T13:31:30.327640](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-POLAR-10.7B-InstructMath-v2/blob/main/results_2024-01-04T13-31-30.327640.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": 0.6640742249502881,
"acc_stderr": 0.03170094021991354,
"acc_norm": 0.6648977588200776,
"acc_norm_stderr": 0.03234593494804173,
"mc1": 0.572827417380661,
"mc1_stderr": 0.017316834410963926,
"mc2": 0.717276829204571,
"mc2_stderr": 0.0151125842350684
},
"harness|arc:challenge|25": {
"acc": 0.6825938566552902,
"acc_stderr": 0.013602239088038167,
"acc_norm": 0.7073378839590444,
"acc_norm_stderr": 0.013295916103619429
},
"harness|hellaswag|10": {
"acc": 0.7093208524198367,
"acc_stderr": 0.004531477407589653,
"acc_norm": 0.8819956184027086,
"acc_norm_stderr": 0.0032195397905004737
},
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}
```
## Dataset Details
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